We used species distribution models which envisage potentially suitable habitat for species based on variables supportive to species occurrences [33 (link)]. Numerous model types are available for developing SDMs; however, maximum entropy (e.g., Maxent) is one of most effective tools for species having limited spatial presence-only data [34 ,35 (link),36 (link)]. Maximum entropy forecasts the species probability distribution of random events with uniform and high stability [37 (link),38 (link)].
We determined potential habitat layers by first selecting 19 bioclimatic variables at 30 arc-second (about 1 km2) resolution from Worldclim [39 (link)] and extracted aspect from a digital elevation model (about 1-km2 resolution; Shuttle Radar Topography Mission (SRTM);https://ita.cr.usgs.gov , accessed on 28 November 2022) (Supplementary Table S1 ). We selected eight environmental variables including mean annual temperature (Bio_1), mean diurnal range (Bio_02), iso-thermality (Bio_3), annual precipitation (Bio_12), precipitation of driest month (Bio_14), precipitation seasonality (Bio_15), precipitation of coldest quarter (Bio_19), and aspect (see Figure 2 ) based on jackknife analysis, which uses a leave-one-out approach to estimate variable importance (Supplementary Materials Table S1; Figure S1 ). We excluded highly correlated (|r| > 0.70) variables to reduce the potential for model overfitting [40 (link)]. We used maximum entropy modeling software (Maxent version 3.3.3 K) [36 (link)] to estimate the potential nesting distribution of Egyptian vultures.
We ran Maxent models using 75% presence data for calibration and the remaining 25% for model validation [41 (link)] using the bootstrapping procedure with 10 replications and a maximum of 500 iterations. We used the default settings for the number of background samples and auto features for model construction. We evaluated model validation and accuracy using the area under the curve (AUC) of the receiver operating characteristics [42 ]. However, because of criticism in the use of AUC for model evaluation [43 (link)], we also evaluated model performance using the true skill statistic (TSS), sensitivity, and specificity [44 (link)]. The TSS values range from −1 to 1, with values approaching 1 indicating good model performance and values <0 indicating performance no better than random. We converted habitat into binary form (suitable or unsuitable) using the 10th-percentile training presence logistic threshold [45 (link)] and used the corresponding raster layer to identify suitable potential nesting habitat for Egyptian vultures.
We used predicted suitable nesting habitat to identify whether the model correctly predicted nesting habitat as well as existing factors influencing the occurrence of Egyptian vulture nests along the Madi River corridor from Damauli, Tanahu to Tanting, Kaski. This area supports abundant Egyptian vultures and we assumed this abundance reflected their residence in this area, where cliffs and forests occur near human settlements. We established 35 stations at 3-km intervals along 135 km of the Madi River. Each station was 500 m × 500 m and within each we established five 100 m × 100 m plots, four at the corners and one at the center. During October 2020–February 2021, we searched each plot for 3 h, recording the occurrence of Egyptian vulture nests and the presence of Egyptian vultures roosting or feeding. We recorded the occurrence of feeding vultures while sitting quietly at the corner of each plot, and for observations of nesting and roosting we searched throughout the plots. From the center of each plot we recorded the elevation using GPS, and measured the distance to the nearest forest, area of agricultural land, water, and human settlement. We measured the nearest distance to human settlement using a GIS if the area had more than one house and were used for living.
We determined potential habitat layers by first selecting 19 bioclimatic variables at 30 arc-second (about 1 km2) resolution from Worldclim [39 (link)] and extracted aspect from a digital elevation model (about 1-km2 resolution; Shuttle Radar Topography Mission (SRTM);
We ran Maxent models using 75% presence data for calibration and the remaining 25% for model validation [41 (link)] using the bootstrapping procedure with 10 replications and a maximum of 500 iterations. We used the default settings for the number of background samples and auto features for model construction. We evaluated model validation and accuracy using the area under the curve (AUC) of the receiver operating characteristics [42 ]. However, because of criticism in the use of AUC for model evaluation [43 (link)], we also evaluated model performance using the true skill statistic (TSS), sensitivity, and specificity [44 (link)]. The TSS values range from −1 to 1, with values approaching 1 indicating good model performance and values <0 indicating performance no better than random. We converted habitat into binary form (suitable or unsuitable) using the 10th-percentile training presence logistic threshold [45 (link)] and used the corresponding raster layer to identify suitable potential nesting habitat for Egyptian vultures.
We used predicted suitable nesting habitat to identify whether the model correctly predicted nesting habitat as well as existing factors influencing the occurrence of Egyptian vulture nests along the Madi River corridor from Damauli, Tanahu to Tanting, Kaski. This area supports abundant Egyptian vultures and we assumed this abundance reflected their residence in this area, where cliffs and forests occur near human settlements. We established 35 stations at 3-km intervals along 135 km of the Madi River. Each station was 500 m × 500 m and within each we established five 100 m × 100 m plots, four at the corners and one at the center. During October 2020–February 2021, we searched each plot for 3 h, recording the occurrence of Egyptian vulture nests and the presence of Egyptian vultures roosting or feeding. We recorded the occurrence of feeding vultures while sitting quietly at the corner of each plot, and for observations of nesting and roosting we searched throughout the plots. From the center of each plot we recorded the elevation using GPS, and measured the distance to the nearest forest, area of agricultural land, water, and human settlement. We measured the nearest distance to human settlement using a GIS if the area had more than one house and were used for living.
Free full text: Click here
