Geographic range estimates and environmental requirements for the harpy eagle derived from spatial models of current and past distribution

Abstract

Understanding species-environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. However, for many species, this baseline information does not exist. With reliable presence data, spatial models that predict geographic ranges and identify environmental processes regulating distribution are a cost-effective and rapid method to achieve this. Yet these spatial models are lacking for many rare and threatened species, particularly in tropical regions. The harpy eagle (Harpia harpyja) is a Neotropical forest raptor of conservation concern with a continental distribution across lowland tropical forests in Central and South America. Currently, the harpy eagle faces threats from habitat loss and persecution and is categorized as Near-Threatened by the International Union for the Conservation of Nature (IUCN). Within a point process modeling (PPM) framework, we use presence-only occurrences with climatic and topographical predictors to estimate current and past distributions and define environmental requirements using Ecological Niche Factor Analysis. The current PPM prediction had high calibration accuracy (Continuous Boyce Index = 0.838) and was robust to null expectations (pROC ratio = 1.407). Three predictors contributed 96% to the PPM prediction, with Climatic Moisture Index the most important (72.1%), followed by minimum temperature of the warmest month (15.6%) and Terrain Roughness Index (8.3%). Assessing distribution in environmental space confirmed the same predictors explaining distribution, along with precipitation in the wettest month. Our reclassified binary model estimated a current range size 11% smaller than the current IUCN range polygon. Paleoclimatic projections combined with the current model predicted stable climatic refugia in the central Amazon, Guyana, eastern Colombia, and Panama. We propose a data-driven geographic range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements.

Keywords: Harpia harpyja; Neotropical raptors; geographic range size; harpy eagle; point process models; species distribution models.

Figure 1

Predicted current distribution for the harpy eagle with values closer to 1 having highest environmental suitability. Gray borders represent national borders and internal state boundaries for Argentina, Brazil, and Mexico. Black points define harpy eagle occurrences

Figure 2

Reclassified binary range prediction for the harpy eagle using 10% training presence (10TP = 0.415) threshold. Khaki area is the suitable environmental space above the 10TP threshold, white areas not suitable. Red polygons define current IUCN range for the harpy eagle. Gray borders represent national borders and internal state boundaries for Argentina, Brazil, and Mexico. Blue points define harpy eagle occurrences

Figure 3

Response curves for predictors used in the current distribution model for the harpy eagle

Figure 4

Distribution of harpy eagle occurrences in selected pairs of environmental variables. Gray points are random background environmental points, and red points are harpy eagle occurrences. Black hashed line defines the minimum convex polygon of harpy eagle occurrences

Figure 5

Ecological Niche Factor Analysis (ENFA) for suitable harpy eagle environment space (khaki) within the available background environment (gray) shown across the marginality (x) and specialization (y) axes. Arrow length indicates the magnitude with which each variable accounts for the variance on each of the two axes. Red circle indicates niche position (median marginality) relative to the average background environment (the plot origin)

Figure 6

Predicted climate stability for the harpy eagle summed from the current, Last Glacial Maximum (LGM, ~22,000 years ago) and Mid‐Holocene (~6,000 years ago) predictions. Values of −2 indicate species absence, −1 to 0 shows colonizable areas, 0 to 1 defines areas of highest stability, and values of 2 (dark red patches) show the most unstable areas. Map defines summed prediction masked to current geographic extent and geo‐political boundaries