Skewed temperature dependence affects range and abundance in a warming world

Hurford, Amy; Cobbold, Christina A.; Molnár, Péter K.

doi:10.1098/rspb.2019.1157

Cited by 18 publications

(28 citation statements)

References 47 publications

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“…the arrow in figure 2) and those that will be pushed from epidemics into R 0 < 1 space [27,42]. At the same time, warming may alter the ability of parasites to disperse into new regions that were previously unsuitable [43]. This suggests that for some systems, it may be insufficient to predict warming's effects on epidemics based solely on changes to R 0 , and that factors such as dispersal and colonization may need to be incorporated [43].…”

Section: Discussionmentioning

confidence: 99%

Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model

et al. 2020

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Predicting the effects of seasonality and climate change on the emergence and spread of infectious disease remains difficult, in part because of poorly understood connections between warming and the mechanisms driving disease. Trait-based mechanistic models combined with thermal performance curves arising from the metabolic theory of ecology (MTE) have been highlighted as a promising approach going forward; however, this framework has not been tested under controlled experimental conditions that isolate the role of gradual temporal warming on disease dynamics and emergence. Here, we provide experimental evidence that a slowly warming host–parasite system can be pushed through a critical transition into an epidemic state. We then show that a trait-based mechanistic model with MTE functional forms can predict the critical temperature for disease emergence, subsequent disease dynamics through time and final infection prevalence in an experimentally warmed system of Daphnia and a microsporidian parasite. Our results serve as a proof of principle that trait-based mechanistic models using MTE subfunctions can predict warming-induced disease emergence in data-rich systems—a critical step towards generalizing the approach to other systems.

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Section: Discussionmentioning

confidence: 99%

Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model

et al. 2020

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“…However, in some situations, understanding and predicting how spatial structure affects a biological invasion is more relevant than predictions of population density. Examples include the invasion of a pest species (Sprague et al, 2019), species range shifts due to climate change (Godsoe et al, 2014;Hurford et al, 2019), wound healing where cells migrate to fill injured tissue (Maini et al, 2004), or invasion of cancer cells into healthy tissue.…”

Section: Discussionmentioning

confidence: 99%

Small-scale spatial structure influences large-scale invasion rates

2020

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Local interactions among individual members of a population can generate intricate small-scale spatial structure, which can strongly influence population dynamics. The two-way interplay between local interactions and population dynamics is well understood in the relatively simple case where the population occupies a fixed domain with a uniform average density. However, the situation where the average population density is spatially varying is less well understood. This situation includes ecologically important scenarios such as species invasions, range shifts, and moving population fronts. Here, we investigate the dynamics of the spatial stochastic logistic model in a scenario where an initially confined population subsequently invades new, previously unoccupied territory. This simple model combines density-independent proliferation with dispersal, and density-dependent mortality via competition with other members of the population. We show that, depending on the spatial scales of dispersal and competition, either a clustered or a regular spatial structure develops over time within the invading population. In the short-range dispersal case, the invasion speed is significantly lower than standard predictions of the mean-field model. We conclude that mean-field models, even when they account for non-local processes such as dispersal and competition, can give misleading predictions for the speed of a moving invasion front.

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“…More realistic growth functions, where the intrinsic growth rate depends directly on the temperature at each location in the habitat, result in a situation where intrinsic growth is highest in the interior of the species range and declines gradually towards range boundaries (Hurford et al 2019). Fully dispersing populations with right-skewed growth functions (long right tail), have been shown to exhibit lower population abundance, and lag behind climate change when compared to populations with left-skewed growth functions (long left tail) (Hurford et al 2019). The travelling pulse solutions associated with partially sedentary populations in our model are rightskewed, but have higher population densities than a corresponding fully dispersing population.…”

Section: Discussionmentioning

confidence: 99%

Should I Stay or Should I Go: Partially Sedentary Populations Can Outperform Fully Dispersing Populations in Response to Climate-Induced Range Shifts

Cobbold

Stana

2020

Bull Math Biol

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Global mean temperatures have increased by 0.72 • C since the 1950s, and climate warming is resulting in geographical shifts in the range limits of many species. Climate velocity is estimated to be 0.42 km/year, and if a species fails to adapt to the new climate, it must track the location of its climatically constrained niche in order to survive. Dispersal has an important role to play in enabling a population to shift is geographical range limits, but many species are partially sedentary, with only a fraction of the population dispersing each year. We ask, can partially sedentary populations keep pace with climate or will such populations be more vulnerable to extinction? Through the development of a moving-habitat integrodifference equation model, we show that, provided climate velocity is not too large, partially sedentary populations can outperform fully dispersing populations in one of two ways: (i) by persisting at climate speeds where a fully dispersing population cannot, and (ii) exhibiting higher population densities. Moreover, we find that positive density-dependent dispersal can further improve the likelihood a population can persist. Our results highlight the positive role that non-dispersers may play in mitigating the effects of overdispersal and facilitating population persistence in a warming world.

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Skewed temperature dependence affects range and abundance in a warming world

Cited by 18 publications

References 47 publications

Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model

Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model

Small-scale spatial structure influences large-scale invasion rates

Should I Stay or Should I Go: Partially Sedentary Populations Can Outperform Fully Dispersing Populations in Response to Climate-Induced Range Shifts

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