Priority conservation areas and a global population estimate for the critically endangered Philippine Eagle

Abstract

Many range‐restricted taxa are experiencing population declines, yet we lack fundamental information regarding their distribution and population size. Establishing baseline estimates for both of these key biological parameters is however critical for directing conservation planning for at‐risk range‐restricted species. The International Union for the Conservation of Nature (IUCN) Red List uses three range metrics that define species distributions and inform extinction risk assessments: extent of occurrence (EOO), area of occupancy (AOO) and area of habitat (AOH). However, calculating all three metrics using standard IUCN approaches relies on a geographically representative sample of locations, which for rare species is often spatially biased. Here, we apply model‐based interpolation using Species Distribution Models (SDMs), correlating occurrences with remote‐sensing covariates, to calculate IUCN range metrics, protected area coverage and a global population estimate for the Critically Endangered Philippine Eagle (Pithecophaga jefferyi). Our final range wide continuous SDM had high predictive accuracy (continuous Boyce Index = 0.934) and when converted to a binary model estimated an AOH as 28 624 km2, a maximum EOO as 617 957 km2, and a minimum EOO as 275 459 km2, with an AOO as 53 867 km2. Based on inferred habitat from the AOH metric, we estimate a global population of 392 breeding pairs (range: 318–447 pairs), or 784 mature individuals, across the Philippine Eagle global range. Protected areas covered 32% of AOH, 13% less than the target representation, with the continuous model identifying key habitat as priority conservation areas. We demonstrate that even when occurrences are geographically biased, robust habitat models can quantify baseline IUCN range metrics, protected area coverage and a population size estimate. In the absence of adequate location data for many rare and threatened taxa, our method is a promising spatial modelling tool with widespread applications, particularly for island endemics facing high extinction risk.

Extensive protected area coverage and an updated global population estimate for the Endangered Madagascar Serpent-eagleEutriorchis astur

“…Even though unstructured occurrence data can have sampling bias (Beck et al 2014), opportunistically collected presence-only data are often the only location data available and generally sample beyond the extent of the smaller spatial scale of systematic surveys , Sutton & Puschendorf 2020. Thus, when used in conjunction with a modelling framework designed to account for inherent spatial biases unstructured data can fill distributional knowledge gaps (Rhoden et al 2017, Sutton et al 2023b). However, obtaining further occurrences would be useful for improving our predictions and updating the baseline biological parameters set out here.…”

“…We followed the spatial modelling framework of Sutton et al (2023b) and converted the final range-wide continuous prediction into a binary threshold prediction which we term model area of habitat (AOH), to be distinct from the standard IUCN AOH methodology (Brooks et al 2019). To calculate model AOH in suitable pixels we reclassified the continuous prediction to a binary threshold using all pixel values equal to or greater than maximising the sum of sensitivity and specificity (maxTSS) from the continuous model prediction.…”

“…Species Distribution Models (SDMs) are a group of geospatial statistical methods that assess species-habitat associations and predict distribution based on correlating environmental covariates with species occurrences (Matthiopoulos et al 2020). SDMs can be effective in estimating potential range limits and ecological associations using satellite remote-sensing data coupled with occurrences from unstructured surveys and community science projects (Bradter et al 2018); this includes data for threatened species distributed across remote, hard to survey areas (Sutton et al 2021b(Sutton et al , 2023b.…”

“…Improving the predictive power of spatial models by incorporating biotic, landcover, and topographical predictors derived from satellite remote sensing would also lead to higher certainty on where to designate new protected areas, strengthening the existing protected area network (Elith and Leathwick 2009). Applying this knowledge to current conservation management can then direct designation of protected areas in line with suitable environmental areas (Sutton et al 2023b). Given this background, our aims were to apply spatial predictive modelling to estimate distribution and identify ecological range limits for the Madagascar Serpent-eagle.…”

Space–time home‐range estimates and resource selection for the Critically Endangered Philippine Eagle on Mindanao

“…This large eagle is endemic to four islands in the Philippine archipelago (Mindanao, Leyte, Samar and Luzon; Kennedy 1977), with a restricted distribution across lowland and montane tropical forests (Salvador & Ibañez 2006, Sutton et al . 2023). The latest global population estimate from inferred habitat area has indicated a potential breeding population of 392 pairs, with 59% expected to be resident on Mindanao (Sutton et al .…”