Climatic niche of the Saker FalconFalco cherrug: predicted new areas to direct population surveys in Central Asia

Abstract

Accurate species distribution data across remote and extensive geographical areas are difficult to obtain. Here, we use bioclimatic envelope models to determine climatic constraints on the distribution of the migratory Saker Falcon Falco cherrug to identify areas in data‐deficient regions that may contain unidentified populations. Sakers live at low densities across large ranges in remote regions, making distribution status difficult to assess. Using presence‐background data and eight bioclimatic variables within a species distribution modelling framework, we applied MaxEnt to construct models for both breeding and wintering ranges. Occurrence data were spatially filtered and climatic variables tested for multicollinearity before selecting best fit models using the Akaike information criterion by tuning MaxEnt parameters. Model predictive performance tested using the continuous Boyce index (B) was high for both breeding (BTEST = 0.921) and wintering models (BTEST = 0.735), with low omission rates and minimal overfitting. The Saker climatic niche was defined by precipitation in the warmest quarter in the breeding range model, and mean temperature in the wettest quarter in the wintering range model. Our models accurately predicted areas of highest climate suitability and defined the climatic constraints on a wide‐ranging rare species, suggesting that climate is a key determinant of Saker distribution across macro‐scales. We recommend targeted population surveys for the Saker based on model predictions to areas of highest climatic suitability in key regions with distribution knowledge gaps, in particular the Qinghai‐Tibet plateau in western China. Further applications of our models could identify protected areas and reintroduction sites, inform development conflicts, and assess the impact of climate change on distributions.

Climatic Constraints on Laggar Falcon (Falco jugger) Distribution Predicts Multidirectional Range Movements under Future Climate Change Scenarios

“…We based final predictor selection on representing seasonal climatic trends, extremes, and limiting environmental factors strongly related theoretically and empirically to species' distributions (Stockwell 2006, Reineking et al 2016, Bradie and Leung 2017, and specifically to distributions of vagile bird species in arid environments (Reside et al 2010). Because current knowledge of Laggar Falcon biology is limited, we selected predictors known to affect avian distributions in arid environments (Sutton and Puschendorf 2018). We selected mean diurnal temperature range as an important predictor for diurnal species in arid environments because variability in daily temperatures likely affects avian survival and population viability (Briga and Verhulst 2015).…”

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

“…Combining both SDMs and ordination is an effective method to define the distributional and ecological constraints of a given species (Chase & Leibold, 2003; Peterson et al., 2011; Soberón & Nakamura, 2009). These methods are particularly useful when using species occurrences generated from biodiversity databases when modeling distributions for species in remote, difficult to survey regions (Peterson, 2001; Rhoden et al., 2017; Sutton & Puschendorf, 2020).…”

White‐faced darter distribution is associated with coniferous forests in Great Britain

“…Species distribution models (SDMs) are a useful tool for the planning of future monitoring programmes (Bourke et al ., 2012) and for identifying priority areas for conservation action (Nazeri et al ., 2012). SDMs are also useful for estimating the distribution of poorly known species (Wilting et al ., 2010), or those which are difficult to survey effectively (Nazeri et al ., 2012) as well as for species which are well known locally but poorly known over wider areas (Sutton & Puschendorf, 2018). A patchy, heterogeneous distribution of monitoring effort and species records can lead to biases in the estimated distribution of species (Millar et al ., 2018).…”