Improving area of occupancy estimates for parapatric species using distribution models and support vector machines

Biodiversity modeling advances will improve predictions of nature's contributions to people

Accurate predictions of ecosystem functions and nature's contributions to people (NCP) are needed to prioritize environmental protection and restoration in the Anthropocene. However, our ability to predict NCP is undermined by approaches that rely on biophysical variables and ignore those describing biodiversity, which have strong links to NCP. To foster predictive mapping of NCP, we should harness the latest methods in biodiversity modeling. This field advances rapidly, and new techniques with promising applications for predicting NCP are still underutilized. Here, we argue that employing recent advances in biodiversity modeling can enhance the accuracy and scope of NCP maps and predictions. This enhancement will contribute significantly to the achievement of global objectives to preserve NCP, for both the present and an unpredictable future.

A review on the status and modeling of suitable habitats of the southern white-cheeked gibbon

The southern white-cheeked gibbon Nomascus siki is endemic to Indochina and is classified as critically endangered on the International Union for Conservation of Nature (IUCN) Red List. The most updated information on the status of this species dates back to a decade ago. As hunting has tremendous impacts on wildlife in Southeast Asia, the population of N. siki might have changed a lot in the last decade. Updated information on the status and potential distribution of this species is critically important for conservation and prioritization, especially for N. siki because of its undefined distribution range. The goal of this study was to review the population status of N. siki in Vietnam and Lao People's Democratic Republic (PDR) and to model its potential distribution. In Vietnam, this species has been intensively surveyed in all major areas of occurrence from 2016 to 2021. The total number of N. siki groups recorded and estimated in Vietnam were 324 and 483, respectively. In Lao PDR, the occurrence of N. siki has been confirmed in Nam Kading, Nakai Nam Theun, Hin Nam No, and Phou Hinpoun national protected areas. However, population estimates are generally lacking. The suitable habitat of N. siki was predicted from about 105.00° to 106.80° E longitude and from about 16.60° to 17.90° N latitude located in Quang Binh and Quang Tri provinces (Vietnam), and Khammounan and Savannakhet provinces (Lao PDR). The area of the potential distribution range is about 9894.15 km², both in Vietnam and Lao PDR. Particularly, the high, medium, and low suitable habitats were estimated at around 1229.58 km², 3019.68 km², and 5644.89 km², respectively. The area of suitable habitat of N. siki in Vietnam was predicted to be 4151.25 km², of which only 1257.93 km² (30.30%) is in the protected area network. Dong Chau-Khe Nuoc Trong and Bac Huong Hoa Nature Reserves, and Phong Nha-Ke Bang National Park should receive priority for conservation of N. siki in Vietnam. Improving conservation beyond the protected areas' boundaries or transforming the forest enterprises and watershed protection forests into protected areas should also be considered as an alternative for the conservation of N. siki. In Lao PDR, surveys of the species in its entire distribution range should be the first priority.

Integrating habitat-masked range maps with quantifications of prevalence to estimate area of occupancy in IUCN assessments

Estimates of species geographic ranges constitute critical input for biodiversity assessments, including those for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. Area of occupancy (AOO) is one metric that IUCN uses to quantify a species' range, but data limitations typically lead to either under- or overestimates (and unnecessarily wide bounds of uncertainty). Fortunately, existing methods in which range maps and land-cover data are used to estimate the area currently holding habitat for a species can be extended to yield an unbiased range of plausible estimates for AOO. Doing so requires estimating the proportion of sites (currently containing habitat) that a species occupies within its range (i.e., prevalence). Multiplying a quantification of habitat area by prevalence yields an estimate of what the species inhabits (i.e., AOO). For species with intense sampling at many sites, presence-absence data sets or occupancy modeling allow calculation of prevalence. For other species, primary biodiversity data (records of a species' presence at a point in space and time) from citizen-science initiatives and research collections of natural history museums and herbaria could be used. In such cases, estimates of sample prevalence should be corrected by dividing by the species' detectability. To estimate detectability from these data sources, extensions of inventory-completeness analyses merit development. With investments to increase the quality and availability of online biodiversity data, consideration of prevalence should lead to tighter and more realistic bounds of AOO for many taxonomic groups and geographic regions. By leading to more realistic and representative characterizations of biodiversity, integrating maps of current habitat with estimates of prevalence should empower conservation practitioners and decision makers and thus guide actions and policy worldwide.

Forest cover and geographical distance influence fine-scale genetic structure of leaf-toed geckos in the tropical dry forests of western Mexico

The biodiversity within tropical dry forests (TDFs) is astounding and yet poorly catalogued due to inadequate sampling and the presence of cryptic species. In the Mexican TDF, endemic species are common, and the landscape has been continually altered by geological and anthropogenic changes. To understand how landscape and environmental variables have shaped the population structure of endemic species, we studied the recently described species of leaf-toed gecko, Phyllodactylus benedettii, in coastal western Mexico. Using double-digest restriction site-associated DNA sequencing data, we first explore population structure and estimate the number of ancestral populations. The results indicate a high degree of genetic structure with little admixture, and patterns corresponding to both latitudinal and altitudinal gradients. We find that genetic structure cannot be explained purely by geographical distance, and that ecological corridors may facilitate dispersal and gene flow. We then model the spatial distribution of P. benedettii in the TDF through time and find that the coastline has been climatically suitable for the species since the Last Glacial Maximum. Landscape genetic analyses suggest that the combination of isolation by distance (IBD) and isolation by resistance (IBR; forest cover) has influenced the spatial genetic structure of the species. Overall, our genomic data demonstrate fine-scale population structure in TDF habitat, a complex colonization history, and spatial patterns consistent with both IBD and other ecological factors. These results further highlight the Mexican TDF as a diversity hotspot and suggest that continued anthropogenic changes are likely to affect native fauna.

Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice

Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.