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A comparison of land use and Coho salmon abundance in the Georgia Basin, British Columbia Morlin, Maria Gabrielle Tamara 2000

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A COMPARISON OF LAND USE AND COHO SALMON ABUNDANCE IN THE GEORGIA BASIN, BRITISH COLUMBIA by MARIA GABRIELLE TAMARA MORLIN B.Sc. The University of British Columbia, 1995 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Department of Zoology) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA March 2000 ® Maria Gabrielle Tamara Morlin, 2000 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of The University of British Columbia Vancouver, Canada Date ftrp/A 3 ^ \ DE-6 (2/88) 11 ABSTRACT Coho salmon (pnayrhynchus kisutch) declines in the Strait of Georgia, British Columbia have been attributed to overfishing, marine survival, and habitat loss; however, the relative impact of each is difficult to ascertain. In many cases of commercial fish population declines, factors other than fishing are blamed. In British Columbia considerable funding is allocated to habitat restoration projects; however, if spawners fail to colonize streams, then funding and effort may be wasted. I investigated the contribution of freshwater habitat loss in the Georgia and Lower Fraser Basins of British Columbia to a substantial decline in coho salmon abundance from mid-1970 to 1995. I used land use as an indicator of potential habitat loss and degradation for an index set of watersheds and measured land use change over four decades using aerial photos and a Geographic Information System. I compared land use to coho escapement and coho fry for sixteen watersheds and over three time periods (1955, 1975 and 1995) using linear regressions. I found no significant relationships between mean coho escapement and land use for each time period. Land use change over time was not related to the realized rate of population growth (g) for coho or coho fry. Coho fry abundance was positively related to proportion old growth in a watershed, and negatively related to proportion urban land use in the 1995 time period. II TABLE OF CONTENTS Abstract ii List of Tables iv List of Figures v Acknowledgements viu CHAPTER I Introduction .'. ... 1 CHAPTER II Materials and Methods 7 2.1 Study area 7 2.2 Land use data collection 11 2.3 Coho fry densities 15 2.4 Coho escapement 16 2.5 Physical characteristics of streams 16 2.6 Methods for statistical analyses 17 CHAPTER III Results 20 3.1 Land use , 20 3.2 Coho fry 27 3.3 Coho escapement 28 3.4 Stream attributes 29 3.5 Comparison of land use to coho escapement and coho fry 29 3.5.1 Statistical analyses 31 CHAPTER IV Discussion 62 4.1 Brief summary of results 62 4.2 Land use 62 4.3 Coho fry 63 4.4 Comparison of land use to coho escapement and coho fry 63 4.4.1 Coho resilience to change 64 4.4.2 Historical land use 67 4.4.3 Other land use types not considered in this study 69 4.4.4 Small, undetectable effect size 70 4.5 Relationships between coho and land use 71 4.6 Stream physical attributes and coho abundance 73 4.7 Evaluation of methods 74 4.7.1. Rationale for land use groups 74 4.7.2. Land use collection 76 CONCLUSION 78 REFERENCES 79 iv LIST OF TABLES Table 1. Land use classifications and area in the Georgia Basin 10 Table 2. Land use classifications and area of the Lower Fraser Basin 10 Table 3. Land use categories identified on aerial photographs and orthophotos and a description of each land use category 12 Table 4. Dates and scales of historical aerial photographs used to collect land use within different areas of the Georgia and Fraser Basins 13 Table 5. Index of imperviousness of land cover, land use categories included in each index level, and an explanation of overland water flow behaviour for each case ... 18 Table 6. Broad land use classification and land use categories included in each classification and an explanation of anthropogenic influences 18 Table 7. Physical attributes of study streams 31 Table 8. Correlation coefficients (r), significance values (p), and power (pw) for regressions of mean coho fry and mean escapement on percent broad land use categories 32 Table 9. Correlation coefficient (r), significance values (p) and power (pw) for regressions of coho fry and population growth rate on percent change of broad land use categories for post-1975 (1975 to 1995) 37 Table 10. Correlation coefficient (r), significance values (p), and power (pw) for regressions of coho fry and population growth rate on percent change of impervious land use categories for the post-1975 time period (1975 to 1995) 39 Table 11. Correlation coefficients (r), significance values (p), and power (pw) for regressions of coho fry and population growth rate on percent change of impervious land use categories for 1975 to 1995 for three buffer widths 43 Table 12. Correlation coefficients (r) and significance values (p) for regressions of coho fry and population growth rate on percent change of broad land use categories for 1955 to 1995 50 Table 13. Correlation coefficients (r) and significance values (p), and power (pw) for regressions of coho fry and population growth rate on percent change of impervious land use categories for the whole time period (1955 to 1995) 52 Table 14. Correlation coefficients (r) and significance values (p) for regressions of coho fry and population growth rate on percent change of agriculture and urban land use categories for 1955 to 1995 for three buffer widths 56 Table 15. Correlation coefficients and significance values (p) and power (pw) for regressions of coho fry density and mean escapement on physical stream characteristics 60 v LIST OF FIGURES Figure 1. The Inset map is British Columbia. The enlarged map is the east coast of Vancouver Island, the coastal mainland of British Columbia and location of the study watersheds 9 Figure 2. Percent land use for all the study watersheds combined. Land use is grouped into impervious categories described in the materials and methods section 22 Figure 3. Land use proportions in the entire Georgia Basin, the Lower Fraser Basin, and study watersheds within the two basins 23 Figure 4. Proportion of impervious categories of land use in each watershed in 1995. There is variation of each land use category amongst the watersheds. The least represented land use types are urban and industrial, and old growth, being represented in only half of the watersheds 24 Figure 5. Impervious categories of land use for combined watersheds in 1955, 1975 and 1995 25 Figure 6. Percent of total land use within three different sized buffer zones and entire watersheds in the three time periods 26 Figure 7. Average coho fry densities (1991-1997) of all study streams 27 Figure 8. Total coho fry densities of all study streams from 1991 to 1997 27 Figure 9. Total coho escapement for all streams in the study from 1953 to 1997 .... 28 Figure 10. Top graphs are the natural log of escapement for each watershed from 1953 to 1997. Bottom graphs are land use (impervious grouping) for each watershed in 1955, 1975 and 1995 30 Figure 11. Least squares linear regression plot of mean coho fry density (1991-1997) (square root transformed) against percent land use (broad grouping) in 1995. Line of best fit is indicated. The coefficent of determination and associated probability are presented for each relationship 33 Figure 12. Least squares linear regression plot of mean coho escapement (1990-1997) (square root transformed) against percent land use (broad grouping) in 1995. Line of best fit is indicated. The coefficient of deterrnination and associated probability are presented for each relationship 34 Figure 13. Least squares linear regression plot of mean coho escapement (1970-1985) (square root transformed) against percent land use (broad grouping) in 1975. Line of best fit is indicated. The coefficient of deterrnination and associated probability are presented for each relationship 35 vi Figure 14. Least squares linear regression plote of mean escapement (1953-1965) (square root transformed) against percent land use (broad grouping) in 1955. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 36 Figure 15. Least squares linear regression plot of mean fry (1991-1997) and population growth rate against percent land use (agriculture and urban broad land use groupings) for the post-1975 time period (1975-1995). Line of best fit is indicated. The coefficient of detenriination and associated probability are presented for each relationship 38 Figure 16. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 40 Figure 17. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 41 Figure 18. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) in a 100 metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 44 Figure 19. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) in a 100-metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 45 Figure 20. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) in a 250 metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 46 Figure 21. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) in a 250-metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 47 Figure 22. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) in a 500 metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 48 vii Figure 23. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) in a 500-metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 49 Figure 24. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) and population growth rate against percent land use (agriculture and urban broad land use groupings) for the entire time period, 1955 to 1995. Line of best fit is indicated. The coefficient of deterrnination and associated probability are presented for each relationship 51 Figure 25. Least squares linear regression plot of mean coho fry against percent land use (impervious land use groupings) for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship :.... 53 Figure 26. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 54 Figure 27. Least squares linear regression plot of mean fry (1991-1997) and population growth rate against percent land use (agriculture and urban groupings) in a 100 metre buffer zone for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 57 Figure 28. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) and population growth rate against percent land use (agriculture and urban groupings) in a 250 metre buffer zone for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship 58 Figure 29. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) and population growth rate against percent land use (agriculture and urban groupings) in a 500 metre buffer zone for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of deterrnination and associated probability are presented for each relationship 59 Figure 30. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against stream physical attributes: mean gradient, mean sinuosity, mean temperature, and conductivity. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship ... 61 Figure 31. Least squares linear regression plot of mean escapement (1990-1997) (square root transformed) against stream physical attributes: mean gradient and mean sinuosity. Line of best fit is indicated. The coefficient of deterrnination and associated probability are presented for each relationship 61 viii Acknowledgements I wish to thank my supervisor, Dr. Carl Walters, and committee members Dr. Scott Hindi and Dr. Blair Holtby for providing guidance and constructive criticism in planning and analyzing this research project. Drs. Brian Klinkenberg, Hans Schrier and Les Lavkulich were of great assistance with land use data collection. Kent Simpson of the Department of Fisheries and Oceans (DFO) was particularly helpful with visiting and selecting coho fry collection streams. Brad Mason and his group at the DFO kindly provided digitial maps, orthophotos and good advice. Field assistants who helped groundtruthing -"tell the truth, ground, or else" - were Dan Reinders, Karl Vernes and Dale Kolody. Very free with advice and encouragement was Dr. Ernest Keeley. Thanks especially to many co-grad students. Data used in this study was kindly provided by Blair Holtby from the DFO, and funding was provided by the Habitat Restoration & Salmon Enhancement Program of the DFO. 1 CHAPTER I - INTRODUCTION Stream habitat loss is often cited as a cause of anadromous salmonid population declines. This study examines the problem of quantifying stream habitat loss, which has been implicated in the decline of coho salmon (Oriaarbyncbus kisuteh) (Brown et al. 1994; Frissell 1993; Nehlsen et al. 1991; Slaney et al. 1996). Wild coho salmon stocks in the Strait of Georgia, British Columbia have declined by at least 50% since the mid-1970's (Walters 1993; Levy et al. 1996; Simpson et al. 1997). Moreover, there has been a loss in spatial diversity of coho, with fewer, larger populations contributing to overall productivity (Walters and Cahoon 1985). In a recent report on the status of anadromous salmon and trout in the Yukon and British Columbia (Slaney et al., 1996), 29 coho stocks were classified as extinct, half of which were located on the mainland side of the Strait of Georgia, 214 stocks were classified at high risk of exinction, 22 at moderate risk, and 21 of special concern. This dramatic decline is of particular concern to the valuable recreational and commercial fisheries in British Columbia, which are considered to be in a crisis situation. Coho salmon are found in the northern Pacific Ocean in the northern Pacific Ocean. Their range extends from the coast of Japan to the Bering Sea and along the western coast of North American California (Sandercock 1991). Coho are semelparous, spend approximately 18 months at sea and return to their natal streams at maturity to spawn (McPhail and Lindsey 1970). In British Columbia varying proportions of coho populations in the Strait of Georgia may remain in the Strait for their marine lifespan (Levy et al. 1996). The remaining coho migrate off the coast of British Columbia to feed in the open ocean. Strait of Georgia populations return to spawn in freshwater streams distributed throughout most of the study area, in numbers ranging from less than a thousand to 75,000 spawners (Levy et al. 1996). Coho may spawn in small first 2 order headwater streams, small and large tributaries, side channels, and the mainstems of large rivers (Sandercock 1991). Females lay between 2000 and 3000 eggs (Sandercock 1991) in gravel substrate. Eggs hatch three to four months after oviposition (van den Bergh and Gross 1989). After one summer and winter rearing in freshwater, juveniles migrate to sea as smolts, returning as adults typically in the second fall of their life at sea . In a comparative review of pacific salmon survival rates, Bradford (1995) reported an average coho egg to smolt survival of 1.5 percent. Coho marine survival rates (smolt to adult) range from less than one percent to 19.1 percent (Sandercock 1991). Coho inhabit most segments of a river system: small and large tributaries, side channels, lakes, ponds, and mainstem river (Irvine and Johnston, 1992; Benda et al. 1992). At the reach level, pools are preferred by coho for resting periods during migration (McMahon and Holtby, 1992), and rearing (Nickelson et al. 1993a). According to Bisson et al. (1988) coho choose pools with velocities less than 20 cm/s for rearing. The body shape of coho juveniles is condusive to foraging and predation avoidance in pools. They have a laterally-compressed body with large median and paired fins which facilitate rapid turns and quick but transient burst swirnrning (Bisson 1988).. Land use practices have resulted in some streams becoming inaccessible or inhospitable to stream-dwelling fishes. Stream inaccessibility is caused by culverts, diking, and dredging which are all associated with urbanization and agricultural development (Beechie et al., 1994). Dewatering of streams resulting from water diversion for agriculture can result in summer habitat loss and the practice appears to be increasing in some areas of Vancouver Island (Brad Mason, DFO, pers. comm.) Damming causes inundation of tributaries and increases lake and pond area. Logging and mining activities can create barriers caused by landslides. Habitat degradation may result from channelization, largely due to urban and agricultural development, and aggradation of sediment and loss of large woody debris. This often leads to increased 3 stream flow velocities, fewer pools and more riffle-type morphology. Loss of pools due to channelization may be detrimental to coho populations. Lucchetti and Fuerstenberg (1993) report that as urbanization in watersheds of King County, Washington increased and riffle stream morphology became dominant, coho abundance declined and streams became dominated by cutthroat trout. Where historical information on stream habitat features exists, one may remeasure these features to ascertain habitat loss. For example, Beechie et al. (1994) estimated coho habitat loss in the Skagit River basin. They identified tributary and side channel losses through diking, ditching and dredging, forestry practices, inundation due to damming, and blocking culverts. They acquired historical stream data from maps and surveys made early this century. Where data didn't exist, they estimated expected pool/riffle ratios at given gradients and volumes of large woody debris from unmanaged forests of the North Cascade and Olympic Mountains in Washington. Considerable stream loss was due to inundation of tributaries caused by dams. They then related loss of pools to loss of coho productivity. They report that where less than 50% of stream area was pool, rearing densities were 50-70% lower than in streams with 50% or more pool area. They further assert that the parameter of pool percentage as it relates to coho production appeared to be a surrogate for overall geomorphic and habitat characteristics of a system. Similarly, in a study of changes in fish habitat over 50 years in 1500 km of streams throughout the Columbia River basin, Mcintosh et al. (1994) found a 28% loss of large pool frequency in managed watersheds (logging, roads, livestock and agriculture). Conversely, they . found an increase in the frequency of large pools (>20 m2 and > 1.0 m deep) of 77% in unmanaged watersheds (wilderness, no roads). Further, they report similar changes in streams of eastern Oregon and Washington. The method they employed was to resurvey streams, which had been surveyed by the U.S. Fish and Wildlife Service between 1934 and 1946. 4 Unfortunately, historical stream inventory data such as pool/riffle surface area are sparse in British Columbia, and re-measuring data from scattered studies is questionable because of difficulties in repeating methods. Watts (1992) found major difficulties in repeating methods used to collect historical stream inventory data on the Salmon River in the Lower Fraser Basin. To bypass the problem of direct comparative measurement of stream habitat changes in this study, I adopted land use as an indicator of potential habitat loss because land use shapes stream morphology and determines stream chemistry. For example, urbanization and agriculture are related to both stream loss (cutoff by culverts and diking), and channelization by mechanical means, such as ditching, diking, and dredging (Beechie 1994; Scott 1986; Rhoads 1991; Booth 1991). Booth (1991) suggested that urbanization alters watershed hydrology by magnifying peak discharges, creating new peak runoff events and reducing base flows. This may contribute to channelization. Logging may also cause channelization by increasing sedimentation rates. Brookes (1994) asserts that the most extensive disturbance affecting streams is land use such as agriculture, forestry, mining, grazing and urbanization. Channels become wider, shallower and less sinuous, and much eroded sediment is retained in channels and floodplains (Brookes 1994). Channelization may result in channel instability and reduced flood attenuation, particularly in valley floors (Rhoads 1991). Rhoads (1991) suggests that instability occurs when channels concentrate flow and depositional systems become erosional ones. Furthermore, increases in hydraulic head and gradient associated with channel incision promote rapid flow of subsurface water through the faces of the channel banks. He also states that channelization reduces attenuation of peak discharge by decreasing floodplain storage. Stream sinuosity also provides complexity, such as undercut banks, higher pool to riffle ratio and more retention of large woody debris, which is lost with channelization (Scott Hinch, personal communication). In summary, land use has been shown to affect stream flow, pool/riffle ratio, instream woody debris, and channel sinuosity. 5 An additional reason that I used land use as an indicator of habitat loss was that it can be measured from aerial photographs which have been taken about every 10 years since the 1940's in British Columbia. Aerial photographs allow for consistent and repeatable land use data collection. I also obtained temperature, conductivity, sinuosity and gradient for each study stream to see if these qualities were useful in predicting coho abundance. I was able to compare land use types to time series trend estimates in spawning numbers collected by Department of Fisheries and Oceans (DFO) personnel since 1953, and coho fry density collected by the DFO in a sampling program that has been in place since 1990. My hypothesis is that habitat loss caused by land use changes is not related to coho stock decline since the 1970's. I base this hypothesis on three observations. First, a majority of the Georgia Basin and Lower Fraser Basin lowlands was developed and logged before the 1970's (Levy et al., 1996), and it is doubtful that land development has proceeded at a high enough rate and intensity since then to cause the recent declines in coho. Indeed, mining and logging interests in British Columbia burgeoned with European colonization in the early- to mid-1800s. Second, Walters (1993) gave a worst case scenario of 24.3% accessible stream length loss by assuming total loss of all streams less than 5 km in length. Third, alternative hypotheses for coho stock fluctuations, all involving marine survival changes in the Strait of Georgia appear to better explain population trends. For example, overfishing (Walters 1995; Levy et al. 1996), climate change (Beamish 1993), solar radiation (Walters and Ward, 1998), predation (Beamish and Neville, 1995), ocean conditions (Coronada and Hilborn 1998) and competition with hatchery fish are some alternative hypotheses suggested for the marine survival changes. Slaney et al. (1996) cite not only habitat destruction (with emphasis on urbanization effects on low elevation coho habitat), but over-utilization, mixed stock fisheries, and disease from cultured, feral and wild populations as possible causes of coho decline. Walters (1993) suggests that because coded wire tagging studies have shown that marine smolt to harvestable-size survival rates have 6 declined enough to account for observed changes in total catches and escapement, then wild smolt production must be stable or increasing. A 1997 (Simpson et al.) Pacific Stock Assessment Review Committee (PSARC) paper showed that marine survival has declined steadily since the mid-1980's for the Chilliwack Hatchery, Salmon River, Quinsam hatchery, and Black Creek coho. In California, the reasons presented for serious coho salmon stock declines are similar to those described above (Brown et al. 1994); however, habitat loss may be more serious than in British Columbia because of extensive impoundment of rivers and "urban sprawl" development. Levy et al. (1996) concluded in their review of fisheries sustainability in the Strait of Georgia, that the major cause of non-sustainability of coho stocks may be over-exploitation. They also stressed that it is critical to provide a realistic assessment of how much • identified hypotheses contribute to non-sustainability. It is critical for management to allocate effort proportionally to rebuild coho stocks in the Strait of Georgia. Three questions are addressed in this study to determine the relationship between land use and coho abundance. The first question is: Does the level of disturbance of a watershed affect coho fry or coho escapement? To answer this question, I compared land use and coho abundance across watersheds for three different decadal time periods. The second question is: Does change in land use in whole watersheds or in riparian zones affect coho escapement or fry abundance? I compared temporal trends of land use in whole watersheds and in riparian zones with coho escapement trends over a 40-year time period to address this question. The third question is: Do selected physical stream attributes affect coho fry or population growth rates? These attributes were compared with coho fry abundance, and with an index of population growth (decline) rate calculated from escapement trends. 7 CHAPTER II - MATERIALS AND METHODS 2.1 Study area It was not practical for me to map habitat changes from aerial photos for the entire Georgia and Lower Fraser basins. I selected fifteen watersheds (Fig. 1) for which full aerial coverage was available and also coho fry and wild coho escapement data were available. The Georgia Basin is the land surrounding the Strait of Georgia off the coast of British Columbia (Fig. 1). This basin is defined by two mountain ranges: the Insular Mountains on the east coast of Vancouver Island, and the Coast Mountains on the mainland coast of British Columbia. The Lower Fraser Basin is the land surrounding the Fraser River from Vancouver to Hope. It extends south to the border between Canada and the U.S.A., and north of Pitt Lake (Boyle et al., 1997). The Lower Fraser Basin is about 4% of the entire Fraser River basin, which drains most of the central interior of British Columbia. The area of the Georgia and Lower Fraser basins combined is approximately 29,805 square kilometres, and is part of the coastal temperate rainforest ecosystem of British Columbia. Biogeoclimatic zones represented in the Georgia and Lower Fraser basins, from sea level to higher elevations, are Coastal Douglas Fir, Coastal Western Hemlock, Mountain Hemlock and Alpine Tundra. The Alpine Tundra zone starts at 1000 to 1400 metres (Levy et al. 1996). Dominant conifers in these zones are Douglas Fir {Psuedotsuga menziessi), Amabalis Fir {Abies amabalis), Grand Fir (Abiesgrandis), Mountain Hemlock {Tsuga menensiana), Sitka Spruce {Pkea sitchensis), Western Hemlock {Tsuga betempbylla), and Western Red Cedar {Thuja plkata). Some common deciduous tree species occurring in these zones are Black Cottonwood {Populus balsarnifera ssp. trichocarpa), Big Leaf Maple {Acer Macrvphyllmi), Garry Oak {Quercusgarryana) and Red Alder {Alnus rubra). Approximately 80% of the study area has either been cleared for settlement, or has been logged and regrown naturally or replanted with commercial tree species. The average rotation for a commercial tree stand is 70 8 years. Proportions of land use in the Georgia and Lower Fraser basins is shown in Table 1 and Table 2 respectively. Elevations in the study area range from sea level to 4000 metres. The Coast Mountains (also called the Coast Ranges Batholith) consist of intrusive igneous or metamorphic rock, with smaller patches of extrusive or volcanic rock. The Insular Mountains are extrusive (volcanic) in nature. Eastern Vancouver Island consists of volcanic rock, metamorphosed volcanic rock, consolidated sedimentary rock, unconsolidated riverine sediments (alluvium), and glacial drift (Levy etal. 1996). The climate of the study area is generally temperate. Average annual precipitation varies between 700mm to 2100 mm (Levy et al. 1996). Daily air temperatures range from 0-5°C in winter and 12 to 22°C in summer. On the mainland side of the Strait of Georgia, high river flows caused by melting snow and ice occur during spring and early summer. Highest flows on Vancouver Island, on the other hand, occur during heavy rainfall in the winter (Levy et al. 1996). 9 Figure 1. The Inset map is British Columbia. The enlarged map is the east coast of Vancouver Island, the coastal mainland of British Columbia and location of the study watersheds. Table 1. Land use classifications and area in the Georgia Basin. Land use Area (ha) Percent of Basin Mature forest 463,950 22 Immature forest 1,111,936 52 Urban 149,277 7 Alpine/glacier 374,383 17 Freshwater 52,273 2 Unknown 565 <1 Total area 2,152,384 100 FromMulholland 1996 Table 2. Land use classifications and area of the Lower Fraser Basin Land use Area (ha) Percent Coniferous 445,800 54 Deciduous/ mixed 4,000 <1 Fen 2,400 <1 Swamp/bog/marsh 9,700 1 Agriculture 132,000 16 Urban 86,300 10 Cleared 8,600 1 Rock/alpine 37,800 5 Lake/river 46,600 6 Ocean 54,900 7 Total land area 828,100 From Boyle et al. 1997 11 2.2 Land use data collection To provide information on land use changes over time, I analyzed two types of aerial photographs from selected watersheds. First, I obtained orthophotos from 1995 to provide a high-resolution analysis of "current" land use. Second, I collected land use from historical black and white aerial photos for time periods in the mid-1950's and the mid-1970's. For collection of 1995 land use, I used the GIS program Arcview to digitize polygons around distinguishable land use types defined in Table 3. Orthophotos of the Fraser Basin were colour photos, had a scale of 1:20,000 and a screen resolution of one metre per pixel. The Stawamus River and Vancouver Island orthophotos were black and white, had a scale of 1:20,000, and a screen resolution of two metres per pixel. 12 Table 3. Land use categories identified on aerial photographs and orthophotos and a description of each land use category. LAND USE CATEGORY DESCRIPTION Pasture Agriculture Grass Second growth Old growth Urban Rural residential Rural farms Industrial Golf course Shrub Water Gravel/sand Clearcuts Fields used for livestock grazing and hay, with low housing density. Various crops that could not always be distinguished from pasture (and are sometime rotated with hay pastures) Parks or grass beside highways and along power lines. Second growth forests in the Fraser Valley is a mix of conifer and deciduous trees, and on Vancouver Island a mix of both in low elevations and regeneration conifer in higher elevations. Old growth forests dominated by conifer stands that had never been harvested. High density block housing. Lower density housing than urban land use on acreages or separated by small stands of trees. Farms with higher density housing than agriculture or pasture, such as hobby farms that raise llamas, sheep or emus. Areas of solid concrete, such as malls and parking lots. Fairways, greens, occasional buildings. (There is only one golf course that is in the Murray system). Vegetation below approximately 10 metres in height, generally small trees (regrowth in clearcuts) or bushes. Lakes Extraction plots, landings, sandpits, and sandy beaches. Blocks of recently logged forest, which was either not replanted or had only small seedlings. 13 Interpretation of aerial photos was aided by magnifying the images (using a zoom function with Arcview for orthophotos and a stereoscope for aerial photos) and using aerial photo interpretation techniques such as association of objects with surrounding objects, relative shades of colour and surface texture. To deterrnine how representative the study watersheds were of the Fraser and Georgia basins, land use in all watersheds was combined for each basin and compared to two previous studies of land use: one of the entire Georgia Basin by Mulholland (1996) and the other of the Lower Fraser Basin by Boyle et al. (1997). I collected land use from historical black and white aerial photos for time periods in the mid-1950's and the.mid-1970's. Dates and scales of aerial photos are in Table 4. Table 4. Dates and scales of historical aerial photographs used to collect land use within different areas of the Georgia and Fraser Basins. AREA DATES OF AERIAL PHOTOS SCALE OF AERIAL PHOTOS Lower Fraser Basin 1954 and 1979 1:12 000 Stawamus River 1954 and 1982 1:12 000 East Coast Vancouver Island 1957 and 1975 1:12 000 1:30 000 For the sake of simplicity, in the remainder of this thesis the dates for all photos in the mid-1950's and the mid-1970's (1982 for Stawamus River watershed) are referred to as 1955 and 1975 respectively. I collected land use from historical aerial photos by placing each one on a digitizing tablet and geo-referencing it to a digital orthophoto of the same location. This involved selecting several identical points on both photos, such as road intersections and river confluences, and fitting the black and white aerial photo to the orthophoto using Arcview. With this method, the points selected for georeferencing were aligned, but other points on the photos 14 may or may not have been aligned, depending on the extent of topographical displacement. The spatial error of fitting involved in this process is measured in root mean squared error (rmse). In map units, rmse ranged from two metres on flat landscapes to an extreme case of 50 metres on a mountainous landscape. Therefore, in this study, land use is reported only to the scale of one hectare. I considered this to be an acceptable level of accuracy, since most polygons drawn around land use types were greater than one hectare, and were mostly in the tens or hundreds of hectares. Digitizing land use on each photo resulted in many polygons of each land use type. I used Arcview to merge similar polygons, and to calculate total area in each watershed for each type of land use. Land use for three different width buffer strips beside streams was obtained using Arcview's "Buffer" and "Clip" options. I obtained land use for buffer areas of 100, 250, and 500 metres along the entire length of streams. Road density was acquired by digitizing the length of roads using lines instead of polygons. Road lengths were multiplied by five metres to transform length into area. I verified land use on the 1995 orthophotos by field surveys. I surveyed each watershed by vehicle or on foot when necessary, identifying landmarks (such as intersections or power line towers) from the aerial photos and determining ground land use in relation to these landmarks. I used a global positioning system (GPS) unit to determine coordinates of areas that did not have obvious landmarks. For land use categories that occurred numerously over wide areas, such as shrub, second growth and old growth forest, I verified sub-samples of the photos. Then, I used these subsamples to verify other areas on the photos with similar characteristics. One useful characteristic for identifying different types of vegetation was the coarseness of vegetation. For example, grass looked very smooth on photos, shrub in clearcuts looked relatively coarser than grass, and tree patches looked coarser the older they got, due to multi-15 aged tree composition and gaps that occur in older forests. I applied aerial photograph interpretation techniques from a very useful book by Paine (1981). 2.3 Coho fry densities Coho fry density data were obtained from the Department of Fisheries and Oceans Pacific Biological Station in Nanaimo, B.C. Coho fry surveys were made between August and October of each year from 1991 to 1997 using either two- or three- pass removal methods in a selected reach of a stream (Kent Simpson, DFO, personal communication). The reaches were sealed off with nets, and multiple passes made along the reach with seines and/or electroshocking equipment. Actual fry density within a reach was estimated using the following models. For two-pass removal surveys, the formula: N0 = — was used, where 7VD is the estimated population size, and c, is number of fish caught in the ith pass. This model is a modification by Robson and Reiger (1968) of a formula developed by Seber and LeCren (1967). _ . , r r i 6x2 -3xy-y2 Jy2 -6xy-3x7 tor three-pass removal surveys, the formula: Na = 18(*-y) was used, where x = 2q + c2 and y = cx + c2 + c3. This model is an explicit solution to a maximum likelihood estimate developed by Zippin (in Cowx 1983). Cowx (1983) provides a description and review of the above population estimates. Fry per square metre of stream was used for analysis. Fry were used in this study because they spend one year in fresh water summer and winter habitats. The fry captured were mostly 1-year-olds, with a small proportion of 0+ and 2-year-olds. Fry collection sites were not randomly selected, but were chosen because they were considered good coho fry habitat. This means that density estimates may be less variable among watersheds and over time than would have been observed had the study sites been selected at random, i.e. the sampling design was not intended to reveal spatial and temporal trends. Further, the density estimates probably do not reflect differences among watersheds in proportion of "good" fry habitat. 2.4 Coho escapement Coho escapement estimates from 1953 to 1997 were obtained from the Department of Fisheries and Oceans (DFO). Escapement estimates were made by DFO officers generally by visual surveys (Irvine et al. 1992). This was usually done by walking along section(s) of stream, counting or estimating numbers of live or dead fish, and multiplying these numbers by stream length to get an estimate of spawners for the entire stream (Irvine et al. 1992). Often, numbers were multiplied by the proportion of stream remaining unsurveyed (C J. Walters, personal communication), or expanded with other unspecified techniques (Irvine et al. 1992). As a result, biases may occur in this data set. Nevertheless, the data provide a "coarse index" of spawner trends and show similar population abundance trends to the DFO's indicator creeks (Black Creek and Mesachie Creek on Vancouver Island) where coho have been assessed with counting fences and mark-recapture methods (Levy et al. 1996). Coho spawner estimates were used in this study because of the fresh water requirements of spawners, such as space, temperature and substrate, and the potential impact of land use on these habitats. 2.5 Physical characteristics of streams Gradient and sinuosity of streams were obtained from the B.C. Ministry of Fisheries' Watershed Atlas. Streams were digitized from l:50,000-scale maps for the Atlas. Sinuosity was 17 measured by dividing streams into reaches of varying lengths and dividing the straight line between two end points of a reach by the actual length of stream between the two points. Gradient was calculated for each reach length based on the difference in elevation between the two end points of the reach. DFO personnel collected temperature and conductivity between August and October of each year (1991-1997), at the same locations as coho fry surveys were done. 2.6 Methods for statistical analyses Land use data were grouped in various ways for comparison to escapement over time, mean escapement per decade, and coho fry estimates. Grouping of land use reflected potential impacts of land use on stream characteristics. For example, ratio of streamflow to rainfall, stream volume, and flow fluctuation increase with a combination of increased impervious surface area and climinished water retention of land cover. I developed an index of land use that reflects the effect of impervious surfaces and water retention of vegetation on stream flow (Table 5). I've termed this the "impervious" index of land use. Land use was also grouped into broader categories which reflect potential impacts of broad anthropogenic land uses on streams. These categories are listed in Table 6. To test the impact of impervious and broad land use groups on coho fry and escapement, regression analyses were undertaken. Land use, measure as proportion of watershed, (LU) was transformed when there were outliers in regression analyses, according to the formula: LU' = arcsin -JLU . I regressed mean coho fry per square metre (1991-1997) against LU in 1995, against change in land use over the entire time period, and against change in land use for the post-1975 time period. Mean fry density was transformed according to the formula: Fry' = ^Fry + .5 . 18 Table 5. Index of imperviousness of land cover, land use categories included in each index level, and an explanation of overland water flow behaviour for each case. Land surface impervious index Land use categories included in impervious index level Explanation High Industrial, urban, farm buildings Hard surfaces cause water to flow overland at high speeds. Medium high Rural residential and rural farms Semi-hard surfaces: lawns, driveways and relatively dense housing. Some infiltration of precipitation into soil. Medium Agriculture, pasture, grass parks, gravel/soil Little water retention by vegetation, infiltration of water into soil, relatively slower drainage Medium low Shrub Increased water retention by relatively high leaf surface area, slower drainage. Low Second growth Higher leaf surface area than shrub - greater water retention, slower drainage Very low Old growth Higher leaf surface area than second growth - greater water retention, slower drainage Roads All road types Impervious to water. Culverts associated with roads may affect streamflow. Table 6. Broad land use classification and land use categories included in each classification and an explanation of anthropogenic influences. Land use category Land use types included Explanation Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Forested Old growth, second growth Second growth Immature forest > 10 metres Old growth Mature forest Clearcut & Clearcuts, shrub shrub High level of human development and human activity & potential non-point source pollution Medium level of human development, high fertilizer input Logged forest, parks Logged forest that has either been clearcut or selectively logged leaving small patches of forest Forest that has never been logged. Recent clearcuts and regrowth < approximately 10 metres in height 19 Mean escapement per kilometre for each decadal time period was regressed against land use for each period to determine the overall effect of watershed state on coho abundance. Escapement data were available from 1953 to 1997. For the three time periods of land use: 1955, 1975 and 1995, mean escapement was calculated for 1953-1965, 1970 - 1985 and 1990 -1997 respectively. Only the stream segment of useable gradient for coho salmon was used. Useable gradient for coho was assumed to be <4% (Bradford et al. 1997). For statistical analyses, mean coho escapement (CE) was transformed according to the formula: CE' = 4CE~+J. Square-root transformations of mean escapement and mean fry densities were performed because residuals showed unequal variances. As multiple independent variables were used in the regressions, a result is only considered significant at a "table-wide" alpha level, as determined by a sequential Bonferroni test (Rice 1989), calculated for an alpha value of 0.05. Power is reported for all tests for an alpha value of 0.05. I did regression analyses of population growth rate as measured in escapement trends, on percent land use change in each watershed for each of the above land use classifications for the entire time period (1955 to 1995), and for the second half of the time series (1975 to 1995). These time periods were chosen because coho escapement begins a pronounced decline in the mid- to late-1970's. I used the rate of coho population growth (g) from these two time periods in the analyses. The formula for the rate of growth is: Nt = N0ert, where TV is escapement, r is rate of growth and t is time. The value r was estimated by taking the natural log of escapement estimates over the appropriate time period and fitting a linear regression to the data points (In N t = dependent variable, In N Q = intercept, t = independent variable and g = regression slope). 2 0 CHAPTER III - RESULTS This section describes land use patterns estimated for the study watersheds, and compares these patterns to indices of coho fry abundance and coho population growth. 3.1 Land use The most common land cover for combined watersheds consisted of second growth forest (58%), including coniferous stands and conifer-deciduous mixed forest (Figure 2). The next highest land cover type was agriculture, including pasture and grass parks (12%). Old growth comprised 11% and clearcuts and shrub 10% cover. Shrub was mostly regrowth in clearcuts. Rural residential and rural farms covered 7%, and urban and industrial land covered 2%. Roads covered < 0.5%. To determine how representative the study watersheds were of the Georgia Basin, I compared 1995 land use in the study watersheds with two separate estimates of land use in the Georgia Basin (Mulholland 1996) and the Lower Fraser Basin (LFB, Boyle et al. 1997) (Figure 3). The Georgia Basin study showed 18% alpine/glacier land cover. Alpine/glacier is not a category in my study since I could not distinguish a definite boundary between alpine areas and surrounding old growth or second growth forest, so alpine was included in the dominant surrounding land use type. Since most of my watersheds (except Stawamus) are coastal watersheds, alpine/glacier land cover is probably underrepresented. Developed areas in the Georgia Basin study (mcluding urban, industrial, rural residential and farm, and agricultural land use) was 6%, and in my study was 11%. Old growth is 19% in the whole basin, and 14% in my study. Immature forest in the Georgia Basin is 54%, and includes clearcuts and shrub. In my study, second growth and clearcuts comprise 75% of land use. Again, the differences in land use between the entire Basin and this study are related to the coastal nature of the study watersheds. 21 The LFB study by Boyle et al. (1997) divided land use into finer categories than I did. Nevertheless, a comparison is possible by grouping the LFB land use into groups used in my study. Second growth coniferous forest comprised 43% of land use types in the LFB study, and 41% in my study. However, second growth in the LFB study is mostly coniferous, whereas in my study is mostly a mix of coniferous and deciduous vegetation. That is because most of this study's watersheds are located in the lowlands, with the exception of upper Whonnock and Maclntyre watersheds. For this same reason, old growth is somewhat underrepresented at 2% versus 11% for the LFB study. Agriculture is greater in the study watersheds (31%) than in the LFB study (16%). Urban is 10% in the LFB and 2% in this study. Individual watersheds vary in their levels of anthropogenic disturbance (Figure 4). The mainland watersheds that lie south of (and flow into) the Fraser River - Nathan, Murray, and Coghlan - are relatively flat, lowland rural watersheds consisting primarily of agriculture and rural dwellings. North of the Fraser River, Whonnock watershed is developed at lower elevations, but is forested at higher elevations. This is the trend for all remaining watersheds except Maclntyre, which is almost all forested. Millard, on Vancouver Island, is the only watershed with a high proportion of urban land use. Over time, urban and industrial land use, agriculture, rural residential and small farms increased (Figure 5) and as a result old growth forest has declined. Second growth increased between 1955 and 1975, and shrub decreased in the same time period. Comparison of percentage land use in the whole watershed and four buffer widths beside the streams reveals that proportion of land use in the whole watershed, and subsections of the watershed near streams does not show much difference (Figure 6), i.e. buffer strip have basically the same land use composition as whole watersheds. 22 i <l% • Urban, industrial • Rural: residential & farms ffl Agriculture, grass parks • Clearcuts, shrub • Second growth conifer & deciduous H Old growth • Roads Figure 2. Percent land use for all the study watersheds combined. Land use is grouped into impervious categories described in the materials and methods section. 23 3 e a c3 •4= C to T3 5 ca t>0 ^ r 3 so • 0 us «8 ori| 1 growth & decid tri +J a M growth & decid wth indus eside 3 o an, *—, • Clearc n «§ So ds an, "c3 • Clearc • Seci coni -o O ESI • Roa 3 m • Rur o Pi C3 cq o u O T3 CJ JS ca -a 3 00 ca T 3 CJ JS c3 a 00 2 J3 C3 3 cn ca ca m T3 f CD > Q 3 ca El cj ca ta [3) & cj SS '3-E • yrs yrs o o CN CN CJ A V .S cn 3 3 3 O O O C+H idu '3 '3 o o o CJ U U Q El • • 60 O I* ca 00 c ca X i 0 H T3 cj tH ca cj u CJ s >-£• > ca •i-' 3a o o O J • 0 o • P H L H CJ o cj ca CO , 5' '5b o u O 3 pa o CJ O as as " O I X J tu o o C/3 3 ca as O CQ O o oo c cj cj JS o o Cl, o -a rH c/l « .3 i-J « cd M CJ E 3 Land use legend H Urban, industrial • Rural: residential & farms H Agriculture, grass parks H3 Clearcuts, shrub • Second growth conifer & deciduous ^ Old growth • Roads Figure 4. Proportion of impervious categories of land use in each watershed in 1995. There is variation of each land use category amongst the watersheds. The least represented land use types are urban and industrial, and old growth, being represented in only half of the watersheds. 25 Roads Old growth Second growth conifer & deciduous Clearcuts, shrub Agriculture, grass parks Rural: residential & farms Urban, industrial t L 0 5000 10000 15000 20000 25000 H e c t a r e s Figure 5. Impervious categories of land use for combined watersheds in 1955, 1975 and 1995. 1995 26 100% 80% 60% 40% 20% 0% m Whole WS 100m 250m • 500m 1975 100% 80% -| 60% 40% 20% 0% m Whole WS 100m 250m 500m 1955 100% 80% 60% 40% 20% 0% Whole WS 100m 250m 500m • Roads H Old growth • Second growth conifer & deciduous E Clearcuts, shrub H Agriculture, grass parks GD Rural: residential & farms II Urban, industrial Figure 6. Percent of total land use within three different sized buffer zones and entire watersheds in the three time periods. 27 3.2 Coho fry Average coho fry density for the streams ranged from 0.43 to 5.8 fry per square metre (Figure 7). The mean of all fry estimates suggest an overall decline over time (Figure 8). Stawamus Bush Whonnock Nathan Murray Maclntyre Coghlan I f> Waterloo is oo Nile Nanoose Morrison Millard Chef Black Beck tegH 0.00 1.00 2.00 3.00 4.00 Fry / sq metre 5.00 6.00 7.00 Figure 7. Average coho fry densities (1991-1997) of all study streams. Figure 8. Total coho fry densities of all study streams from 1991 to 1997. 28 3.3 Coho escapement The sum of escapements for all the study watersheds from 1953 to 1997 fluctuated between <2000 and 18000 spawners (Figure 9). Mean spawners from 1953-1995 was 6260, and from 1976-1995 (22 years) was 4282. 20000 18000 16000 g 14000 | 12000 § 10000 | 8000 ° 6000 4000 2000 0 i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—rn—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r ^ ^ J> x^ ^> / N^ N^ ^ ^ Year Figure 9. Total coho escapement for all streams in the study from 1953 to 1997. 29 3.4 Stream attributes Watershed sizes ranged from 649 to 7471 hectares (Table 7), and watershed area was correlated with stream length (r = 0.73, p = 0.002). Average gradient ranged from 1.22 percent (Black Creek) to 22.29 percent (Stawamus Creek). Maximum gradient was the lowest for Morrison Creek (3.42%) and the highest for Stawamus Creek (68.68%). Stream sinuosity ranged from 5.31 to 97.98. Temperatures taken at the coho fry collection sites ranged from 11.83 to 16.23 degrees Celsius. Conductivity at the coho fry sites ranged from 18.20 to 383.33 microsiemens. 3.5 Comparison of land use to coho escapement and coho fry Escapement for each stream declined from the mid-point of the time series (~ 1975-1980) with the exception of Nathan and Salmon Creeks which showed an increase in escapement in the 1970's (Figure 10). Data were scant for some watersheds. Murray Creek, for example, only had a few years of escapement data. Land use change over three time periods for seven impervious land use categories shows decline in old growth and clearcut, replaced by second growth forest. The other categories - urban, rural, agriculture and roads - all show steady increases. A visual inspection of this data (Figure 10) does not reveal any obvious relationships between escapement and land use trends. 30 Murray Nathan Salmon Whonnock Black Nanoose Waterloo Chef Bush Nile Millard Morrison Beck Maclntyre Stawamus Land use legend Horizontal axes: Top graph: year (1953 to 1997) Bottom graph: land use (1955, 1975, 1995) —•—Urban, industrial —•—Rural: residential & farms A Agriculture, grass parks X Clearcut, shrub X Second growth conifer & deciduous —e— Old growth —1— Roads Figure 10. Top graphs are the natural log of escapement for each watershed from 1953 to 1997. Bottom graphs are land use (impervious grouping) for each watershed in 1955, 1975 and 1995. 31 Table 7. Physical attributes of study streams. Watershed Stream Stream Average Average Maximum Average Temp. Conductivity area (ha) length (km) length <4% gradient gradient (%) gradient <4% (%) gradient (%) sinuosity ("Q Beck 1453 7.8 7.8 14.80 383.33 Black 7471 176.2 165.7 1.22 2.74 7.49 1.15 14.18 85.85 Bush 2901 28.4 7.6 4.96 1.76 17.35 1.05 13.83 227.90 Chef 2705 52.6 10.4 12.69 2.67 49.18 1.05 12.06 85.76 Coghlan 1529 19.5 19.5 1.46 1.46 3.82 1.12 11.83 137.43 Maclntyre 649 13.5 12.7 12.81 2.55 40.33 1.17 14.00 18.20 Millard 975 4.6 4.6 2.24 1.27 5.15 1.33 15.85 138.70 Morrison 1031 21.5 21.5 1.74 1.74 3.42 1.15 16.23 277.00 Murray 2715 48.2 47.7 1.41 1.36 4.56 1.13 Nanoose 3183 43.5 25.1 5.12 2.06 23.87 1.17 16.00 173.80 Nathan 3092 81.6 72.3 2.31 1.11 8.77 1.14 13.00 125.15 Nile 1962 25.3 7 11.51 1.59 31.76 1.00 11.65 59.00 Stawamus 4594 123.6 6.4 22.29 1.76 68.68 1.06 11.50 39.00 Waterloo 1082 16.8 3 6.81 2.19 9.62 1.03 14.55 76.60 Whonnock 2304 30.3 29.6 1.81 1.13 5.37 1.02 13.00 34.65 3.6 Statistical analyses Following are the results of regressions of coho fry, mean escapement and growth rate (g) on various land use groupings for entire watersheds and buffer zones. The first set of analyses was conducted to answer the question: Does the level of disturbance of a watershed affect coho fry or coho escapement? Mean coho fry density (1991-1997) was.regressed on broad categories of 1995 land use (Table 8). There is a significant positive relationship between coho fry density and percent old growth (p = 0.0015). Note that this effect is due largely to a single high observation for the Stawamus River (Fig. 11). Average coho escapement for three time periods was regressed 32 against land use (Table 8), but no significant relationships at the "table-wide" significance level were found. Table 8. Correlation coefficients (r), significance values (p), and power (pw) for regressions of mean coho fry and mean escapement on percent broad land use categories. Percent land use Coho Agriculture Urban Forested Second Old Clearcut & growth growth shrub Mean fry r 0.00 -0.60* +0.33 -0.24 +0.74* 0.00 1991-1997 P 0.84 0.02 (ns) 0.24 0.40 0.0015** 0.10 pw 0.05 0.70 0.21 0.13 0.98 0.05 Mean esc. r 0.00 +0.45* -0.35 -0.40 0.00 +0.22 1990-1997 P 0.85 0.10 0.21 0.14 0.88 0.41 pw 0.05 0.38 0.23 0.31 0.05 0.12 Mean esc. r -0.36 -0.17* +0.33 +0.14 +0.24 +0.44 1970-1985 P 0.18 0.54 0.22 0.63 0.39 0.11 pw 0.26 0.07 0.22' 0.07 0.13 0.36 Mean esc. r -0.70 -0.17* 0.00 -0.41 +0.40 +0.37 1953-1965 P 0.0113 (ns) 0.57 0.91 0.18 0.20 0.23 pw 0.80 0.08 0.05 0.26 0.24 0.21 * Land use proportions were transformed in this analysis. **Significant at the "table-wide" significance level of 0.008. (ns) - Not significant at the "table-wide" significance level. Graphic representations of regressions in this table follow (Figures 11-14). 33 0% 10% 20% 30% 40% 50% 60% 70% Agriculture 0% 20% 40% 60% 80% 100% Arcsin sqrt (Urban) 20% 30% 40% 50% 60% 70% 80% 90% 100% Forested 20% 40% 60% 80% 100% 2nd growth 0% 20% 40% 60% 80% 100% Arcsin sqrt (Old Growth) 0% 5% 10% 15% 20% 25% Clearcut and shrub 1995 % LAND USE IN WATERSHED Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Forested Mature forest, immature forest (> 10 metres) Second growth Immature forest > 10 metres Old growth Mature forest never harvested Clearcut & Clearcuts, shrub, immature forest < 10 metres shrub Figure 11. Least squares linear regression plot of mean coho fry density (1991 -1997) (square root transformed) against percent land use (broad grouping) in 1995. Line of best fit is indicated. The coefficent of determination and associated probability are presented for each relationship. 34 + u oo O K O o H o o Pi w I a 00 0% 10% 20% 30% 40% 50% 60% 70% Agriculture Forested 0% 20% 40% 60% 80% 100% Arcsin sqrt (Urban) 20%, 30%, 40% 50% 60% 70% 90% 100% 2nd growth Old growth 0% 5% 10% 15% 20% Clearcut and shrub 1995 % LAND USE IN WATERSHED Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Forested Mature forest, immature forest > 10 metres Second growth Immature forest > 10 metres Old growth Mature forest never harvested Clearcut & Clearcuts, shrub, immature forest < 10 metres shrub Figure 12. Least squares linear regression plot of mean coho escapement (1990-1997) (square root transformed) against percent land use (broad grouping) in 1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 35 0% 10% 20% 30% 40% 50% 60% 70% Agriculture 0% 10% 20% 30% 40% 50% 60% 70% Arcsin sqrt (Urban) 40% 60% Forested 100% 120% • • • — r2 = 0.02, p = 0.63 20% 30% 40% 50% 60% 70%, 80% 90% 100% 2nd growth 0%, 10% 20% 30% 40% 50"% 60% 70% Old growth 0% 2% 4% 6% 8% 10% 12% 14% 16% Clearcut and shrub 1975 % LAND USE IN WATERSHED Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Forested Mature forest, immature forest > 10 metres Second growth Immature forest > 10 metres Old growth Mature forest never harvested Clearcut & Clearcuts, shrub, immature forest < 10 metres shrub Figure 13. Least squares linear regression plot of mean coho escapement (1970-1985) (square root transformed) against percent land use (broad grouping) in 1975. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 36 10% 20% Agriculture Arcsin sqrt (Urban) 20% 40% 60% 80% 100%, Forested 20%, 30%, 40% 50% 60% 70% 2nd growth 60% 80% Old growth 0% 20% 40% 60% 80% 100% Clearcut and shrub 1955 % LAND USE IN WATERSHED Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Forested Mature forest, immature forest > 10 metres Second growth Immature forest > 10 metres Old growth Mature forest never harvested Clearcut & Clearcuts, shrub, immature forest < 10 metres shrub Figure 14. Least squares linear regression plote of mean escapement (1953-1965) (square root transformed) against percent land use (broad grouping) in 1955. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 37 The following analyses addressed the second question: Does land use change over time in whole watersheds or in riparian zones beside streams, affect coho escapement or fry abundance? Coho fry and coho population growth rate (g) were regressed against land use change from 1975 to 1995. For the first analysis, land use was grouped into two broad categories: agriculture (including crop agriculture, pasture, rural farms and farm buildings), and urban (including urban, industrial, rural residential and roads). No significant relationships were found (Table 9). Table 9. Correlation coefficient (r), significance values (p) and power (pw) for regressions of coho fry and population growth rate on percent change of broad land use categories for post-1975 (1975 to 1995). Percent land use change post-1975 Coho Agriculture Urban Mean fry r +0.33 -0.54 1991-1997 P 0.24 0.04 (ns) pw 0.21 0.59 Growth rate (g) r -0.32 +0.41 P 0.24 0.13 pw 0.21 0.32 (ns) - Not significant at the "table-wide" alpha value of 0.025. Graphic representations of regressions in this table follow (Figure 15). 38 % Watershed land use change 1975-1995 Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Figure 15. Least squares linear regression plot of mean fry (1991-1997) and population growth rate against percent land use (agriculture and urban broad land use groupings) for the post-1975 time period (1975-1995). Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 39 Regressions of fry and population growth rate against impervious land use change from 1975 to 1995 do not reveal any significant relationships (Table 10). This indicates that growth in urbanized land area cannot explain differences in population trend among the study watersheds. Table 10. Correlation coefficient (r), significance values (p), and power (pw) for regressions of coho fry and population growth rate on percent change of impervious land use categories for the post-1975 time period (1975 to 1995). Percent change impervious land use 1975-1995 Coho High Medium high Medium Medium low Low Very low Roads Fry r -0.47* -0.52 0.00 +0.10 +0.56 -0.26* -0.17 P 0.11 0.05 (ns) 0.10 0.78 0.03 (ns) 0.35 0.53 pw 0.36 0.53 0.05 0.06 0.61 0.15 0.09 (g) r +0.41 +0.10 -0.49 +0.42 -0.14 -0.26 +0.28 P 0.12 0.71 0.06 0.12 0.66 0.32 0.30 pw 0.34 0.06 . 0.47 0.34 0.07 0.16 0.17 !i\Land use proportions were transformed in this analysis, (ns) - Not significant at the "table-wide" alpha level of 0.007. i. Graphic representations of regressions in this table follow (Figures 16-17). 40 3.00 2.00 1.00 0.00 0% 10% 2 0 % 3 0 % 4 0 % A r c s i n sqrt ( H I G H ) 5 0 % 3.00 2.00 1.00 0.00 - 2 % 0 % 2 % 4 % M E D I U M - H I G H + in C/3 O O U 3.00 2.00 1.00 0.00 r2 = 0.00, p = 0.10 ^ < • • • • -6% - 4 % - 2 % 0% M E D I U M 2 % 3.00 2.00 1.00 0.00 ^ = 0.01^ = 0.78 < • • • • 4 % - 1 5 % -10% - 5 % 0 % 5% 10% 15% 2 0 % M E D I U M - L O W 3.00 r2 = 0.31, p = 0.03 3.00 • 2.00 • • • 2.00 1.00 * — : • • • • 1.00 0.00 0.00 - 2 5 % -20% - 1 5 % -10% - 5 % 0% 5% 10% L O W -50% -40% - 3 0 % - 2 0 % -10% 0% A r c s i n sqrt ( V E R Y L O W ) 3.00 2.00 1.00 0.00 < r2 = 0.03, p = 0.53 • *• • f • • • * . • Impervious land use groups - 1 % - 1 % 0 % 1% 1% 2 % 2 % R O A D S High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % Watershed land use change 1975-1995 Figure 16. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 41 2 0 % 3 0 % 4 0 % Arcsin sqrt (HIGH) 3 0 . 1 0 0 . 0 5 0 . 0 0 u - 0 . 0 5 - 0 . 1 0 O - 0 . 1 5 - 0 . 2 0 o 0 . 1 0 do 0 . 0 5 0 . 0 0 ho - 0 . 0 5 o - 0 . 1 0 U - 0 . 1 5 - 0 . 2 0 r2 = 0.24, p = 0.06 • •6% - 4 % - 2 % ' 0 % M E D I U M 2 % 4 % r2 = 0.02, p = 0.6^ * • T 1 < • • • * - 2 5 % - 2 0 % - 1 5 % - 1 0 % - 5 % 0 % 5 % 1 0 % L O W 0 . 1 0 0 . 0 5 0 . 0 0 - 0 . 0 5 - 0 . 1 0 - 0 . 1 5 - 0 . 2 0 4 r2 = 0.08, p = 0.30 • • • • • -1% -1% 0 % 1% R O A D S 1 % 2 % 2 % 4 r2 = 0.01, p = 0.71 <> • • 1 A < i - 2 % 0 % 2 % 4 % M E D I U M - H I G H 6% 8% 0 . 1 0 0 . 0 5 0 . 0 0 - 0 . 0 5 - 0 . 1 0 - 0 . 1 5 - 0 . 2 0 r2 = 0.18,p = 0._ 2 • - A * • 1 ^^^^^^^^^ - 1 5 % - 1 0 % - 5 % 0 % 5 % 1 0 % M E D I U M - L O W 1 5 % 2 0 % 0 . 1 0 0 . 0 5 0 . 0 0 - 0 . 0 5 - 0 . 1 0 - 0 . 1 5 - 0 . 2 0 r2 = 0.02, p = 0.60 - 5 0 % - 4 0 % - 3 0 % - 2 0 % - 1 0 % 0 % Arcsin sqrt ( V E R Y L O W ) Impervious land use groups High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % Watershed land use change 1975-1995 Figure 17. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 4 2 No significant relationships were found when mean fry abundance and population growth rate (g) were regressed against impervious land use for three buffer widths: 100, 250, and 500 metres (Table 11). This result indicates that development effects in the immediate proximity of streams cannot explain differences in population trend that cannot be explained by broader changes at watershed scale. 43 Table 11. Correlation coefficients (r), significance values (p), and power (pw) for regressions of coho fry and population growth rate on percent change of impervious land use categories for 1975 to 1995 for three buffer widths. Percent change impervious land use 1975-1995 Coho Buffer Width (m) High Medium High Medium Medium Low Low Very ' Low Roads Fry 100 m r P pw -0.66* 0.0094(ns) 0.59 +0.14 0.62 0.08 0.00 0.99 0.05 -0.35 0.21 0.24 +0.44 0.10 0.37 -0.20 0.47 0.11 +0.1 0.72 0.06 <8) 100 m r P pw +0.2 0.47 0.11 0.00 0.94 0.05 -0.47 0.07 0.45 +0.24 0.40 0.13 +0.32 0.25 0.20 -0.45 0.10 0.38 +0.40 0.14 0.31 Fry 250 m r P pw -0.68* 0.0113(ns) 0.79 -0.10 0.72 0.06 0.00 0.98 0.05 -0.28 0.30 0.42 +0.47 0.08 0.42 -0.22* 0.44 0.11 +0.10 0.80 0.06 <s) 250 m r P pw +0.22* 0.45 0.11 +0.10 0.78 0.06 -0.45 0.10 0.18 +0.30 0.28 0.18 +0.10 0.72 0.06 -0.22* 0.44 0.11 +0.57 0.24 0.20 Fry 500 m r P pw -0.47* 0.09 0.40 -0.26 0.32 0.16 0.00 0.82 0.06 -0.24 0.36 0.14 +0.52 0.05 0.52 -0.26* 0.36 0.14 +0.14 0.63 0.07 fc) 500 m r P pw +0.39* 0.17 0.27 +0.10 0.75 0.06 -0.41 . 0.13 0.32 +0.22 0.40 0.13 0.00 0.91 0.05 -0.17* 0.52 0.09 +0.26 0.33 0.15 * Land use proportions were transformed in this analysis. (ns) - Not significant at the "table-wide" significant value of 0.002. i. Graphic representations of regressions in this table follow (Figures 18-23). 44 10% 15% 2 0 % 2 5 % . 5o/ 0 A r c s i n sqrt ( H I G H ) 0% 5% 10% 15% 2 0 % M E D I U M - H I G H 3.00 2.00 1.00 0.00 r2 = 0.00, p = 0.99 -< < • • -10% - 5 % 0 % M E D I U M 5% -20% -10% 0 % 10% 2 0 % M E D I U M - L O W 0.00 r2 = 0.04, p= 0.47 " • > •; • -40% - 3 0 % -20% -10% L O W 0 % 10% -30% -20% - 1 0 % 0 % V E R Y L O W 10% 2 0 % 3.00 2.00 1.00 0.00 - r2 = 0.01, p = 0.72 • • - 1 % 0 % 1% 1% R O A D S 2 % 2 % Impervious land use groups High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % 100m buffer land use change 1975-1995 Figure 18. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) in a 100 metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 45 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 • * * r2 = 0.23, p = 0.07 ' * • -10% -5% 0% M E D I U M 5% 4. = 0.00, p = 0.94 • * * t -5% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 0% 5% 10% M E D I U M - H I G H 15% 20% r2 = 0.06, p = 0.40 1 • * -* • • • -20% -10% 0% 10% M E D I U M - L O W 20% 30% 0.10 0.05 0.00 -0.05 -0.10 -0.20 r2 = 0.10, p = 0.25 •-. • • •• • -30% -20% -10% L O W 0% 10% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 -30% r2 = 0.20, p = 0.10 < • -20% -10% 0% V E R Y L O W 10% 20% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 -r2 = 0.16, p = 0.14 • . * * • • • • -1% 0% 1% 1% R O A D S 2% 2% Impervious land use groups High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % 100m buffer land use change 1975-1995 Figure 19. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) in a 100-metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 46 3.00 2.00 1.00 0.00 3.00 -<> 2.00 1.00 • 0.00 0% 5% 10% 15% 20% 25% 30% -3% HIGH -1% 1% 3% 5% 7% MEDIUM-HIGH 3.00 2.00 1.00 0.00 3.00 -• 2.00 -• V 1.00 0.00 1 8% -3% 2% MEDIUM 7% -10% -5% 0% 5% 10% 15% 20% MEDIUM-LOW 3.00 r2 = 0.22, p = 0.08 - 3.00 • 2.00 • • 2.00 1.00 ; 1.00 0.00 1 1 ' — i — 0.00 -25% -15% -5% 5% -60% -50% -40% -30% -20% -10% 0% VERY LOW 3.00 - r2 = 0.01, p = 0.80 • 2.00 1.00 0.00 Impervious land use groups -1% 0% 1% 2% ROADS 3% High Medium high Medium Medium low Low Very low 4% Roads Industrial, urban, farm buildings Rural residential and rural farms Agriculture, pasture, grass parks, gravel/soil Shrub Second growth Old growth All road types % 250m buffer land use change 1975-1995 Figure 20. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) in a 250 metre buffer zone for the post-1975 time period. Line of best fit' indicated. The coefficient of determination and associated probability are presented for each relationship. 47 r2 = 0.01, p = 0.78 • • , i - 3 % - 1 % 1% 3 % 5% 7% M E D I U M - H I G H 9% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 • • • ^ r2 = 0.20, p = 0.10 * I -10% - 5 % 0% M E D I U M 5% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 # r2 = 0.09, p = 0.28 • 1 * • • 4 -10% - 5 % 0 % 5% 10% M E D I U M - L O W 15% 2 0 % 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 r2 = 0.01, p = 0.72, * * •* • • • • • • - 2 5 % - 1 5 % - 5 % L O W 5% 15% - 4 0 % - 3 0 % -20% V E R Y L O W 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 + r2 = 0.10, p = 0.24 • 1 • A — • — - — ' • 4 • . - 1 % 0% 1% 2 % R O A D S 3% 4 % Impervious land use groups High Medium high Medium Medium low Low Very low Roads Industrial, urban, farm buildings Rural residential and rural farms Agriculture, pasture, grass parks, gravel/soil Shrub Second growth Old growth All road types % 250m buffer land use change 1975-1995 Figure 21. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) in a 250-metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 48 3.00 2.00 1.00 0.00 3.00 r2 = 0.07, p = 0.32 < > 2.00 i • » • 1.00 . 0.00 0 % 10% 2 0 % 3 0 % 4 0 % 5 0 % - 1 % A r c s i n sqrt ( H I G H ) 1% 3 % 5% M E D I U M - H I G H 7% 9% + m • m d o o O 3.00 2.00 1.00 0.00 -1 3.00 2.00 1.00 0.00 r 2 < 0.01 p = o 8 2 -4 < > • • • 1 0% - 2 5 % 3.00 2.00 1.00 0.00 0.00 - 5 % 0% M E D I U M 5% -10% - 5 % 0% 5% 10% 15% 2 0 % M E D I U M - L O W r2 = 0.27, p = 0.05 " • • • <fc——r 1 1 \ ! - 1 5 % • - 5 % L O W 5% - r2 = 0.02, p = 0.63 • -• • • -60% -50% - 4 0 % - 3 0 % - 2 0 % - 1 0 % 0 % A r c s i n sqrt ( V E R Y L O W ) Impervious land use groups - 1 % 0 % 1% 2 % R O A D S 3 % 4 % High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % 500m buffer land use change 1975-1995 Figure 22. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against percent land use (impervious land use groupings) in a 500 metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 49 3 -(-> ? o o • I-H > &. o o 43 o U r2 = 0.01, p = 0.75 t 0% 10% 20% 30% Arcsin sqrt (HIGH) 40% 50% -1% 1% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 • • 4 r2 = 0.17, p = 0.13 -10% -5% 0% M E D I U M 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 -30% -20% -10% L O W 0% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 -1% 0% 1% 2% R O A D S 3% 5% r 2< 0.01 ,p = 0.91 * i • • • 10% O r2 = 0.07, p = 0.33 • 1 —* • * ' -< • • 4% 3% 5% M E D I U M - H I G H 7 % 9% -10% -5% 0% • 5% 10% M E D I U M - L O W 15% 20% 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 • r2 = 0.03, p = 0.52 o " » 1 • I -60% -50% -40% -30% -20% -10% 0% Arcsin sqrt ( V E R Y LOW) Impervious land use groups High Medium high Medium Medium low Low Very low Roads Industrial, urban, farm buildings Rural residential and rural farms Agriculture, pasture, grass parks, gravel/soil Shrub Second growth Old growth All road types % 500m buffer land use change 1975-1995 Figure 23. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) in a 500-metre buffer zone for the post-1975 time period. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 50 Coho population growth rate (g) and coho fry were regressed against land use change for the whole time period from 1955 to 1995. No significant relationships were found between population growth rate (g) or fry and agriculture or urban land use groupings (Table 12) although a negative association between increasing urban development and fry density was close to being statistically significant. Table 12. Correlation coefficients (r) and significance values (p) for regressions of coho fry and population growth rate on percent change of broad land use categories for 1955 to 1995. Percent broad land use chan ie 1955-1995 Coho Agriculture Urban Mean fry r +0.24 -0.54* 1991-1997 P 0.37 0.038 (ns) pw 0.14 0.57 Growth rate (g) r +0.17 +0.39* P 0.55 0.16 pw 0.09 0.28 * Land use proportions were transformed in this analysis. (ns) - Not significant at the "table-wide" significant value of 0.025. i. Graphic representations of regressions in this table follows (Figure 24). 51 0 - 20% -10% 0% 10% 2 0 % 0% 10% 2 0 % 3 0 % 4 0 % 5 0 % 6 0 % gn A G R I C U L T U R E A r c s i n sqrt ( U R B A N ) CU % Watershed land use change 1955-1995 Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Figure 24. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) and population growth rate against percent land use (agriculture and urban broad land use groupings) for the entire time period, 1955 to 1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 52 Regressions of fry and population growth rate against impervious land use change from 1955 to 1995 do not reveal any significant relationships (Table 13). Table 13. Correlation coefficients (r) and significance values (p), and power (pw) for regressions of coho fry and population growth rate on percent change of impervious land use categories for the whole time period (1955 to 1995). Percent chan ge impervious land use 1955-1995 Medium Medium Very Coho High High Medium Low Low Low Roads r -0.37* -0.14 +0.20 0.00 +0.26 -0.48 -0.30 Fry P 0.18 0.65 0.50 0.98 0.34 0.07 0.27 pw 0.26 0.07 0.10 0.05 0.15 0.45 0.19 r +0.17* +0.48 -0.10 +0.14 -0.35 +0.33 +0.33 (z) P 0.55 0.07 0.68 0.64 0.20 0.23 0.23 pw 0.09 0.45 0.07 0.07 0.24 0.22 0.22 * Land use proportions were transformed in this analysis. i. Graphic representations of regressions in this table follow (Figures 25-26). 53 3.00 2.00 1.00 0.00 3.00 2.00 1.00 0.00 + cr on on a CD O xi o U 0% 10% 20% 30% 40% 50% 60% 0% 5% 10% 15% 20% 25% Arcsin sqrt (HIGH) MEDIUM-HIGH 3.00 2.00 1.00 3.00 2.00 1.00 0.00 I 1 1 1 1 1 1 0.00 -20% -15% -10% -5% 0% 5% 10% -80% -60% -40% -20% 0% 20% MEDIUM MEDIUM-LOW 3.00 2.00 1.00 0.00 + r2 = 0.07, p = 0.34 O • 3.00 2.00 1.00 0.00 -30% -10% 10% 30% 50% 70% "30% -25% -20% -15% -10% -5% 0% 5% LOW VERY LOW 3.00 2.00 1.00 0.00 1% 2% ROADS Impervious land use groups High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % Watershed land use change 1955-1995 Figure 25. Least squares linear regression plot of mean coho fry against percent land use (impervious land use groupings) for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 54 0% 10% 20% 30% 40% 50% 60% A r c s i n s q r t ( H I G H ) 0% 5% 10% 15% 20% 25% M E D I U M - H I G H 3 CD s-% O >-OD o • r—I +-> a 3 ft o ft o o O 0.10 0.05 0.00 -0.05 -0.10 -A 0.10 0.05 0.00 -0.05 • r2 = 0.01, p = 0.68 * 1 - ' i , < • 0.10 0.05 0.00 -0.05 -0.10 • r2 = 0.02, p = 0.64 • « * • • • • 9 -0.10 r2 = 0.12, p = 0.20 • • r • 1 1 1 • • -0.10 20% -15% -10% -5% 0% 5% 10%. -80% -60% -40% -20% 0% 20% M E D I U M M E D I U M - L O W -40% -20% 0% 20% 40% 60% 80% -30% -25% -20% -15% -10% -5% 0% V E R Y L O W Impervious land use groups 1% 2% R O A D S High Industrial, urban, farm buildings Medium high Rural residential and rural farms Medium Agriculture, pasture, grass parks, gravel/soil Medium low Shrub Low Second growth Very low Old growth Roads All road types % Watershed land use change 1955-1995 Figure 26. Least squares linear regression plot of population growth rate against percent land use (impervious land use groupings) for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 55 No significant relationships were found when mean fry abundance and coho growth rate (g) were regressed against agriculture and urban land use categories (1955 to 1995) for three buffer widths: 100, 250, and 500 metres (Table 14). Estimated effects on r were generally positive, i.e. opposite in sign from the expectation of simple hypotheses about deleterious effects of development. 56 Table 14. Correlation coefficients (r) and significance values (p) for regressions of coho fry and population growth rate on percent change of agriculture and urban land use categories for 1955 to 1995 for three buffer widths. % Land use change 1955-1995 Coho Buffer Width (m) Agriculture Urban 100 m r +0.32 -0.10 Fry P 0.25 0.71 pw 0.20 0.06 (g) 100 m r +0.24 +0.32 P 0.39 0.23 pw 0.13 0.21 Fry r +0.33 -0.54 250 m P 0.23 0.04 (ns) pw 0.21 0.57 r +0.28 +0.57 (g) 250 m P 0.28 0.02 (ns) pw 0.18 0.66 r +0.32 -0.50 Fry 500 m P 0.25 0.05 (ns) pw 0.20 0.50 (g) r +0.35 +0.51 500 m P 0.21 0.05 (ns) pw 0.23 0.52 (ns) - Not significant at the "table-wide" significance level of 0.00625. ' i. Graphic representations of regressions in this table follow (Figures 27-29). 57 0.00 3.00 2.00 1.00 0.00 -40% -30% -20% -10% 0% 10% 20% 0% AGRICULTURE 5% 10% 15% URBAN 20% 3 0.10 tS s- 0.05 0.00 O & -0.05 s o • i—i -0.10 P H O OH r 2 = 0.06, p = 0.39 • • 4 • < -| • 40% -20% 0% AGRICULTURE 20% 0% 5% 10% 15% URBAN % 100m buffer land use change 1955-1995 20% Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Figure 27. Least squares linear regression plot of mean fry (1991-1997) and population growth rate against percent land use (agriculture and urban groupings) in a 100 metre buffer zone for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 58 "1 + >< • Di &-h Oi O C/3 3.00 2.00 0.00 - 4 0 % - 3 0 % - 2 0 % - 1 0 % 0 % a g r i c u l t u r e 10% 2 0 % 0 % 5% 10% U R B A N 15% ^ 0.10 CU 03 S-H 0.05 0.00 O -0.05 e -o.io o r 2 = 0.09, p = 0.28 • <> • < • i • 4 0 % - 3 0 % - 2 0 % - 1 0 % 0 % a g r i c u l t u r e 3 P H O 0.10 0.05 0.00 -0.05 -0.10 r 2 = 0.33, p = 0.02 • ^ 10% 2 0 % 0 % 5 % 10% u r b a n % 250m buffer land use change 1955-1995 15% Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Figure 28. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) and population growth rate against percent land use (agriculture and urban groupings) in a 250 metre buffer zone for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 59 3.00 0.00 -30% 10% 10% AGRICULTURE 3.00 2.00 1.00 0.00 30% o% 5% 10% 15% 20% U R B A N 3 <D H—> cd 43 O OX) a o H—> P H O fin 0.10 0.05 0.00 -0.05 -0.10 r 2 = 0.12, p = 0.21 • * • 1 < ' — • « ^—— r • • 0.10 -30% -20% -10% 0% 10% 20% 30% A G R I C U L T U R E 0% 5% 10% 15% URBAN % 500m buffer land use change 1955-1995 20% Broad land use groups Urban Urban, rural residential, industrial, roads Agriculture Agriculture, pasture, rural farms, farm buildings Figure 29. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) and population growth rate against percent land use (agriculture and urban groupings) in a 500 metre buffer zone for the entire time period, 1955-1995. Line of best fit is indicated. The coefficient of detenriination and associated probability are presented for each relationship. 60 The third question addressed in the following analyses was: do physical stream attributes affect coho fry or population growth rates? No significant relationships were found in regressions of mean fry density and mean escapement on several stream attributes (mean gradient,' mean sinuosity, mean temperature and mean conductivity) - (Table 15). Surprisingly, the estimated effects of increasing conductivity and temperature were weakly negative, while the effect of increasing gradient was weakly positive. Table 15. Correlation coefficients (r) and significance values (p), and power (pw) for regressions of coho fry density and mean escapement on physical stream characteristics. Stream characteristics Coho Mean gradient (%) Mean sinuosity Mean temp. (°C)** Conductivity** Fry r P pw +0.39 0.17 0.27 +0.22 0.44 0.11 -0.63 0.02 (ns) ' 0.74 -0.52 0.06 0.49 Mean Esc. r P pw +0.10 0.69 0.07 0.52 0.06 0.48 n/a n/a (ns) - Not significant at the "table-wide" significance level of 0.0125. **Temperature and conductivity were taken at the coho fry sites. i. Graphic representations of regressions in this table follow (Figures 30 and 31). 61 55 w Q > oi P H O o u H O O Pi w D a r 2 = 0.15, p = 0.17 1.5 2 2.5 3 Mean gradient (%) r 2 = 0.40, p = 0.02 II 12 13 14 15 16 Mean temperature (C) r 2 = 0.05, p = 0.44 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 Mean sinuosity r 2 = 0.27, p = 0.06 11 111 211 311 411 Conductivity (us) Figure 30. Least squares linear regression plot of mean fry (1991-1997) (square root transformed) against stream physical attributes: mean gradient, mean sinuosity, mean temperature, and conductivity. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 9 ^ I w o w w p o oo r 2 = 0.01, p = 0.69 1.5 2 2.5 Mean gradient (%) r 2 = 0.27, p = 0 • 06 I 1.05 1.1 1.15 1.2 1.25 1.3 1.35 Mean sinuosity Figure 31. Least squares linear regression plot of mean escapement (1990-1997) (square root transformed) against stream physical attributes: mean gradient and mean sinuosity. Line of best fit is indicated. The coefficient of determination and associated probability are presented for each relationship. 62 C H A P T E R IV - D I S C U S S I O N 4.1 Brief summary of results All but two of the fifteen study watersheds showed pronounced declines in escapement over the second half of the time period (~ 1975-1995), Coho growth rate (g) did not correspond to land use changes for the whole or post-1975 time series. Current fry estimates showed a weak negative relationship with urban land use change in the 1955-1995 and post-1975 time series. Proportion land use in buffer areas did not correspond differently than land use in whole watersheds to differences in spatial densities of fry or to population growth (g). Of the comparisons made between land use and coho fry in 1995 two showed fairly strong relationships. Coho fry showed a positive significant relationship with proportion old growth forest in a watershed, and a strong negative relationship with proportion urbanization. Mean escapement showed a weak negative relationship with agriculture in 1955. Coho fry density showed a weak negative relationship with temperature. 4.2 Land use Land use in my study watersheds was 68 percent forest regrowth, 22 percent urban and agricultural development and 11 percent undisturbed. I found a slight discrepancy between land use composition in my study watersheds and the whole of the Georgia and Lower Fraser basins, reflecting the coastal location of study watersheds on Vancouver Island, and the lowland location of Fraser Basin study watersheds. Alpine and glacier land use was under-represented in both basins and coniferous second growth and old growth was under-represented in the Fraser Basin. The reason for the discrepancy is that the location of study watersheds was determined by specific criteria. I selected watersheds for which coho fry data, escapement data, and orthophotos were available. Also, only wild coho streams were chosen (as far as this is possible with the information available on historical hatchery fish releases). Watersheds in coastal regions and those close to the Fraser River typically met these criteria. There was little difference between land use in buffer strips along the length of streams and land use in entire watersheds. I expected development to decrease from beside the rivers or creeks outward. However, development in Vancouver Island watersheds decreased from the coast inland, and in the Lower Fraser Basin from the Fraser River back. This is because the creeks are relatively small and the watersheds well developed. 4.3 Coho fry Although coho fry data were not intended to reveal spatial or temporal trends, I considered the data adequate for comparison between streams because they are likely accurate relative abundance estimates. This is evident in the variability of fry abundance estimates between streams. If each site surveyed had maximum densities of coho fry, one would expect little variation between streams. Furthermore, I believe the methods used to collect fry led to sound estimates for the sites sampled. Cowx (1983) explains that the best multiple-pass estimates of fry density are made when a large proportion of the actual population is caught. This appears to be true for the coho surveys, since about 90% of all the fish caught in two- or three- pass surveys were usually caught in the first pass. 4.4 Comparison of coho fry and escapement to land use Following are four possible reasons why primarily no significant relationships or weak relationships between fry and land use and no significant relationships between escapement and land use were detected: 1. Coho are resilient to stream changes caused by development and some changes may enhance stream habitat used by coho. 64 2. Development was well-established in the study watersheds before the 1950s and further development negligible in comparison. 3. There may be other land use groupings not considered in this study that are related to coho abundance. 4. The effect size is too small to detect. 4.4.1. Coho resilience to change Coho may be a "weedy" species that is resilient to change and opportunistic; taking advantage of habitat created by land use practices. The ability of coho salmon to respond to habitat changes and new habitat opportunities depends largely on their ability to disperse. Because of large natural annual variation in stream discharge (largely due to freshets that may be unpredictable) coho have developed good dispersal capabilities. Coho juveniles move in response to streamflow to find suitable habitat. They move both because of seasonal timing and day to day fluctuations in stream flow (Hartman et al. 1982). Juvenile coho generally move upstream in response to decreased streamflow and downstream in response to increased streamflow (Shirvell 1994). They have been known to move 200-400 metres upstream to overwinter in small tributaries, and as far as 38 km downstream (Groot and Margolis 1991). Giannico and Healey (1998) report that when temperatures decreased from 4.9°C to 3.0°C in a laboratory setting, coho moved less. When stream temperatures are warmer, coho fry move seaward earlier in the year. (Hartman et al. 1982). Emigration begins earlier with logging - i.e. warmer temperaturs shift emigration earlier. It could be that in the wild increased stream temperatures due to clearing beside streams promotes coho movement and their ability to find good foraging sites. Coho dispersal occurs mainly in the fall in search of overwintering habitat. They overwinter in deep pools, undercut banks, off-channel ponds, wetlands, side channels and low gradient 65 tributaries (McMahon and Holtby 1992; Nichelson et al. 1992a). This habitat tends to be slack water lotic habitat. Nichelson et al. (1992a) found that beaver ponds support higher numbers of fish than other pool types, perhaps because beavers maintain pool depth. There are more beaver ponds currently in the Squamish River watershed than have been for several decades (personal observation). This is because logged watersheds have more deciduous trees -particularly with natural unplanted regrowth - which are more palatable to beavers than conifers. This may be a widespread phenomenon in the Georgia and Lower Fraser basins. Temperature increases due to forest harvesting may have contributed to the lack of or weak correlations seen between coho and land use. If forest is logged to the edge of streams, loss of canopy resulting in direct light increases stream temperature. Alternatively, increased temperatures can occur as a result of higher radiant energy storage in the soil of burned clearcuts, which transfers to groundwater and then to intragravel water on streambeds (Scrivener and Andersen 1984). Increased temperature appears to have a positive effect on coho growth. Holtby (1988) reports that after 41% of the Carnation Creek watershed was logged, stream temperatures increased 0.7°C in winter and 3.2°C in summer. As a result, coho salmon emerged earlier thereby adding six weeks to their summer growing season, which resulted in larger fingerlings than before logging. Yearling smolt numbers doubled and two-year-old smolt numbers decreased. Scrivener and Andersen (1984) found that fry emerged earlier and moved seaward earlier after logging than before logging. This observation is probably attributable to increased temperatures, although earlier migration of spawning females may also be a factor. Increased temperature promotes stream productivity and thus food availability to coho. If food availability is limiting to coho productivity, then increased growth and survival of fry would be expected. Intraspecific competition is reduced which positively affects growth. Smolts are influenced by growth the previous summer, and larger average size of coho in streams with poor quality rearing may confer improved ability to survive the following winter (Fransen et al. 1993). o 66 Drift (floating detritus) increases as productivity of a stream increases and coho feed primarily on terrestrial and aquatic invertebrates in drift. They position themselves in slow-moving water (pools) close to fast-moving water (riffles) where food delivery occurs at a high rate. Drift declines over the summer and coho juveniles head for areas of good cover from predation and low energy expenditure. Stream productivity increases as a result of nutrient input from external sources such as farms. If nutrients are limiting, then small increments of nutrient concentrations can result in large relative increases in periphyton (Richardson and Perrin 1992). Periphyton growth may in turn promote growth of invertebrate populations, and even change the community structure of stream invertebrates. The invertebrate community shifts from predominantly "shredders" -such as stoneflies and crayfish that tear apart large coarse particulate organic matter (CPOM) to grazers such as larva of midges or caddisflies. Community structures altered by nutrient concentrations may be more suitable to rearing coho whose diet consists of terrestrial insects and aquatic invertebrates (i.e. winged dipterans, chironomids). The Salmon River in the Lower Fraser Valley is an example of a high-density coho stream running through agricultural land. The increased primary productivity in these creeks due to agricultural by-product inputs (i .e. phosphates, manure) may actually promote colonization by coho. Coho may adjust to changes in their environment because they have elastic behaviour. Indeed, the behavioural characteristics of coho may give the fish a wider range of habitats to exploit in low elevation, low-gradient stream situations. For example, if suitable rearing area is small, then an individual may hold an area, repelling others; however sometimes coho form groups in pools with the large ones in front defending territory (intercepting drift) and smaller ones milling about behind not defending a territory (Groot and Margolis 1991). It appears that coho are quite versatile in their selection of physical habitat variables, and have a fairly high tolerance range for stream conditions. Coho's preferred depth is greater than 67 or equal to about 18 cm, velocity range is 30-91 cm/s, and they reside in a range of substrate sizes (1.3 - 10.2 cm diameter) (Bjornn and Reiser 1991). The temperature range at which they migrate is 7.2 to 15.6°C (Bjornn and Reiser 1991). Because a fairly wide range of physical stream attributes are acceptable to coho they may readily exploit new habitat such as irrigation ditches or side channels created by land use practices. 4.4.2 Historical land use Development was well-established in the early part of this century and the Georgia Basin and Lower Fraser Valley developed or cleared for agriculture and urban development. Moreover, early land use practices may have been more detrimental to streams than those today Recent development is tmlikely to have accelerated enough to cause the massive declines of coho in the past two decades. The entire Georgia Basin, including the area adjacent to Vancouver was developed by Europeans settling in the early 1800s. Timber harvesting and mining in the Georgia Basin was well underway by 1870 and the evolution of forestry was spurred at the turn of the century by some major events (Mulholland, 1995). First, the transcontinental railway to Vancouver was completed in 1886, allowing Vancouver to supply timber to settlers in the prairies. Second, the San Francisco earthquake of 1906 resulted in Vancouver Island becoming the major provider of lumber for rebuilding the city. Mulholland (1995) asserts that by 1954 about 50% of Vancouver Island's low elevation forest (<300m) had been harvested and by 1972 only 37% of original low elevation forest cover remained. After 1972 over 50% of logging occurred on high elevation land with steep slopes. It is difficult to ascertain the impacts on fish populations of historical land use because of the lack of localized, quantified land use and fish abundance information. The impact of logging since the 1970's may be less than before since practices such as "splash-damming" and log flumes that were common in the past have been discontinued. Logs stored in lakes behind temporary dams would certainly have caused major scouring of streambeds when 68 dams were blasted and logs transported downstream. Streams may take 30 to 100 years to recover from such disturbance. Historically, land use has been notoriously harmful to streams with a blatant disregard for fish and fish habitat. Early land use practices such as mining began a destructive phase in the mid-19th century. Olin (1996) reviewed historical land use practices and impacts on salmon. He postulated that placer mining, which used hydraulic cannons for dredging streams, and washing sediments to extract gold nuggets, caused massive erosion and sedimentation, destroying hundreds of miles of tributary streams in the Sierra Nevada foothills. In Meggs' (1991) historical account of the B.C. salmon fishery, he reports a flood of American miners in the 1850's. Mining gold in Fraser River tributaries involved cutting trees, diverting streams, and digging gravel through streams. Trenches several kilometres long were built to drain lakes and ponds into sluices and down to the riverbed. In the Kootenay region, traces of silver mines built at the turn of the century are still evident in the area (personal observation). Hundreds of silver mines were functioning during that period (Harris, 1997). On the mainland, Boyle et al. (1997) found initial logging and settling in the Lower Fraser Basin was completed by about 1930. Much of the area had been diked and urbanization and agriculture had spread over the delta and lowlands of the Fraser River by 1930. Between Langley and the Fraser River about 50% of the area had been cleared for agriculture. Richmond and Vancouver were already urbanized. Development has certainly continued into the latter part of the century and population growth is high in the Georgia Basin (6-7%, Mulholland 1995). However, it doesn't appear that coastal development in the last two decades has been intense enough to create major habitat losses for coho salmon. Coastal land use practices may not have as intense an effect on streams as elsewhere in the province. There was a weak negative correlation in my study between coho escapement and agriculture. Bradford's (1999) study of the Thompson River system showed a significant negative correlation between agriculture and coho salmon abundance. There may be a stronger effect size in the Thompson system because of heavier disturbance and cattle grazing to the edge of streams. The Georgia and Lower Fraser basins' agricultural practices include cranberries and raspberries, pasture, and horse ranches as well as emu, sheep and llama farms. Grazing is not particularly intensive, and many of my study streams were lined with shrub and deciduous trees (personal observation). 4.4.3. Other land use types not considered in this study There may be other land use types potentially affecting coho that I did not use in this study. One is intensity of agriculture. Apparently, farmland use has become more intensive in recent years. According to Moore (1990), annual tillage crops and close grown crops (grains) increased by 24%, mostly in Delta, Surrey, Abbotsford and Chilliwack. Berry production increased 58% in Pitt Meadows, East Richmond, and Abbotsford. These new crops generally replace forage crop production and grazing on improved grassland. I could not distinguish crop type on my aerial photos. I was also unable to evaluate the influence on streams from small land use sources that may have large impacts. For example, poultry and livestock farms are scattered throughout the Lower Fraser Basin watersheds resulting in manure stockpiling that may create soil and water pollution (Moore, 1990). Some types of pollution may well exceed the ability of freshwater systems to dilute them and to assuage negative effects. Wetlands were not a category in my study, because I could not accurately distinguish boundaries between wetland and surrounding shrub or second growth forest. Brown et al. (1996) examined wetland losses in their study of land use change in Black Creek watershed between 1976 and 1994. In 1976 Hamilton (1978 in Brown et al. 1996) estimated that 17.4% of the watershed was wetland before development, 42% of which had been reclaimed by 1976. An '70 additional 5-8% loss occurred by 1994. However, the losses of wetlands probably cannot account for the loss of coho salmon from Black Creek. Municipal wastewater input and channelization was investigated by Schlosser (1982). He studied two stream systems in east central Illinois and found a shift from dominantly benthic insectivores and insectivore-piscivore fish species to generalized insectivores, omnivores and herbivore-detritivores from a natural to a modified channel. Apparently this shift in trophic guilds is indicative of similar changes in large river fish communities in the Midwestern United States. Interestingly, both watersheds had similar dominant land use cover: 80% and 90% row crop agriculture respectively. In a study of Lake Simcoe in Ontario, Evans et al. (1996) developed a case history of changes in the watershed and subsequent water quality and fish changes. Although the watershed was transformed into agricultural land before 1900, 70% of the human population of the watershed accrued during the last 30 years. Phosphorus loading into the lake had been enormous, and the authors linked this loading with declining dissolved oxygen concentrations to lethal thresholds for the coldwater lake trout, whitefish and herring. I did not include road crossings in my study because roads are hidden by vegetation (forest canopy) on aerial photos. Warren and Pardew (1998) compared different kinds of road crossings to fish movement of 21 species. They found culverts had an order of magnitude less fish movement than open-box and ford crossings, no movement at a slab crossing, and little difference between open-box and ford crossings with natural reaches. A study of road crossing disturbance would involve a great deal of ground verification. 4.4.4. Small, undetectable effect size The relationship between land use and coho salmon abundance may have been too small to detect. The low power of many of the tests showing no significant relationships indicates that effect size may be a problem. The power of statististical tests is determined by sample size, 71 effect size, and significance value (alpha). Two ways of. rectifying low power are to increase effect size and increase sample size. Increasing sample size would increase power, but collecting land use of many watersheds from aerial photos can be costly and time-consuming. For future studies, perhaps a better idea would be to choose watersheds that have wider variation in development. 4.5 Relationships between coho and land use One of my findings was a significant positive relationship between proportion old growth forest in watersheds and coho fry abundance for the 1995 time period. The strength of this trend was largely dictated by one point - the Stawamus Creek watershed. However, when the Stawamus data point was removed, a positive though weaker trend remains. This is contrary to results discussed thus far; however it may be that freshwater habitat has become relatively more important in recent years. I am not aware of any previous studies that compared proportion old growth per se in watersheds to coho fry. However, there is evidence in the literature to suggest that coho is affected negatively by logging practices. It may be that there is a time lag of these affects, particularly the lack of large woody debris derived from old growth accumulating in streams. Historical clearcutting may lead to lack of large woody debris (LWD) transported downstream creating important low-velocity microhabitat preferred by coho juveniles (Bustard and Narver 1975). LWD plays an important role in forming coho microhabitat. In mainstream sections of Carnation Creek, sections with deep pools and undercut banks in association with tree roots and debris (from unlogged forest) lost fewer fish during freshets than sections without those features Tschaplinski and Hartman 1983). Smolt density has also been positively correlated with large woody debris (McMahon and Holtby 1992). 72 In old growth forests more large woody debris is available as coho microhabitat than in logged forests. McHenry et al. (1998) monitored large woody debris (LWD) in 27 watersheds in the Olympic Peninsula in various states of deforestation. They found that total volume of LWD decreased for more recently logged watersheds although number of pieces of LWD (old growth and second growth derived) did not differ between watersheds. Old growth-derived LWD volume declined. In my study watersheds, old growth was relatively rare and at high elevations; however decreased transport of LWD over time to lower elevations may have affected microhabitat of fry. Increased stream temperature occurs as a result of removing trees and canopy providing shade. There may be a point where negative effects due to incident solar radiation offset the positive effects of higher temperatures discussed previously. High incident solar radiation has been implicated in the decline of salmonids (Walters and Ward 1998). They hypothesize that damage from radiation may impair the metabolism of salmonids during smoking and ocean entry. Logging also causes deposition of sediments that could also effect coho habitat. In Carnation Creek, Scrivener and Brownlee (1989) found that survival of eggs to emergence of coho and chum (Oncorbyncbus keta) declined following logging-related substrate composition change. Deposition of fines in redds resulted in increased fry mortality due to "entombment" of fry. However, this is usually a short-term effect because sediments are transported downstream with high flow periods. Sedimentation may have coincided with coho fry cohorts in this study. Scrivener and Brownlee (1989) also found that peak flows were negatively correlated with percent coho egg to fry survival. Since peak flows are dampened by old growth, this may explain the positive relationship between coho fry and old growth that is appearing now. My study revealed a negative relationship between intensive human development (urban and industrial land use) and coho fry density. This may be due to higher peak flows, 73 channelization and riffle-dominated stream systems associated with highly impervious surfaces. Resulting increased stream velocity or lack of cover (i.e. few deep pools) may influence the amount of habitat available to coho fry. Where cutthroat trout (Orajrhynchus clarki) co-exist with coho, there is a dominance shift to cutthroat trout (Lucchetti and Fuerstenberg 1993; Scott et al. 1986; Horner et al. 1996) when moving from forested to urban watersheds. Horner et al. (1996) looked at urbanization that ranged from 0% to >50% cover in 11 watersheds, and the ratio of coho to cutthroat. They found that coho dominated in streams with less than 4% urban cover and have slight dominance in streams of 10-15% urban cover, after which dominance switches to trout. Scott et al. (1986) found lower coho biomass of about 21% in an urban creek relative to an undisturbed creek, as well as a shift to cutthroat dominance. 4.6 Stream physical attributes and coho abundance Coho fry showed a weak negative correlation with temperature (11-16°C). Apparently optimal temperatures preferred by salmonids is approximately 12-15°C; at which temperatures food conversion to body tissue is most efficient (Scott Hinch, 1995, personal communication). , In a laboratory study Brett (1951) found in a vertical gradient that a stratum of 12 to 14°C was the region of greatest concentration of salmonids. He also found that coho and chinook (O. tshawytscba) had the highest temperature tolerance of the Oriaorhynahus species. It may be, however, at the higher range of temperatures at coho fry rearing sites in this study (14 to 16°C) the increased metabolic rates of fry require too much energy, and they seek slightly colder temperatures. At Carnation Creek, where coho grew larger and faster with increased temperatures (Holtby 1988), median temperatures of the creek throughout the year did not exceed about 12°C. r 74 4.7 Evaluation of my methods 4.7.1 Rationale for land use groups used in this thesis A problem with land use studies is the many different kinds of land use types, some of which cover only very small areas, such as small parks, and some that occur very rarely, such as one golf course in Murray Creek in this study. Once I began measuring land use, I soon realized that I would have to group land use types into larger categories that made sense in the way streams would potentially be impacted. Other land use studies specified land use types according to the scale of the photos or satellite images used, or to specific land use types in which they were interested, such as wetlands or urban cover. Studies by Horner (1997) and Booth (1991) were used as the basis of land use classifications in my study. These studies looked at the impacts of urbanization on streams and the property of surface imperviousness on overland water flow, peak stream discharge, stream flow fluctuations and channelization. Since I needed to take into account all land use types including agriculture, forests, and shrub, I expanded the impervious concept to all land use types by combining the imperviousness of surfaces and the capacity of vegetation to intercept precipitation. Interception water loss by a forest canopy is the gross precipitation falling on the canopy rninus the throughfall and stemflow. interception increases with the age of a forest from seedlings to mature forest. Throughfall decreases as canopy cover increases, stemflow increases but is a relatively small quantity and the storage capacity of vegetation and litter, which is related mainly to leaf surface area increases substantially (Brooks et al. 1991). Collectively, the loss of precipitation from a watershed into the atmosphere is evapotranspiration, the combination of evaporation from vegetation and soil, and transpiration through plant leaves. The fate of rainfall that is not intercepted by vegetation is runoff, which is a combination of channel interception, surface and subsurface flow. Surface flow is considerably faster than overland flow, and impervious surfaces contribute to surface flow. This is the mechanism responsible for increases in peak stream flow 75 and increased frequency of spates or floods. Therefore, I grouped land use into different categories depending on a combination of imperviousness and water interception by vegetation. Corbett et al. (19,97) used a non-point source pollution model to compare hydrological properties of a forested and an urban watershed. They found runoff volume was on average 5.5 (±2.7) times and sediment yield was 5.5 (±2.3) times as high in the urban watershed than the forested watershed. The ratio of runoff volume to rainfall volume (runoff ratio) was on average 14.5% higher in the urban watershed. They attributed the greater variation in runoff in the forested watershed to the variable effects of infiltration (of rainfall into soil) and transpiration. Meuser (1990) found that afforestation of abandoned land in West German highland areas led to reduction of total run-off of floodwaters and of groundwater replenishment. Spruce tree removal followed by grass growth led to increased flooding. Troendle (1987) compared a clearcut plot and non-clearcut plots in Colorado and found peak flow rate and total flow increased during the first full year after harvesting. The Coweeta catchment study in North Carolina, USA, is probably the world's longest-term study of forestry practices in 20 sub-catchments and changes in catchment hydrology. In a review of this study, Whitehead and Robinson (1993) report that long-term streamflow records showed that mean streamflow and peak flow rates increase by about 15% after clearcutting. Forest-type also influenced streamflow. For example, hardwood to pine conversion reduced runoff by 250mm year"1. Hardwood to grass conversion altered streamflow, and a decline in growth of grass led to increased streamflow. In a comparison of forested and grassland catchments in central Wales (the Plynlimon catchment study), annual evaporation losses were consistently higher from the forested catchment (Whitehead and Robinson 1993). This is attributed to the interception of precipitation by the tree canopy, and the faster rate of evaporation than that of the grasslands (Calder 1990 in Whitehead and Robinson 1993). Booth (1991) states that urban development magnified peak discharges and also created new peak runoff events caused by smaller storms the 76 magnitude of which produced no storm runoff before urban development. Surface flow rates are typically a few tenths of a meter per second, and subsurface flow rates are usually several orders of magnitude slower for most storms; therefore surface flow contributes the greater volume of runoff (Booth, 1991). Other influences of urban land use are reduced base flow (groundwater) (Klein 1979), channel expansion and/or incision (Booth 1990), and increased pollutants such as heavy metals, nutrients, and sediment (Wahl 1997; Cohn-Lee and Cameron 1992). Further evidence of hydrological impacts of streams is changes in fish assemblages. In a study of stream fish assemblages in 34 sites in Wisconsin and Minnesota, Poff and Allan (1995) used long-term hydrological data to discriminate between two. ecologically defined groups of assemblages. The assemblages were associated with either hydrologically stable streams (high predictability of daily flows and stable baseflow conditions) or hydrologically variable streams (high coefficient of variation of daily flows, moderate frequency of spates). They propose that hydrological alterations induced by climate change or other anthropogenic disturbances could modify stream fish assemblages. 4.7.2 Land use collection To verify my land use collection methods, I compared my land use from Black Creek to that collected by Brown et al. (1996). I found the results to be similar from both studies. For example in Brown et al.'s (1996) Black Creek estimates, in 1972 740 hectares of the watershed was agricultural, and in 1994 1289 hectares was agricultural. This roughly corresponds to my estimates of 943 hectares in 1975 and 1251 hectares (agriculture and rural farms) in 1995. To verify my criteria for usable stream length (to coho) I compared useable length of Black Creek calculated in my study to Brown et al.'s (1996) study. My criteria for useable habitat was streams of gradient less than or equal to 4%, which I calculated was 165 km of Black Creek. 77 According to Brown et al. (1999 unpublished data), useable habitat was 125.7 km - 55.4 km known habitat, 23.3 km probable habitat and 47.0 km potential habitat, totalling 125.7 km. Probable habitat was habitat similar in character to and upstream of known habitat and there were no restrictions to coho movement. Potential habitat didn't contain coho due to a possible physical limitation to upstream movement, if there were cutthroat trout present, and if they had similar characteristics to known coho habitat. Also Brown et al. (1996) report that 48% of the drainage is absent from TRIM (Terrain Resource Information Maps) and that much of the missing stream network contains coho or cutthroat. Since I used TRIM maps to measure stream lengths, stream lengths may have been underestimated. However, this was probably offset by the amount of 4% gradient streams inaccessible or inhospitable to coho. An excellent method of measuring land use is from aerial photographs. However, since the scale of a photo changes with topography, I recommend for temporal land use change studies that historical photos are georeferenced to orthophotos as in this study. This is less costly than making orthophotos from historical aerial photos. If photos are not scaled then important ecological land use information may be lost. Scale on aerial photos is the inverse of camera focal length divided by the height of the plane above land (i.e. 10 km at sea level is 1 cm on a photo, but 10 km at 1000 feet above sea level is Vz cm). Hence, area at high elevations are smaller than actual area if measured at sea-level scale. In this study, the area of tree cover at all elevations was important ecologically because of its evapotranspiration and water storage properties. 78 C O N C L U S I O N The results of my study support the hypothesis that land use has had a minor effect on coho declines in the Strait of Georgia over the past two decades. However historical land use practices may have contributed to differences in spatial densities of coho fry in the 1990s. Employing land use as an indicator of habitat loss or change in wildlife population studies has some advantages. One is the availability of aerial photos that provide consistent land use coverage, which can be supplemented by other land use records. Second, land use affects habitat in many different ways, which although important, does not have to be ascertained specifically. Thirdly, recommendations altering land use practices can be made with direct evidence from land use studies. However, interpretation of photos is subject to observer bias, so standard methods should be applied if possible. In this way multiple land use studies could be joined to provide extensive databases for comparison with wildlife populations. It is important to conduct watershed-scale studies of land use effects on fish and wildlife to try and quantify the contribution of land use to population fluctuations. Then appropriate research effort and funding may be allocated proportionately to land use issues, or to other factors affecting wildlife populations. 79 References Beamish, R. J. 1993. Climate change and exceptional fish production off the west coast of North America. Can. J. Fish. Aquat. Sci. 50: 2270-2291. Beamish, R. J. and C. M. Neville. 1995. Pacific salmon and pacific herring mortalities in the Fraser River plume caused by river lamprey (Lampetra ayresi). Can. J. Fish. Aquat. Sci. 52: 644-650. Beechie, T., E. Beamer, and L. Wasserman. 1994. 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