Search for: All records

Stochastic diffusion is the noisy process through which dynamics like epidemics, or agents like animal species, disperse over a larger area. These processes are increasingly important to better prepare for pandemics and as species ranges shift in response to climate change. Unfortunately, modelling is mostly done with expensive computational simulations or inaccurate deterministic tools that ignore the randomness of dispersal. We introduce ‘mean-FLAME’ models, tracking stochastic dispersion using approximate master equations to follow the probability distribution over all possible states of an area of interest, up to states active enough to be approximated using a mean-field model. In the limit where we track all states, this approach is locally exact, and in the other limit collapses to traditional deterministic models. In predator–prey systems, we show that tracking a handful of states around key absorbing states is sufficient to accurately model extinction. In disease models, we show that classic mean-field approaches underestimate the heterogeneity of epidemics. And in nonlinear dispersal models, we show that deterministic tools fail to capture the speed of spatial diffusion. These effects are all important for marginal areas that are close to unsuitable for diffusion, like the edge of a species range or epidemics in small populations. 

ABSTRACT Wind is the primary dispersal mechanism of most fungal spores but is rarely considered in studies of fungal communities, limiting inference of assembly mechanisms and forecasting responses to climate change. We compiled wind‐connectivity models—‘windscapes’—to model potential dispersal of fungal spores at the continental scale and linked them with a molecular dataset of North American soil fungi. Our analyses demonstrate that prevailing windflow patterns exhibit a significantly stronger signal on fungal community structure than do geographic distances amongst sites. Notably, the signature of wind was detectable for mushrooms and fungi producing primarily wind‐dispersed spores. Contrastingly, fungi primarily reliant on animal dispersal exhibited a strong signature of geographic distance but not wind‐connectivity. Additionally, we show that directionally ‘downwind’ sites are more diverse than comparatively ‘upwind’ sites. Altogether, our findings suggest that future wind patterns will shape the adaptation potential of fungal communities dispersing into suitable climatic niches. 

Fine‐scale spatial climate variation fosters biodiversity and buffers it from climate change, but ecological studies are constrained by the limited accessibility of relevant fine‐scale climate data. In this paper we introduce a novel form of species distribution model that uses species occurrences to predict high‐resolution climate variation. This new category of ‘bioclimate' data, representing micro‐scale climate as experienced by one or more species of interest, is a useful complement to microclimate data from existing approaches. The modeling method, called BISHOP for ‘bioclimate inference from species' high‐resolution occurrence patterns,' uses data on species occurrences, coarse‐scale climate, and fine‐scale physiography (e.g. terrain, soil, vegetation) to triangulate fine‐scale bioclimate patterns. It works by pairing a climate‐downscaling function predicting a latent bioclimate variable, with a niche function predicting species occurrences from bioclimate. BISHOP infers how physiography affects bioclimate, estimates how these effects vary geographically, and produces high‐resolution (10 m) maps of bioclimate over large regions. It also predicts species distributions. After introducing this approach, we apply it in an empirical study focused on topography and trees. Using data on 216 North American tree species, we document the biogeographic patterns that enable BISHOP, estimate how four terrain variables (northness, eastness, windward exposure, and elevational position) each influence three climate variables, and use these results to produce downscaled maps of tree‐specific bioclimate. Model validation demonstrates that inferred bioclimate outperforms macroclimate in predicting distributions of separate species not used during inference, confirming its ecological relevance. Our results show that nearby bioclimates can differ by 5°C in temperature and twofold in moisture, with equator‐facing, east‐facing, windward‐facing, and locally elevated sites exhibiting hotter, drier bioclimates on average. But these effects vary greatly across climate zones, revealing that topographically similar landscapes can differ strongly in their bioclimate variation. These results have important implications for micrometeorology, biodiversity, and climate resilience. 

It has been proposed that climate adaptation research can benefit from an evolutionary approach. But related empirical research is lacking. We advance the evolutionary study of climate adaptation with two case studies from contemporary United States agriculture. First, we define ‘cultural adaptation to climate change’ as a mechanistic process of population-level cultural change. We argue this definition enables rigorous comparisons, yields testable hypotheses from mathematical theory and distinguishes adaptive change, non-adaptive change and desirable policy outcomes. Next, we develop an operational approach to identify ‘cultural adaptation to climate change’ based on established empirical criteria. We apply this approach to data on crop choices and the use of cover crops between 2008 and 2021 from the United States. We find evidence that crop choices are adapting to local trends in two separate climate variables in some regions of the USA. But evidence suggests that cover cropping may be adapting more to the economic environment than climatic conditions. Further research is needed to characterize the process of cultural adaptation, particularly the routes and mechanisms of cultural transmission. Furthermore, climate adaptation policy could benefit from research on factors that differentiate regions exhibiting adaptive trends in crop choice from those that do not. This article is part of the theme issue ‘Climate change adaptation needs a science of culture’. 

Crop switching, in which farmers grow a crop that is novel to a given field, can help agricultural systems adapt to changing environmental, cultural, and market forces. Yet while regional crop production trends receive significant attention, relatively little is known about the local-scale crop switching that underlies these macrotrends. We characterized local crop-switching patterns across the United States using the US Department of Agriculture (USDA) Cropland Data Layer, an annual time series of high resolution (30 m pixel size) remote-sensed cropland data from 2008 to 2022. We found that at multiple spatial scales, crop switching was most common in sparsely cultivated landscapes and in landscapes with high crop diversity, whereas it was low in homogeneous, highly agricultural areas such as the Midwestern corn belt, suggesting a number of potential social and economic mechanisms influencing farmers’ crop choices. Crop-switching rates were high overall, occurring on more than 6% of all US cropland in the average year. Applying a framework that classified crop switches based on their temporal novelty (crop introduction versus discontinuation), spatial novelty (locally divergent versus convergent switching), and categorical novelty (transformative versus incremental switching), we found distinct spatial patterns for these three novelty dimensions, indicating a dynamic and multifaceted set of cropping changes across US farms. Collectively, these results suggest that innovation through crop switching is playing out very differently in various parts of the country, with potentially significant implications for the resilience of agricultural systems to changes in climate and other systemic trends. 

Quantitative knowledge of xylem physical tolerance limits to dehydration is essential to understanding plant drought tolerance but is lacking in many long-vessel angiosperms. We examine the hypothesis that a fundamental association between sustained xylem water transport and downstream tissue function should select for xylem that avoids embolism in long-vessel trees by quantifying xylem capacity to withstand air entry of western North American oaks ( Quercus spp.). Optical visualization showed that 50% of embolism occurs at water potentials below −2.7 MPa in all 19 species, and −6.6 MPa in the most resistant species. By mapping the evolution of xylem vulnerability to embolism onto a fossil-dated phylogeny of the western North American oaks, we found large differences between clades (sections) while closely related species within each clade vary little in their capacity to withstand air entry. Phylogenetic conservatism in xylem physical tolerance, together with a significant correlation between species distributions along rainfall gradients and their dehydration tolerance, suggests that closely related species occupy similar climatic niches and that species' geographic ranges may have shifted along aridity gradients in accordance with their physical tolerance. Such trends, coupled with evolutionary associations between capacity to withstand xylem embolism and other hydraulic-related traits, yield wide margins of safety against embolism in oaks from diverse habitats. Evolved responses of the vascular system to aridity support the embolism avoidance hypothesis and reveal the importance of quantifying plant capacity to withstand xylem embolism for understanding function and biogeography of some of the Northern Hemisphere’s most ecologically and economically important plants. 

Abstract The impacts of climate change have re‐energized interest in understanding the role of climate in setting species geographic range edges. Despite the strong focus on species' distributions in ecology and evolution, defining a species range edge is theoretically and empirically difficult. The challenge of determining a range edge and its relationship to climate is in part driven by the nested nature of geography and the multidimensionality of climate, which together generate complex patterns of both climate and biotic distributions across landscapes. Because range‐limiting processes occur in both geographic and climate space, the relationship between these two spaces plays a critical role in setting range limits. With both conceptual and empirical support, we argue that three factors—climate heterogeneity, collinearity among climate variables, and spatial scale—interact to shape the spatial structure of range edges along climate gradients, and we discuss several ways that these factors influence the stability of species range edges with a changing climate. We demonstrate that geographic and climate edges are often not concordant across species ranges. Furthermore, high climate heterogeneity and low climate collinearity across landscapes increase the spectrum of possible relationships between geographic and climatic space, suggesting that geographic range edges and climatic niche limits correspond less frequently than we may expect. More empirical explorations of how the complexity of real landscapes shapes the ecological and evolutionary processes that determine species range edges will advance the development of range limit theory and its applications to biodiversity conservation in the context of changing climate.