Reduced complexity models for regional aquatic habitat suitability assessment

Abstract

Generalizable methods that identify suitable aquatic habitat across large river basins and regions are needed to inform resource management. Habitat suitability models intersect environmental variables to predict species occurrence, but are often data intensive and thus are typically developed at small spatial scales. This study estimated mean monthly aquatic habitat suitability throughout Utah (USA) for Bonneville Cutthroat Trout (Oncorhynchus clarkii utah) and Bluehead Sucker (Catostomus discobolus) with publicly available, geospatial datasets. We evaluated 15 habitat suitability models using unique combinations of percent of mean annual discharge, velocity, gradient, and stream temperature. Environmental variables were validated with observed conditions and species presence observations to verify habitat suitability estimates. Stream temperature, gradient, and discharge best predicted Bonneville Cutthroat Trout presence, and gradient and discharge best predicted Bluehead Sucker presence. Simple aquatic habitat suitability models outperformed models that used only streamflow to estimate habitat for both species, and are useful for conservation planning and water resources decision‐making. This modeling approach could enable resource managers to prioritize stream restoration across vast regions within their management domain, and is potentially compatible with water management modeling to improve ecological objectives in management models.

Embedded Complexity of Evolutionary Sequences

“…With respect to digital elevation models, for example, the highest available resolutions are not always optimal for ecosystem modeling or terrain representation (e.g., [ 46 , 47 , 48 ]). Several studies have also found that, in addition to advantages in computational efficiencies and implementation costs, reduced-complexity models provide more accurate and useful results in many environmental modeling contexts (e.g., [ 9 , 10 , 11 , 12 , 49 , 50 ]).…”

“…The issue of complexities of evolutionary sequences parallels the rise of reduced-complexity models in ecology, hydrology, and geomorphology. Including more factors, processes, or constraints in a model tends to reduce the generality of the results, and simpler models often produce satisfactory or even more accurate results (e.g., [ 9 , 10 , 11 , 12 ]). However, the appropriate level of complexity (in terms of the number of variables, parameters, and processes included in the model) for the problem at hand is not always clear [ 12 , 13 ].…”