Rudolph Scheffer

Complex distribution data can be summarized by grouping species with similar or overlapping distributions to unravel spatial patterns and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data‐driven rigor of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices drive substantial variations in clustering outcomes and performance. To facilitate robust decision‐making, we provide guidance on choosing methods appropriate to one's study objective(s). As a case study, we apply our framework to modeled tree distributions in Borneo and subsequently evaluate the impact of land‐cover change on separate species groupings. Based on the modeled distribution of 390 tree species prior to anthropogenic land‐cover changes, we identified 11 distinct clusters that unraveled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associated species and elucidates the potential of resultant clusters as a spatially explicit framework for investigating distribution‐related questions in ecology, biogeography, and conservation. By adopting our methodological framework and publicly available codes, practitioners can leverage the ever‐growing abundance of distribution data to better understand complex spatial patterns among species distributions and the disparate effects of global changes on biodiversity.

The analysis of climate change impact is essential to include in conservation planning of crop wild relatives (CWR) to provide the guideline for adequate long-term protection under unpredictable future environmental conditions. These resources play an important role in sustaining the future of food security, but the evidence shows that they are threatened by climate change. The current analyses show that five taxa were predicted to have contraction of more than 30 % of their current ranges: Artocarpus sepicanus (based on RCP 4.5 in both no dispersal and unlimited dispersal scenario and RCP 8.5 in no dispersal scenario by 2050), Ficus oleifolia (RCP 4.5 5 in both no dispersal and unlimited dispersal scenario by 2080), Cocos nucifera and Dioscorea alata (RCP 8.5 in both no dispersal and unlimited dispersal scenario by 2050), and Ficus chartacea (RCP 8.5 in both no dispersal and unlimited dispersal scenario by 2050 and 2080). It shows that the climate change impact is species-specific. Representative Concentration Pathways (RCP) of greenhouse gas (GHG) emission and dispersal scenarios influence the prediction models, and the actual future distribution range of species falls in between those scenarios. Climate refugia, holdout populations, and non-analogue community assemblages were identified based on the Protected Areas (PAs) network. PAs capacity is considered an important element in implementing a conservation strategy for the priority CWR. In areas where PAs are isolated and have less possibility to build corridors to connect each other, such as in Java, unlimited dispersal scenarios are unlikely to be achieved and assisted dispersal is suggested. The holdout populations should be the priority target for the ex situ collection. Therefore, by considering the climate refugia, PAs capacity and holdout populations, the goal of keeping high genetic variations for the long-term conservation of CWR in Indonesia can be achieved.

Lithocarpus kostermansii Soepadmo (Fagaceae) is an endemic tree confined to the western part of Java Island, Indonesia. The IUCN Red List classifies the species as Endangered (EN) based on the assessment conducted more than 20 years ago. The species is not known to be present in ex-situ collections. After more than 5 decades since the last herbarium specimen was collected in 1964, here we reported the rediscovery of L. kostermansii in Sawarna, Lebak Regency, Banten Province. The species identity was confirmed by comparing the herbarium and living specimens newly collected with the type specimen image and protologue description. This study contributes new information on the tree morphology through photos of living plants made in the field. It also updates the threats to its natural habitat and provides additional locality information for the species. To enhance the conservation of the species, we collected and cultivated saplings of the species and these are growing in Bogor Botanic Gardens as part of their ex-situ conservation collections. Based on our findings, we proposed a new IUCN Red List assessment for L. kostermansii of EN A2c, B1 + 2ab(i,ii) as it is considered to be facing a very high risk of extinction in the wild due to forest degradation and land conversion.

Vatica venulosa is one of the Dipterocarpaceae species that is threatened with extinction. Distributed in Peninsular Malaysia, Sumatra and Borneo, the tree is classified as Critically Endangered by IUCN Red List (International Union for Conservation of Nature) based on an assessment conducted in 1998. This assessment, however, was only based on the populations in Sumatra and Peninsular Malaysia. Therefore, this study aims to update the global conservation status of V. venulosa using all the occurrence records across the species’ natural distribution. Reduction analysis and geographic range of the species were calculated based on the occurrence data stored in the Global Biodiversity Information Facility and scientific publications. Following IUCN Red List Categories and Criteria, the category Vulnerable (VU A4c) was proposed as the new global conservation status of the species. Under this category, V. venulosa is facing a high risk of extinction in the wild. Therefore, comprehensive conservation action is needed to prevent it from extinction.

Protected areas (PAs) are key tools to prevent extinction and preserve ecosystem functions. As countries reiterated their commitment to expand the reach of PAs by up to 30 % by 2030, stronger purpose and pertinence in the establishment of PAs is needed to ensure effective conservation. In this study, we used Dipterocarpaceae as a proxy for threatened and ecologically important trees to determine the role of PAs in tree conservation and the potential shortfalls at a global scale. We quantified the overlap between the geo-referenced occurrence data of 433 Dipterocarpaceae species and the distribution of global PAs, followed by a conservation gap analysis on Borneo, the center of diversity of the family. We found that while Southeast Asia is the hotspot for species diversity and threat to Dipterocarpaceae, a high proportion of threatened species were found at the range edges of Dipterocarpaceae. Half of all the countries with Dipterocarpaceae met the Aichi Target 11 of designating at least 17 % of their land area as PAs, and most had <10 % of their total number of PAs being relevant to Dipterocarpaceae conservation. Our conservation gap analysis demonstrated that only 5.02 % of the total area of habitat (AOH) of endemic and Critically Endangered dipterocarps was formally protected, while 18.6 % of the total AOH was included in the Heart of Borneo complex. Our data highlights the need for a more effective global conservation gap analysis for threatened trees that could inform area-based conservation post-2020.

Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

Identifying physical and ecological boundaries that limit where species can occur is important for predicting how those species will respond to global change. The island of Borneo encompasses a wide range of habitats that support some of the highest richness on Earth, making it an ideal location for…

Plant invasion has been proved to threaten biodiversity conservation and ecosystem maintenance at a global scale. It is a challenge to project suitable habitat distributions of invasive plant species (IPS) for invasion risk assessment at large spatial scales. Interaction outcomes between native and …

Southeast Asian forests are dominated by the tree family Dipterocarpaceae, whose abundance and diversity are key to maintaining the structure and function of tropical forests. Like most biodiversity, dipterocarps are threatened by deforestation and climate change, so it is crucial to understand the …