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

Enhanced risk assessment framework integrating distribution dynamics, genetically inferred populations, and morphological traits of Diploderma lizards

Assessing the threat status of species in response to global change is critical for biodiversity monitoring and conservation efforts. However, current frameworks, even the IUCN Red List, often neglect critical factors such as genetic diversity and the impacts of climate and land-use changes, hindering effective conservation planning. To address these limitations, we developed an enhanced extinction risk assessment framework using Diploderma lizards as a model. This framework incorporates long-term field surveys, environmental data, and land-use information to predict distributional changes for 10 recently described Diploderma species on the Qinghai-Xizang Plateau, which hold ecological significance but remain underassessed in conservation assessment. By integrating the distribution data and genetically inferred effective population sizes ( Ne), we conducted scenario analyses and used a rank-sum approach to calculate Risk ranking scores (RRS) for each species. This approach revealed significant discrepancies with the IUCN Red List assessments. Notably, D. yangi and D. qilin were identified as facing the highest extinction risk. Furthermore, D. vela, D. batangense, D. flaviceps, D. dymondi, D. yulongense, and D. laeviventre, currently classified as "Least Concern", were found to warrant reclassification as "Vulnerable" due to considerable threat from projected range contractions. Exploring the relationship between morphology and RRS revealed that traits such as snout-vent length and relative tail length could serve as potential predictors of extinction risk, offering preliminary metrics for assessing species vulnerability when comprehensive data are unavailable. This study enhances the precision of extinction risk assessment frameworks and demonstrates their capacity to refine and update risk assessments, especially for lesser-known taxa.

Software codesign between end users and developers to enhance utility for biodiversity conservation

Creating software tools that address the needs of a wide range of decision-makers requires the inclusion of differing perspectives throughout the development process. Software tools for biodiversity conservation often fall short in this regard, partly because broad decision-maker needs may exceed the toolkits of single research groups or even institutions. We show that participatory, collaborative codesign enhances the utility of software tools for better decision-making in biodiversity conservation planning, as demonstrated by our experiences developing a set of integrated tools in Colombia. Specifically, we undertook an interdisciplinary, multi-institutional collaboration of ecological modelers, software engineers, and a diverse profile of potential end users, including decision-makers, conservation practitioners, and biodiversity experts. We leveraged and modified common paradigms of software production, including codesign and agile development, to facilitate collaboration through all stages (including conceptualization, development, testing, and feedback) to ensure the accessibility and applicability of the new tools to inform decision-making for biodiversity conservation planning.

Update of the geographic range of Humboldt's squirrel monkeys (Saimiri cassiquiarensis cassiquiarensis Lesson, 1840) using a model-based approach

Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model-based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO, as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation.