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Using multiple scales for enhancing predictive capacity in modelling responses to the cumulative effects of disturbance in streams

University of British Columbia

Disturbances affect ecosystems in complex ways at multiple spatial and temporal scales. Much research has focused on quantifying multiscale landscape patterns but less has focused on using multiscale information to increase predictive capacity in understanding complex ecosystem processes. Predicting cumulative effects of stressors is important for managing ecologically resilient landscapes. Of particular importance is predicting how changes in land use cumulatively affect aquatic ecosystems, such as streams. Furthermore, despite strong functional linkages showing that headwaters deliver important resources downstream, limited research focuses on understanding landscape-scale variation in headwaters or predicting how many instances of fine-scale headwater alterations cumulatively affects downstream. My dissertation addressed predictive capacity and headwater variability in several ways. First, I used empirical data from headwaters in an urbanizing region to examine multiscale variability in headwater condition and showed that incorporating spatial dependencies can nearly double predictive capacity. Second, I developed and analyzed data from a citizen science protocol designed to examine functional and structural variability of urban headwater streams. Specifically, I examined cotton strip decomposition rates to show that local-scale variation explained nearly 70% of the variability. Third, I developed benthic macroinvertebrate cumulative effects models to examine the effects of environmental context and land cover conditions. I showed that the relative importance of environmental, land cover, spatial, and headwater variables were indicator-dependent suggesting that practitioners should address context dependency when evaluating land cover conclusions. Fourth, I applied a common, multiscale analytical framework (i.e., spatial stream network models) to examine the variability of chemical, decomposition, respiration, and benthic macroinvertebrate indicators to also show that incorporating multiscale dependencies can increase predictive capacity on average, but again this was indicator-dependent. To confront complexity, generate stronger predictions, identify knowledge gaps, and improve understanding of cumulative effects, environmental practitioners stand to benefit from incorporating multiscale dependencies.

Text

2020-03-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/73850

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/