No abstract

Human-induced climate and land-use change impact species’ habitats and survival ability. A growing body of research uses species distribution models (SDMs) to predict potential changes in species ranges under global change. We constructed SDMs for 411 Chinese endemic vertebrates using Maximum Entropy (MaxEnt) modeling and four shared socioeconomic pathways (SSPs) spanning to 2100. We compared four different approaches: (1) using only climatic and geographic factors, (2) adding anthropogenic factors (land-use types and human population densities), but only using current data to project into the future, (3) incorporating future estimates of the anthropogenic variables, and (4) processing species occurrence data extracted from IUCN range maps to remove unsuitable areas and reflect each species’ area of habitat (AOH). The results showed that the performance of the models (as measured by the Boyce index) improved with the inclusion of anthropogenic data. Additionally, the predicted future suitable area was most restricted and diminished compared to the current area, when using the fourth approach. Overall, the results are consistent with other studies showing that species distributions will shift to higher elevations and latitudes under global change, especially under higher emission scenarios. Species threatened currently, as listed by the IUCN, will have their range decrease more than others. Additionally, higher emission scenarios forecast more threatened species in the future. Our findings show that approaches to optimizing SDM modeling can improve accuracy, predicting more direct global change consequences, which need to be anticipated. We also show that global change poses a significant threat to endemic species even in regions with extensive protected land at higher latitudes and elevations, such as China.

Long-term vegetation plots represent one of the largest types of research investments in ecology, but efforts to interrelate data on plants with that on animals are constrained because of the disturbance produced by human observers. Recent advances in the automated identification of animal sounds on large datasets of autonomously collected audio recordings hold the potential to describe plant-animal interactions, such as between frugivorous birds and fruiting trees, without such disturbance. We deployed an array of nine autonomous recording units (ARUs) on the 400 9 500 m Bubeng Forest Dynamics Plot, in Xishuangbanna, southwest China, and collected a total of 1965 h of recordings across two seasons. Animal Sound Identifier (ASI) software was used to detect the vocalizations of five frugivorous bird species, and the probability of detection was related to the number of mature fruiting trees within a 50 m radius of the ARUs. There were more significant positive relationships than would be expected by chance in analyses that investigated bird/tree interactions across 3 months, both in the wet season and the dry season, as well as in short-term analyses within the dry season months of October and November. The analysis identified 54 interactions between bird and tree species with significant positive relationships. Follow-up observations of birds on the plot validated that such interactions were more likely to be observed than other interactions. We demonstrate that ARUs and automated voice identification can map the distribution and/or movement of vocal animals across large vegetation plots, allowing this data on animals to be inter-related to that on plants. We suggest that ARUs be added to the standardized protocols of the plot network, leveraging their vast amount of information about vegetation to describe plant-animal interactions currently, and monitor changes in the future.