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Environmental fluctuations are pervasive in nature, but the influence of non-directional temporal variation on range limits has received scant attention. We synthesize insights from the literature and use simple models to make conceptual points about the potentially wide range of ecological and evolutionary effects of temporal variation on range limits. Because organisms respond nonlinearly to environmental conditions, temporal variation can directionally alter long-term growth rates, either to shrink or to expand ranges. We illustrate this diversity of outcomes with a model of competition along a mortality gradient. Temporal variation can permit transitions between alternative states, potentially facilitating range expansion. We show this for variation in dispersal, using simple source–sink population models (with strong Allee effects, or with gene flow hampering local adaptation). Temporal variation enhances extinction risk owing to demographic stochasticity, rare events, and loss of genetic variation, all tending to shrink ranges. However, specific adaptations to exploit variation (including dispersal) may permit larger ranges than in similar but constant environments. Grappling with temporal variation is essential both to understand eco-evolutionary dynamics at range limits and to guide conservation and management strategies. This article is part of the theme issue ‘Species’ ranges in the face of changing environments (Part II)’. 

Abstract Genetic connectivity lies at the heart of evolutionary theory, and landscape genetics has rapidly advanced to understand how gene flow can be impacted by the environment. Isolation by landscape resistance, often inferred through the use of circuit theory, is increasingly identified as being critical for predicting genetic connectivity across complex landscapes. Yet landscape impediments to migration can arise from fundamentally different processes, such as landscape gradients causing directional migration and mortality during migration, which can be challenging to address. Spatial absorbing Markov chains (SAMC) have been introduced to understand and predict these (and other) processes affecting connectivity in ecological settings, but the relationship of this framework to landscape genetics remains unclear. Here, we relate the SAMC to population genetics theory, provide simulations to interpret the extent to which the SAMC can predict genetic metrics and demonstrate how the SAMC can be applied to genomic data using an example with an endangered species, the Panama City crayfish Procambarus econfinae , where directional migration is hypothesized to occur. The use of the SAMC for landscape genetics can be justified based on similar grounds to using circuit theory, as we show how circuit theory is a special case of this framework. The SAMC can extend circuit‐theoretic connectivity modelling by quantifying both directional resistance to migration and acknowledging the difference between migration mortality and resistance to migration. Our empirical example highlights that the SAMC better predicts population structure than circuit theory and least‐cost analysis by acknowledging asymmetric environmental gradients (i.e. slope) and migration mortality in this species. These results provide a foundation for applying the SAMC to landscape genetics. This framework extends isolation‐by‐resistance modelling to account for some common processes that can impact gene flow, which can improve predicting genetic connectivity across complex landscapes. 

Successful public health regimes for COVID-19 push below unity long-term regionalR_t—the average number of secondary cases caused by an infectious individual. We use a susceptible-infectious-recovered (SIR) model for two coupled populations to make the conceptual point that asynchronous, variable local control, together with movement between populations, elevates long-term regionalR_t, and cumulative cases, and may even prevent disease eradication that is otherwise possible. For effective pandemic mitigation strategies, it is critical that models encompass both spatiotemporal heterogeneity in transmission and movement. 

Abstract Habitat loss is often considered the greatest near‐term threat to biodiversity, while the impact of habitat fragmentation remains intensely debated. A key issue of this debate centers on the problem of scale–landscape or patch–at which to assess the consequences of fragmentation. Yet patterns are often confounded across scales, and experimental designs that could solve this scaling problem remain scarce. We conducted two field experiments in 30 experimental landscapes in which we manipulated habitat loss, fragmentation, and patch size for a community of four insect herbivores that specialize on the cactusOpuntia. In the first experiment, we destroyed 2088Opuntiapatches in either aggregated or random patterns and compared the relative effects of landscape‐scale loss and fragmentation to those of local patch size on species occurrence. This experiment focused on manipulating the relative separation of remaining patches, where we hypothesized that aggregated loss would disrupt dispersal more than random loss, leading to lower occurrence. In the second experiment, we destroyed 759Opuntiapatches to generate landscapes that varied in patch number and size for a given amount of habitat loss and assessed species occurrence. This experiment focused on manipulating the subdivision of remaining habitat, where we hypothesized that an increase in the number of patches for a given amount of loss would lead to negative effects on occurrence. For both, we expected that occurrence would increase with patch size. We find strong evidence for landscape‐scale effects of habitat fragmentation, with aggregated loss and a larger number of patches for a given amount of habitat loss leading to a lower frequency of patches occupied in landscapes. In both experiments, occurrence increased with patch size, yet interactions of patch size and landscape‐scale loss and fragmentation drove species occurrence in patches. Importantly, the direction of effects were consistent across scales and effects of patch size were sufficient to predict the effects of habitat loss and fragmentation across entire landscapes. Our experimental results suggest that changes at both the patch and landscape scales can impact populations, but that a long‐standing pattern—the patch‐size effect—captures much of the key variation shaping patterns of species occurrence. 

Abstract Evolutionary rescue occurs when genetic change allows a population to persist in response to an environmental change that would otherwise have led to extinction. Most studies of evolutionary rescue assume that species have either fully clonal or fully sexual reproduction; however, many species have partially clonal reproductive strategies in which they reproduce both clonally and sexually. Furthermore, the few evolutionary rescue studies that have evaluated partially clonal reproduction did not consider fluctuations in the environment, which are nearly ubiquitous in nature. Here, we use individual‐based simulations to investigate how environmental fluctuations (either uncorrelated or positively autocorrelated) influence the effect of clonality on evolutionary rescue. We show that, for moderate magnitudes of environmental fluctuations, as was found in the absence of fluctuations, increasing the degree of clonality increases the probability of population persistence in response to an abrupt environmental change, but decreases persistence in response to a continuous, directional environmental change. However, with large magnitudes of fluctuations, both the benefits of clonality following a step change and the detrimental effects of clonality following a continuous, directional change are generally reduced; in fact, in the latter scenario, increasing clonality can even become beneficial if environmental fluctuations are autocorrelated. We also show that increased generational overlap dampens the effects of environmental fluctuations. Overall, we demonstrate that understanding the evolutionary rescue of partially clonal organisms requires not only knowledge of the species life history and the type of environmental change, but also an understanding of the magnitude and autocorrelation of environmental fluctuations.