The past and future human impact on mammalian diversity - PubMed (original) (raw)
The past and future human impact on mammalian diversity
Tobias Andermann et al. Sci Adv. 2020.
Abstract
To understand the current biodiversity crisis, it is crucial to determine how humans have affected biodiversity in the past. However, the extent of human involvement in species extinctions from the Late Pleistocene onward remains contentious. Here, we apply Bayesian models to the fossil record to estimate how mammalian extinction rates have changed over the past 126,000 years, inferring specific times of rate increases. We specifically test the hypothesis of human-caused extinctions by using posterior predictive methods. We find that human population size is able to predict past extinctions with 96% accuracy. Predictors based on past climate, in contrast, perform no better than expected by chance, suggesting that climate had a negligible impact on global mammal extinctions. Based on current trends, we predict for the near future a rate escalation of unprecedented magnitude. Our results provide a comprehensive assessment of the human impact on past and predicted future extinctions of mammals.
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
Figures
Fig. 1. Different time periods of diversity decline and extinction rate increases between areas and orders.
The plots show the declining diversity (black lines, 100 modeled extinction dates for each species) and the magnitude of extinction rate increases relative to the starting rate (red lines, mean values) through time, for all spatial (A to H) and two examples of taxonomic subsets (I to J) analyzed in this study. Extinction rates were estimated with a Bayesian rate-shift model, inferring the timing, number, and magnitude of shifts in extinction rates from the extinction dates of each subset. We calculated the mean marginal rates (harmonic mean) separately for all shift number models, which were supported by more than 10% posterior probability (table S3). The rate-shift model that was best supported by the data is shown in solid red, while the transparent red lines show the second-best model, if present. All rate estimates are transformed and plotted as the magnitude of extinction rate increase relative to the base value 126 ka ago. Note that the extinction rate axis (right, in red) is plotted in logarithmic space for better visibility. The time axis to the left of the solid vertical black line (0 CE) is plotted in units of ka before present (BP), while the time axis to the right of 0 CE is plotted in years CE in logarithmic space for better visibility of recent rate changes. Vertical columns shaded in green mark the times of first human arrival (if applicable).
Fig. 2. Higher model adequacy for human correlation model compared to climate model.
The displayed models can be grouped into correlation models (A to C) in which extinction rates are estimated as a function of time continuous predictors and a rate-shift model (D) with a distinct and limited number of rate changes, estimated solely from the extinction dates dataset. The applied correlation variables were global human population density (A) and global mean temperature (B), as well as the interaction of the two in a mixed model (C). Shown for each model are the time-continuous predictor trajectories (black, top; only for correlation models), the estimated rates through time (red, middle; mean values and 95% HPD) and the simulated mammal species diversity based on the estimated rates (green, bottom). The accuracy scores in the bottom of the lower panels reflect how accurately the respective model predicted past extinctions and was calculated from the MAPE scores of each model (see. fig. S4). The input for the mixed model (C) included the product of human population density and global temperature, as well as each of these variables individually. See fig. S5 for further correlation models. The time axis is scaled in ka before present (BP).
Fig. 3. Substantial species losses predicted by year 2100 CE.
The subplots show the estimates of mammalian species diversity globally (A) and for all spatial (B to H) and taxonomic subsets (I to T) analyzed in this study. The colored violin plots represent density plots of the 95% CI of diversity predictions based on the simulation scenarios “IUCN threat status prediction” (IUCN, red), “present extinction rate prediction” (PR, yellow), and “human population model prediction” (HU, green). In the first scenario (IUCN), we simulated extinctions based on the current threat statuses of species, applying extinction probabilities associated with these statuses. In the second scenario (PR), we applied current extinction rates as estimated from past extinction data. In the third scenario [only for spatial data subsets (A to H)], we simulated future extinction based on the correlation factor estimated for human population density combined with future human population predictions for different areas. The horizontal dashed lines show the current species diversity of each group. Note that the y axes only show a subsection of possible diversity values and do not include 0.
Fig. 4. Expected increases in extinction rates for most orders and areas.
In structure equivalent to Fig. 3, the violin plots (A to T) show the 95% HPD interval density of estimated extinction rates in the year 2100 based on the different diversity prediction scenarios. The IUCN rates (red) were estimated from simulated future extinctions based on the IUCN threat status of species in each subset. These extinction rate predictions are consistently higher than the present rates (PR) estimated from recent extinctions (yellow). For several spatial subsets (B,C,E,F, and H) we predict rate increases based solely on human population size increases (HU, green). Rates were estimated applying a shift model as implemented in PyRate. The multimodality of some rate distributions reflects the model uncertainty of the applied shift model.
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