Integrating intraseasonal grassland dynamics in cross‐scale distribution modeling to support waterbird recovery plans

Abstract

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water‐vegetation dynamics into a cross‐scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel‐2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water‐vegetation dynamic models, based exclusively on Sentinel‐2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water‐vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost‐effective recovery planning by monitoring management effectiveness from space.

Caution Is Needed When Using Niche Models to Infer Changes in Species Abundance: The Case of Two Sympatric Raptor Populations

“…Another issue that can constrain our ability to infer population dynamics from ENMs is the interannual fluctuations in prey availability. In this regard, the inclusion of remotely sensed ecosystem functional variables (e.g., annual mean of NDVI or NDWI as a proxy for primary productivity [56], food resources [57] or prey availability [58]) into ENMs has been found to improve model predictions [13,15,58,59].…”

“…Thus, SRS data have increasingly been used as predictor variables for both species distribution and abundance models in recent years [13,14]. For instance, the incorporation of SRS variables related to water and carbon cycles (e.g., the normalized difference water and vegetation indices (NDWI and NDVI, respectively)) has recently been reported to be critical for model-assisted monitoring of endangered plant and animal species [13,15]. These studies suggest that calibrating ENMs with SRS variables would enable the accurate prediction of species distributions at local scales (across space and time).…”

The Dynamic Change of Bosten Lake Area in Response to Climate in the Past 30 Years

“…On the basis of six factors (annual average precipitation, annual average temperature, annual average sunshine hours, annual average evaporation, annual average relative humidity, and annual average wind speed) [38][39][40][41] this paper selected meteorological data at intervals of 5 years from Water 2020, 12, 4 7 of 23 1988-2018, and then built meteorological databases of drought season and wet season, respectively. Finally, the meteorological factors of Bosten Lake Basin were obtained through factor interpolation calculation of five meteorological stations by ordinary kriging interpolation algorithm [39,[41][42][43]. The statistics are shown in Table 3.…”

“…For the noise problem existing in a binary image extracted by the threshold segmentation algorithm, this study selected the median filter [43] and the open-close algorithm [44][45][46][47][48] to achieve noise removal. The median filter was a nonlinear signal processing technique that was used to reduce noise effectively based on sorting statistical theory [43].…”

“…For the noise problem existing in a binary image extracted by the threshold segmentation algorithm, this study selected the median filter [43] and the open-close algorithm [44][45][46][47][48] to achieve noise removal. The median filter was a nonlinear signal processing technique that was used to reduce noise effectively based on sorting statistical theory [43]. Its basic principle is that the intermediate value is replaced by the digital value of one point or the neighboring value of point in a sequence of numbers, thereby eliminating isolated noise points by making the surrounding pixel values closing to true value.…”