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Abstract All populations are affected by multiple environmental drivers, including climatic drivers such as temperature or precipitation and biotic drivers such as herbivory or mutualisms. The relative response of a population to each driver is critical to prioritizing threat mitigation for conservation and to understanding whether climatic or biotic drivers most strongly affect fitness. However, the importance of different drivers can vary dramatically across species and across populations of the same species. Theory suggests that the response to climatic versus biotic drivers can be affected by both the species' fundamental niche breadth and the latitude of the population at which the response is measured. However, we have few tests of how these two factors affect relative response to drivers separately, let alone tests of how niche breadth and latitude together influence responses. Here, we use a meta‐analysis of published studies on population response to climatic and biotic drivers in terrestrial plants, combined with estimates of climatic niche breadth and position within climatic niche derived from herbarium records, to show that species' niche breadth is the primary determinant of response to climatic versus biotic drivers. Namely, we find that response to climatic drivers changes only minimally with increasing niche breadth, while response to biotic drivers increases with niche breadth. We see similar relationships when considering range size instead of niche breadth. Surprisingly, we find no effects of latitude on the relative effect of climatic versus biotic drivers. Our work suggests that populations of species with small and large ranges experience similar extirpation risks due to the negative impacts of climate change. By contrast, populations of species with large (but not small) ranges may be highly susceptible to changes in densities or distributions of interacting species. 

Abstract Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. We extend regression LTREs to incorporate nonlinear (second‐order) terms and compare the accuracy of both approaches for three previously published demographic datasets. We show that the second‐order approach equals or outperforms the linear approach for all three case studies, even when all of the underlying vital rate functions are linear. Nonlinear vital rate responses to driver changes contributed most to population growth rate responses, but life history changes also made substantial contributions. Our results suggest that moving from linear to second‐order LTRE analyses could improve our understanding of population responses to changing environments. 

Multiple, simultaneous environmental changes, in climatic/abiotic factors, interacting species, and direct human influences, are impacting natural populations and thus biodiversity, ecosystem services, and evolutionary trajectories. Determining whether the magnitudes of the population impacts of abiotic, biotic, and anthropogenic drivers differ, accounting for their direct effects and effects mediated through other drivers, would allow us to better predict population fates and design mitigation strategies. We compiled 644 paired values of the population growth rate ( λ ) from high and low levels of an identified driver from demographic studies of terrestrial plants. Among abiotic drivers, natural disturbance (not climate), and among biotic drivers, interactions with neighboring plants had the strongest effects on λ . However, when drivers were combined into the 3 main types, their average effects on λ did not differ. For the subset of studies that measured both the average and variability of the driver, λ was marginally more sensitive to 1 SD of change in abiotic drivers relative to biotic drivers, but sensitivity to biotic drivers was still substantial. Similar impact magnitudes for abiotic/biotic/anthropogenic drivers hold for plants of different growth forms, for different latitudinal zones, and for biomes characterized by harsher or milder abiotic conditions, suggesting that all 3 drivers have equivalent impacts across a variety of contexts. Thus, the best available information about the integrated effects of drivers on all demographic rates provides no justification for ignoring drivers of any of these 3 types when projecting ecological and evolutionary responses of populations and of biodiversity to environmental changes. 

Abstract Populations of many species are genetically adapted to local historical climate conditions. Yet most forecasts of species’ distributions under climate change have ignored local adaptation (LA), which may paint a false picture of how species will respond across their geographic ranges. We review recent studies that have incorporated intraspecific variation, a potential proxy for LA, into distribution forecasts, assess their strengths and weaknesses, and make recommendations for how to improve forecasts in the face of LA. The three methods used so far (species distribution models, response functions, and mechanistic models) reflect a trade‐off between data availability and the ability to rigorously demonstrate LA to climate. We identify key considerations for incorporating LA into distribution forecasts that are currently missing from many published studies, including testing the spatial scale and pattern of LA, the confounding effects of LA to nonclimatic or biotic drivers, and the need to incorporate empirically based dispersal or gene flow processes. We suggest approaches to better evaluate these aspects of LA and their effects on species‐level forecasts. In particular, we highlight demographic and dynamic evolutionary models as promising approaches to better integrate LA into forecasts, and emphasize the importance of independent model validation. Finally, we urge closer examination of how LA will alter the responses of central vs. marginal populations to allow stronger generalizations about changes in distribution and abundance in the face of LA.