Parasite biodiversity faces extinction and redistribution in a changing climate
Colin J. Carlson,Kevin R. Burgio,Eric R. Dougherty,Anna J. Phillips,Veronica M. Bueno,Christopher F. Clements,Giovanni Castaldo,Tad A. Dallas,Carrie A. Cizauskas,Graeme S. Cumming,Jorge Doña,Nyeema C. Harris,Roger Jovani,Sergey Mironov,Oliver Muellerklein,Heather C. Proctor,Wayne M. Getz,Wayne M. Getz +17 more
TLDR
The most comprehensive spatially explicit data set available for parasites, projected range shifts in a changing climate, and estimated extinction rates for eight major parasite clades is compiled, finding that ectoparasites (especially ticks) fare disproportionately worse than endopar asites.Abstract:
Climate change is a well-documented driver of both wildlife extinction and disease emergence, but the negative impacts of climate change on parasite diversity are undocumented. We compiled the most comprehensive spatially explicit data set available for parasites, projected range shifts in a changing climate, and estimated extinction rates for eight major parasite clades. On the basis of 53,133 occurrences capturing the geographic ranges of 457 parasite species, conservative model projections suggest that 5 to 10% of these species are committed to extinction by 2070 from climate-driven habitat loss alone. We find no evidence that parasites with zoonotic potential have a significantly higher potential to gain range in a changing climate, but we do find that ectoparasites (especially ticks) fare disproportionately worse than endoparasites. Accounting for host-driven coextinctions, models predict that up to 30% of parasitic worms are committed to extinction, driven by a combination of direct and indirect pressures. Despite high local extinction rates, parasite richness could still increase by an order of magnitude in some places, because species successfully tracking climate change invade temperate ecosystems and replace native species with unpredictable ecological consequences.read more
Citations
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Host and parasite thermal ecology jointly determine the effect of climate warming on epidemic dynamics
TL;DR: Experiments and modeling demonstrate that vital rates of a host and parasite respond differently to temperature, with local parasite extinction in the coastal southeastern United States predicted under climate warming, and highlights the need to measure host and parasites thermal performance to predict infection responses to climate change.
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Ecological and Evolutionary Consequences of Parasite Avoidance.
TL;DR: It is suggested that the nonconsumptive effects of parasites might overshadow their consumptive effects, as has been shown for predators.
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Making ecological models adequate
Wayne M. Getz,Wayne M. Getz,Charles R. Marshall,Colin J. Carlson,Luca Giuggioli,Sadie J. Ryan,Sadie J. Ryan,Stephanie S. Romañach,Carl Boettiger,Samuel D. Chamberlain,Laurel G. Larsen,Paolo D'Odorico,David O'Sullivan +12 more
TL;DR: Common issues in ecological modelling are examined and criteria for improving modelling frameworks are suggested and an appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures.
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A global parasite conservation plan
Colin J. Carlson,Skylar R. Hopkins,Kayce C. Bell,Kayce C. Bell,Jorge Doña,Jorge Doña,Stephanie S. Godfrey,Mackenzie L. Kwak,Kevin D. Lafferty,Melinda L. Moir,Melinda L. Moir,Kelly A. Speer,Giovanni Strona,Mark E. Torchin,Chelsea L. Wood +14 more
TL;DR: In this article, a working group identified 12 goals for the next decade that could advance parasite biodiversity conservation through an ambitious mix of research, advocacy, and management, and proposed a strategy to improve parasite biodiversity.
Journal ArticleDOI
Assessing the reliability of species distribution projections in climate change research
Luca Santini,Luca Santini,Luca Santini,Ana Benítez-López,Ana Benítez-López,Luigi Maiorano,Mirza Čengić,Mark A. J. Huijbregts +7 more
TL;DR: In this paper, the authors provide an overview of common modelling practices in the field and assess model predictions reliability using a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and Random Forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions.
References
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Book
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