Modeling climate‐driven range shifts in populations of two bird species limited by habitat independent of climate

Abstract

Ranges of species around the world are expected to contract in response to climate change. Species distribution models (SDMs) are a powerful tool for predicting changes in habitat availability, but the variables selected to create SDMs influence their performance. In addition to climate, habitat characteristics and species traits can play a role in predicting species distribution. In this paper, we consider how variable selection influences the accuracy of SDMs when applied to isolated subpopulations of two widely distributed bird species: great gray owl (Strix nebulosa) and willow flycatcher (Empidonax traillii). In the Sierra Nevada of California, these species are restricted largely to discrete patches of meadow habitat within a forest matrix, providing the potential to identify specific locations to target conservation efforts. We contrast predictions made by SDMs that consider climatic variables alone with those that incorporate both climate and geophysical variables. Adding geophysical variables resulted in differing model predictions. For willow flycatchers, adding geophysical variables improved predictive performance. In the case of great gray owls, models with and without geophysical variables had nearly identical performance under historical conditions but differed starkly in their predictions. The full model (climatic and geophysical variables) predicted habitat availability to decrease moderately, whereas the climate-only model predicted nearly complete loss of favorable habitat by 2099. The climate-only model is consistent with expectations based on previous SDMs of birds across North America, but previous studies also assumed homogeneity in species traits and range-wide habitat requirements. The full model appears more consistent with recent trends in great gray owl numbers in the Sierra Nevada specifically, where the population has remained relatively stable over recent decades. Given contradictions in our model predictions, care should be taken when trying to apply similar SDMs to other systems.

MERRA-based trend analysis identifies complex, multidecadal patterns of changing climatic suitability for Cassin’s Sparrow (Peucaea cassinii)

“…Given that data on species occurrences are far more common and readily accessible than data on species abundance, it is not surprising that a significant amount of research has focused on clarifying the abundance-suitability (AS) relationship and the extent to which ENM-derived estimates of environmental suitability can serve as a proxy for population abundance [145]. While much remains to be learned about modeling the distribution of abundance, a consensus appears to be emerging around the following points: the distribution of environmental suitability based on bioclimatic variables alone generally shows little, if any, correlation with the distribution of abundance [124,146–148]; the combination of bioclimatic predictors with other environmental attributes, such as EFAs, edaphic variables, topographic data, vegetation indices, etc., can capture the influence of factors affecting abundance rather than just occurrence, thereby yielding suitability model results that are often highly correlated with abundance [145,147–149], and, in some cases, reflect well the mean and maximal local abundances of a species [150]; the AS correlation is particularly strengthened when the added non-climatic predictors capture, in one way or another, environmental drivers that influence fitness, dispersal, recruitment, or other demographic properties of a species [149,151–153]; and correlative ENM that takes into account this bioclimatic-demographic connection can provide practical benefits to spatial conservation efforts, such as readily-attainable, large-scale abundance estimates; hot-spot identification; and reduced survey and monitoring costs [24,145,147,148,150]. Our findings are consistent with the first point: we see different patterns of historical change in environmental suitability depending on whether time series models are driven by bioclimatic variables alone or by variables more aligned with ecological functioning, and therefore potentially species demography.…”

“…correlative ENM that takes into account this bioclimatic-demographic connection can provide practical benefits to spatial conservation efforts, such as readily-attainable, large-scale abundance estimates; hot-spot identification; and reduced survey and monitoring costs [24,145,147,148,150].…”

“…(2) the combination of bioclimatic predictors with other environmental attributes, such as EFAs, edaphic variables, topographic data, vegetation indices, etc., can capture the influence of factors affecting abundance rather than just occurrence, thereby yielding suitability model results that are often highly correlated with abundance [145,[147][148][149], and, in some cases, reflect well the mean and maximal local abundances of a species [150];…”

“…(1) the distribution of environmental suitability based on bioclimatic variables alone generally shows little, if any, correlation with the distribution of abundance [124,[146][147][148];…”