The Philippine Eagle Pithecophaga jefferyi, first discovered in 1896, is one of the world's most endangered eagles. It has been reported primarily from only four main islands of the Philippine archipelago. We have studied it extensively for the past three decades. Using data from 1991 to 1998 as best representing the current status of the species on the island of Mindanao, we estimated the mean nearest‐neighbour distances between breeding pairs, with remarkably little variation, to be 12.74 km (n = 13 nests plus six pairs without located nests, se = ±0.86 km, range = 8.3–17.5 km). Forest cover within circular plots based on nearest‐neighbour pairs, in conjunction with estimates of remaining suitable forest habitat (approximately 14 000 km2), yield estimates of the maximum number of breeding pairs on Mindanao ranging from 82 to 233, depending on how the forest cover is factored into the estimates.

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.

Two pieces of information are minimally required to conserve endangered raptor species — (i) an estimate of its remaining global population, and (ii) the main factors responsible for its decline. Data suggest that no more than 400 adult pairs of the Critically Endangered Philippine Eagle could remain in the wild. As to what is causing population decline, shooting and hunting continue to be the primary factor while forest habitat loss is another. This paper reflects on the growing incident of human-caused deaths in Philippine Eagles, prominently on Mindanao Island where estimates suggest more than half of the eagle’s wild population exists. By analyzing data from eagle rescues, surveys, and field monitoring through radio and satellite tracking techniques, this paper shows that shooting and trapping is a “clear and present” danger which may potentially drive the population to extinction even when suitable forest habitats still exist. Cases of death within the last decade show that the nature and/or extent of law enforcement, conservation education, and population and habitat monitoring fall short of being effective deterrents to eagle persecution in the wild. We review emerging theories on wildlife crime and cases of community-based species conservation to justify a holistic and grounded approach to preventing eagle poaching as an alternative to the conservation status quo.

Many range-restricted taxa are currently experiencing severe population declines yet lack fundamental biological information regarding distribution and population size. Establishing baseline estimates for both these key biological parameters is however critical for directing long-term monitoring and conservation planning for at-risk range-restricted species. The International Union for the Conservation of Nature (IUCN) Red List uses three spatial 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 an ensemble Species Distribution Model (SDM), correlating occurrences with remote-sensing derived environmental covariates, to calculate IUCN range metrics and a global population estimate for the Critically Endangered Philippine Eagle (Pithecophaga jefferyi). Our ensemble-averaged SDM had high predictive accuracy and was able to identify key areas of Philippine Eagle habitat across the species global range. We estimated an AOH = 49,426 km2 and from this metric calculated a maximum EOO = 609,697 km2 and a minimum EOO = 273,794 km2, with an AOO = 54,695 occupied cells. Based on inferred habitat from the AOH metric and territorial habitat area from home range estimates, we provide an updated global population estimate of 677 breeding pairs (range: 549-772 pairs), or 1354 mature individuals, across the entire Philippine Eagle range. We demonstrate that even when occurrence sampling is geographically biased, robust habitat models can be built which enable quantification of IUCN range metrics and a baseline 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, in particular for island endemics facing high extinction risk.

Quantifying home‐range size and habitat resource selection are important elements in wildlife ecology and are useful for informing conservation action. Many home‐range estimators and resource selection functions are currently in use. However, both methods are fraught with analytical issues inherent within autocorrelated movement data from irregular sampling and interpretation of resource selection model parameters to inform conservation management. Here, we apply satellite remote sensing technologies to provide updated estimates of home‐range size and first estimates of fine‐scale resource selection for six adult Philippine Eagles Pithecophaga jefferyi using a space–time autocorrelated kernel density estimate (AKDE) home‐range estimator and non‐parametric resource selection functions. All six adult Eagles showed distinct site fidelity, with continuous range residency between 2 and 18 km, 1 month after tagging. The space–time AKDE home‐range estimators had a median 95% home‐range size = 68 km2 (95% confidence interval (95% CI) 62–74 km2, range: 39–161 km2), with the median 50% core range size = 13 km2 (95% CI 11–14 km2, range 9–33 km2). From the resource selection functions, all adult Philippine Eagles used habitat high in photosynthetic leaf and canopy structure but avoided areas of old‐growth biomass and denser areas of vegetation. This is possibly due to foraging forays into secondary forest and fragmented agricultural areas away from nesting sites. For the first time, we determine two important fine‐scale spatial processes for this Critically Endangered raptor that can help in directing conservation management. Rather than employing traditional home‐range estimators and resource selection functions, we recommend that analysts consider space–time approaches and non‐parametric resource selection functions to animal movement data to explore fully space–time and resource selection.