Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands

Comparing expedient and proactive approaches to the planning of protected area networks on Borneo

Protected areas are an important tool for wildlife conservation; however, research is increasingly revealing both biases and inadequacies in the global protected area network. One common criticism is that protected areas are frequently located in remote, high-elevation regions, which may face fewer threats compared to more accessible locations. To explore the conservation implications of this issue, we consider a thought experiment with seven different counterfactual scenarios for the Sunda clouded leopard's conservation on Borneo. This allows us to examine two contrasting paradigms for conservation: "proactive conservation" which prioritises areas with high biodiversity and high risk of development, and "expedient conservation" which focusses on areas with the lowest development risk. We select clouded leopards as our focal species not only because of their emerging conservation importance, but also because, as top predators, they represent both keystone species and ambassadors for wider forest biodiversity. Furthermore, a published analysis of the likely impacts of forest loss in their habitat provides a benchmark for evaluating the modelled outcomes of alternative hypothetical conservation scenarios. We find that, across all metrics, expedient reserve design offered few benefits over the business-as-usual scenario, in contrast to the much greater conservation effectiveness of proactive protected area design. This paper sheds light on the challenging trade-offs between conservation goals and the competing land uses essential for the economic development and well-being of local communities.

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Drivers of Three Most Charismatic Mammalian Species Distribution across a Multiple-Use Tropical Forest Landscape of Sumatra, Indonesia

Tropical Rainforest Heritage sites of Sumatra are some of the most irreplaceable landscapes in the world for biodiversity conservation. These landscapes harbor many endangered Asiatic mammals all suffering multifaceted threats due to anthropogenic activities. Three charismatic mammals in Sumatra: Elephas maximus sumatranus, Pongo abelii, and Panthera tigris sumatrae are protected and listed as Critically Endangered (CR) within the IUCN Red List. Nevertheless, their current geographic distribution remains unclear, and the impact of environmental factors on these species are mostly unknown. This study predicts the potential range of those species on the island of Sumatra using anthropogenic, biophysical, topographic, and climatic parameters based on the ensemble machine learning algorithms. We also investigated the effects of habitat loss from current land use, ecosystem availability, and importance of Indonesian protected areas. Our predictive model had relatively excellent performance (Sørensen: 0.81-0.94) and can enhance knowledge on the current species distributions. The most critical environmental predictors for the distribution of the three species are conservation status and temperature seasonality. This study revealed that more than half of the species distributions occurred in non-protected areas, with proportional coverage being 83%, 72%, and 54% for E.m. sumatranus, P. abelii, and P.t. sumatrae, respectively. Our study further provides reliable information on places where conservation efforts must be prioritized, both inside and outside of the protected area networks, to safeguard the ongoing survival of these Indonesian large charismatic mammals.