Uncertainty of future projections of species distributions in mountainous regions

Integrated framework for dynamic conservation of bamboo forest in giant panda habitat under climate change

Climate change presents formidable challenges to forest biodiversity and carbon storage. Bamboo forests will be affected particularly in Southwest China's mountainous regions. Bamboo serves as not only a key food resource and habitat for giant panda Ailuropoda melanoleuca but also a potential carbon sink due to its rapid energy-to-matter conversion capability. We employ the MaxEnt model to project the distribution shifts of 20 giant panda foraged bamboo species in Sichuan Province under future climate scenarios, utilizing climate data of 30m resolution. Based on the changes in the diversity and distribution area of bamboo communities caused by climate change, the changing of giant pandas' food resources and the carbon storage of bamboo forests were calculated. The results indicated that the area of bamboo communities is projected to expand by 17.94%-60.88% more than now by the end of the 21st century. We analyzed the energy balance between the dietary needs of giant pandas and the energy provided by bamboo. We predicted that bamboo communities from 2000 to 2150 could support the continuous growth of the giant panda population (6533 wild individuals by 2140-2150 in an ideal state in Sichuan province). However, the species diversity and carbon storage of bamboo forests face out-of-sync fluctuations, both temporally and spatially. This is a critical issue for subalpine forest ecosystem management under climate change. Therefore, we propose a dynamic conservation management framework for giant panda habitats across spatial and temporal scales. This framework aims to facilitate the adaptation of subalpine forest ecosystems to climate change. This innovative approach, which integrates climate change into the conservation strategy for endangered species, contributes a conservation perspective to global climate action, highlighting the interconnectedness of biodiversity preservation and climate mitigation.

Gaps and opportunities in modelling human influence on species distributions in the Anthropocene

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no 'rule of thumb' for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

Accounting for niche truncation to improve spatial and temporal predictions of species distributions

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.

Of pandas, fossils, and bamboo forests: ecological niche modeling of the giant panda (Ailuropoda melanoleuca) during the Last Glacial Maximum

The giant panda (Ailuropoda melanoleuca) is the most basal living species in the phylogeny of the family Ursidae, with a specialized diet composed of a variety of bamboo species. The evolutionary history and past distribution patterns of the giant panda remain poorly understood. Our aim was to integratively apply distinct methods to evaluate the evolutionary history and distributional patterns of the giant panda; these included phylogeography, ecological niche modeling (ENM), and fossil data. To this end, we characterized the panda’s past and present ecological niches and the environmental conditions that define them. To estimate the panda’s phylogeographic patterns and the environmental conditions (precipitation and temperature) available across its historical geographic range, we evaluated its past distribution during the Last Glacial Maximum (LGM). Considering that modeling biotic interactions (e.g., foraging, predation) is still an enormous challenge, we propose a novel modeling strategy based on the panda’s specialized diet, using an ensemble of three bamboo genera with distribution across the panda’s historical geographic range. Finally, we tested the accuracy of our approach by evaluating its ability to predict the LGM fossils. Our results support that the panda’s diversification across its distribution happened ca. 2.7 million years (Mya), coinciding with the likely period when the panda changed from a carnivorous to a vegetarian diet (from the Pleistocene to the Pliocene), acquiring its exclusively bamboo-feeding habits until the mid-Holocene. Our findings provide evidence of a historical directional niche change along which the panda has currently reached the lower limits of temperature and precipitation conditions existing on the geography where its food is available. Our proposed ENM based on the panda’s food habits accurately predicted 85.7% of the LGM fossils, in stark contrast with the traditional approach of modeling the distribution of species by using exclusively its own occurrences. These results provide insights on how to include Eltonian components to undertake more robust ENM when only abiotic variables are available. We emphasize the importance of integrating fossil information, whenever available, into the niche modeling process to include the historical component when estimating species ecological niches.

Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas

Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.

Red squirrel (Sciurus vulgaris) habitats change modelling in Eastern Europe in the scope of climate change according to new generation scenarios (SSPs) by 2100

In Ukraine during 2008–2010, the first SDM matched the red squirrel (S. vulgaris) based on GLM-by-2050, and which covered Eastern Europe, was developed and used by the Land and Resource Management Center (ULRMC, Kyiv). Our new study reveals further development of the analysis by using relevant IPCC climate change scenarios. We took into account materials on S. vulgaris (and S. anomalus) distribution, as well as selected associated species, and the WorldClim with respective maps and current bioclimatic variables, and its projections for four relevant scenarios which combined SSPs & RCPs by 2100. The simulations of scenario SSP1 & RCP2.6 associated with an average temperature increase of 1.5 °C show that climate change could cause the loss of 12 % of suitable habitats of the species in Eastern Europe and 49 % in Ukraine. The simulations for SSP2 & RCP4.5 (with average temperature increase of 1.8 °C) demonstrates, respectively, a potential loss of 14 % and 57 % of suitable habitats. Simulations of SSP3 & RCP7.0 and SSP5 & RCP8.5 scenario (with average temperature increase of >> 2 °C) shows a loss of 30 % and 41 % of suitable habitats within Eastern Europe, and more than 90 % in Ukraine. Since each percent of such changes provokes enormous losses in ecosystems and biodiversity, we emphasize the current need for countries to aim and achieve the most ambitious climate change commitments to stabilize the increase of temperature, i.e. within 1.5 °C. Our comparison platform included also SDMs of some trees (oak, beech, spruce, pine, linden, and birch — Quercus robur, Fagus sylvatica, Picea abies, Pinus silvestris, Tilia cordata, Betula spp.), as well as SDM for the marten (Martes martes), for all of which we had already developed GLM-by-2050. Consequently, the new projections demonstrated that all habitats of the red squirrel and associated species are expected to shift mostly ‘to the north’ by 2100, and their localities in the Caucasus Mountain areas might be fragmented. Most likely, in nature, this complicated displacement revealed by the mentioned modelling will happen not in the form of direct migration of individuals ‘to the north’ directly, but through active synanthropization of the red squirrel. How durable and satisfactory this mechanism is for natural selection remains a mystery. The territories from which S. v. ukrainicus (Mygulin, 1928) historically originated and was described have changed significantly: the respective landscape ecosystem losses have reached up to 50 % and more. By 2100, significant habitat changes are likely to be also demonstrated by beech and birch. This research can be used by educators in teaching the history of science, applied ecology, nature conservation, and geoinformatics in biology. This research is dedicated to the Squirrel Year 2020.

One hundred years of climate change in Mexico

Spatial assessments of historical climate change provide information that can be used by scientists to analyze climate variation over time and evaluate, for example, its effects on biodiversity, in order to focus their research and conservation efforts. Despite the fact that there are global climatic databases available at high spatial resolution, they represent a short temporal window that impedes evaluating historical changes of climate and their impacts on biodiversity. To fill this gap, we developed climate gridded surfaces for Mexico for three periods that cover most of the 20^th and early 21^st centuries: t₁-1940 (1910–1949), t₂-1970 (1950–1979) and t₃-2000 (1980–2009), and used these interpolated surfaces to describe how climate has changed over time, both countrywide and in its 19 biogeographic provinces. Results from our characterization of climate change indicate that the mean annual temperature has increased by nearly 0.2°C on average across the whole country from t₂-1970 to t₃-2000. However, changes have not been spatially uniform: Nearctic provinces in the north have suffered higher temperature increases than southern tropical regions. Central and southern provinces cooled at the beginning of the 20^th century but warmed consistently since the 1970s. Precipitation increased between t₁-1940 and t₂-1970 across the country, more notably in the northern provinces, and it decreased between t₂-1970 and t₃-2000 in most of the country. Results on the historical climate conditions in Mexico may be useful for climate change analyses for both environmental and social sciences. Nonetheless, our climatology was based on information from climate stations for which 9.4–36.2% presented inhomogeneities over time probably owing to non-climatic factors, and climate station density changed over time. Therefore, the estimated changes observed in our analysis need to be interpreted cautiously.

Interactive spatial scale effects on species distribution modeling: The case of the giant panda

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species' habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.