Enhanced Migratory Waterfowl Distribution Modeling by Inclusion of Depth to Water Table Data

Abstract

In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.

Potential breeding distributions of U.S. birds predicted with both short‐term variability and long‐term average climate data

“…Waterbirds and water foraging species are tied to seasonally variable and ephemeral wetland habitats (Kreakie et al. ), where local weather processes such as precipitation and evapotranspiration directly affect habitat quality in the short‐term (Poiani et al. ).…”

“…However, we did find that short-term variability models predicted true absences for waterbirds and water foraging species better. Waterbirds and water foraging species are tied to seasonally variable and ephemeral wetland habitats (Kreakie et al 2012), where local weather processes such as precipitation and evapotranspiration directly affect habitat quality in the short-term (Poiani et al 1995). These species community dynamics can be influenced by extreme events such as high precipitation events, which can reduce water quality for waterbirds, lowering habitat quality for these species (Studds et al 2012).…”

Climate effects on the distribution of wetland habitats and connectivity in networks of migratory waterbirds

“…Our results show the geographic displacement of the relative contribution of the patches for network connectivity, identifying a latitudinal threshold beyond which a sudden collapse of the system is possible. The implications for conservation might be serious, in view of the high dependence of migratory waterbirds on aquatic habitats (Kreakie et al, 2012).…”

“…This can result in a reduction of their functional capacity, or the shifting of their geographic location (Erwin, 2008) with significant impacts on the migratory fauna, especially birds (Robinson et al, 2009). Waterbirds include a large group of species whose distribution and persistence are intimately linked with the presence, availability and quality of water bodies, as well as by the seasonal conditions depending on the local resource peaks (Kreakie et al, 2012). Seasonal contraction and alteration of hydrological regimes can influence migrants, by reducing their migratory tendency or by expanding the range of resident species (Fiedler, 2003), thus increasing the mismatch between trophic levels (Both et al, 2006).…”

Effect of climate change on halophytic grasslands loss and its impact in the viability of Gopherus flavomarginatus

“…In most of the studies, on large spatial scales, only climatic variables have been used for predicting spatial distribution of the species (Araújo and Peterson 2012;Anadón et al 2015). In some cases, dealing with local spatial scales, soil and orography variables have been included (Guisan and Hofer 2003;Pearson et al 2004;Anadón et al 2007;Marini et al 2010;Kreakie et al 2012), for example, the dependence of herbivores specialized on some plants (Kissling et al 2007). Nevertheless, it is very difficult to determine spatial data of biological interactions; and for this reason the studies where the interactions are used to assess the distribution area of the species are very scarce (Pearson and Dawson 2003).…”