Abstract
Confronted with a rapidly changing world and limited resources for conservation, ecologists are increasingly challenged with predicting the impact of climate and land‐use change on wildlife. A common approach is to use habitat‐suitability models (HSMs) to explain aspects of species' occurrence, such as presence, abundance, and distribution, utilizing physical habitat characteristics. Although HSMs are useful, they are limited because they are typically created using spatial rather than temporal data, which omits temporal dynamics. We explored the value of combining spatial and temporal approaches by comparing HSMs with autoregressive population models. We investigated a 28‐year period of bird community dynamics at a field site in northern California during which time the plant community has been transitioning from scrub to conifer forest. We used the two model frameworks to quantify the contribution of vegetation change, weather, and population processes (autoregressive models only) to variation in density of seven bird species over the first 23 years. Model predictive ability was then tested using the subsequent five years of population density data. HSMs explained 58% to 90% of the deviance in species' density. However, models that included density dependence in addition to vegetation covariates provided a better fit to the data for three of the seven species, Song Sparrow (Melospiza melodia), White‐crowned Sparrow (Zonotrichia leucophrys), and Wrentit (Chamaea fasciata). These three species have more localized dispersal compared to the other four species, suggesting that dispersal tendency may be an important life‐history trait to consider when predicting the impact of climate and land‐use change on population levels. Our results suggest that HSMs can effectively explain and predict variation in species' densities through time, however for species with localized dispersal, it may be especially informative to include population processes.
Highlights
Habitat suitability models (HSMs) are an integral component of most species distribution models, and are a cornerstone of predicting species’ response to climate and land-use change (Guisan and Thuiller 2005, Elith and Leathwick 2009)
If the abundance of a species is strongly influenced by local reproductive rates, density dependence, or other temporally varying factors, HSMs may fail to provide an accurate prediction of habitat suitability, or misrepresent the true uncertainty associated with the process (Pearson and Dawson 2003, Rotenberry and Wiens 2009)
We compare the utility of HSMs and population models both retrospectively and prospectively using a 28-year dataset detailing changes in density of seven passerines and near-passerines in a landscape undergoing rapid secondary vegetation succession. We developed both HSM and population models for these species to evaluate the influence of population processes, vegetation change, and variation in rainfall on species density from 1983 to 2005
Summary
Habitat suitability models (HSMs) are an integral component of most species distribution models, and are a cornerstone of predicting species’ response to climate and land-use change (Guisan and Thuiller 2005, Elith and Leathwick 2009). Certain assumptions may limit PORZIG ET AL Their applicability, for predicting species responses to future conditions (Pearson and Dawson 2003, Araujo and Guisan 2006). Because HSMs are usually created using population data collected across space rather than through time, any temporal dynamics in speciesenvironment interactions are not captured (Elith and Leathwick 2009, Wiens et al 2009). Most of these models ignore populationdynamic processes, such as dispersal, biologically realistic growth rates, and density dependence. If the abundance of a species is strongly influenced by local reproductive rates, density dependence, or other temporally varying factors, HSMs may fail to provide an accurate prediction of habitat suitability, or misrepresent the true uncertainty associated with the process (Pearson and Dawson 2003, Rotenberry and Wiens 2009)
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