Dataset Information

Spatial dynamics of Chinese Muntjac related to past and future climate fluctuations.

ABSTRACT: Climate fluctuations in the past and in the future are likely to result in population expansions, shifts, or the contraction of the ecological niche of many species, and potentially leading to the changes in their geographical distributions. Prediction of suitable habitats has been developed as a useful tool for the assessment of habitat suitability and resource conservation to protect wildlife. Here, we model the ancestral demographic history of the extant modern Chinese Muntjac Muntiacus reevesi populations using approximate Bayesian computation (ABC) and used the maximum entropy model to simulate the past and predict the future spatial dynamics of the species under climate oscillations. Our results indicated that the suitable habitats for the M. reevesi shifted to the Southeast and contracted during the Last Glacial Maximum, whereas they covered a broader and more northern position in the Middle Holocene. The ABC analyses revealed that the modern M. reevesi populations diverged in the Middle Holocene coinciding with the significant contraction of the highly suitable habitat areas. Furthermore, our predictions suggest that the potentially suitable environment distribution for the species will expand under all future climate scenarios. These results indicated that the M. reevesi diverged in the recent time after the glacial period and simultaneously as its habitat's expanded in the Middle Holocene. Furthermore, the past and future climate fluctuation triggered the change of Chinese muntjac spatial distribution, which has great influence on the Chinese muntjac's population demographic history.

SUBMITTER: Sun Z

PROVIDER: S-EPMC8489110 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Most studies on the biological effects of future climatic changes rely on seasonally aggregated, coarse-resolution data. Such data mask spatial and temporal variability in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to the conditions that organisms experience in the wild. Here, I present the methods for providing fine-grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales. Observed climate data and spatially coherent probabilistic projections of daily future weather were disaggregated to hourly and used to drive empirically calibrated physical models of thermal and hydrological microclimates. Mesoclimatic effects (cold-air drainage, coastal exposure and elevation) were determined from the coarse-resolution climate surfaces using thin-plate spline models with coastal exposure and elevation as predictors. Differences between micro and mesoclimate temperatures were determined from terrain, vegetation and ground properties using energy balance equations. Soil moisture was computed in a thin upper layer and an underlying deeper layer, and the exchange of water between these layers was calculated using the van Genuchten equation. Code for processing the data and running the models is provided as a series of R packages. The methods were applied to the Lizard Peninsula, United Kingdom, to provide hourly estimates of temperature (100 m grid resolution over entire area, 1 m for a selected area) for the periods 1983-2017 and 2041-2049. Results indicated that there is a fine-resolution variability in climatic changes, driven primarily by interactions between landscape features and decadal trends in weather conditions. High-temporal resolution extremes in conditions under future climate change were predicted to be considerably less novel than the extremes estimated using seasonally aggregated variables. The study highlights the need to more accurately estimate the future climatic conditions experienced by organisms and equips biologists with the means to do so.

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Spatial dynamics of Chinese Muntjac related to past and future climate fluctuations.

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