Distribution modelling to guide stream fish conservation: an example using the mountain sucker in the Black Hills National Forest, USA

Abstract

ABSTRACT1. Conservation biologists need tools that can utilize existing data to identify areas with the appropriate habitat for species of conservation concern. Regression models that predict suitable habitat from geospatial data are such a tool. Multiple logistic regression models developed from existing geospatial data were used to identify largescale stream characteristics associated with the occurrence of mountain suckers (Catostomus platyrhynchus), a species of conservation concern, in the Black Hills National Forest, South Dakota and Wyoming, USA.2. Stream permanence, stream slope, stream order, and elevation interacted in complex ways to influence the occurrence of mountain suckers. Mountain suckers were more likely to be present in perennial streams, and in larger, higher gradient streams at higher elevations but in smaller, lower gradient streams at lower elevations.3. Applying the logistic regression model to all streams provided a way to identify streams in the Black Hills National Forest most likely to have mountain suckers present. These types of models and predictions can be used to prioritize areas that should be surveyed to locate additional populations, identify stream segments within catchments for population monitoring, aid managers in assessing whether proposed forest management will potentially have impacts on fish populations, and identify streams most suitable for stream rehabilitation and conservation or translocation efforts.4. When the effect of large brown trout (Salmo trutta) was added to the best model of abiotic factors, it had a negative effect on the occurrence of mountain suckers. Negative effects of brown trout on the mountain sucker suggest that management of recreational trout fisheries needs to be balanced with mountain sucker conservation in the Black Hills. However, more spatially explicit information on brown trout abundance would allow managers to understand where the two species interact and where recreational fisheries need to be balanced with fish conservation.

A Synopsis of Global Mapping of Freshwater Habitats and Biodiversity: Implications for Conservation

“…For the US, a comparable dataset is the NHD plus system (1:24K scale), which provides climate, hydrology, and land-use information summarized within the entire upstream network above each stream reach. Many freshwater species distribution modeling efforts have utilized the NHDplus data (1:24k) and architecture because of topological connectivity and habitat predictors offered by the resource [102][103][104][105][106][107] ( Figure 11). Although NHDplus is a convenient database to support freshwater species distribution modeling, it does not adequately represent 1st order streams, the majority of which provide habitat for freshwater taxa (Figure 11).…”

Identification and Implementation of Native Fish Conservation Areas in the Upper Colorado River Basin

“…To avoid any biases associated with the survey data, and to fill in information gaps, we used spatially explicit predictions of probability of occurrence (sensu Dauwalter and Rahel 2008) for roundtail chub, flannelmouth sucker, and bluehead sucker to estimate likelihood of occurrence in unsampled areas ( Figure 2). Spatially explicit predictions were made using artificial neural network models developed to model species presence-absence as a function of landscapescale variables (Dauwalter et al 2011).…”

Integrating catchment properties in small scale species distribution models of stream macroinvertebrates

“…Recent conservation efforts in the terrestrial realm have relied on species distribution models (SDMs) to deliver insights into the relationship between biodiversity and its environment, as well as predictions of suitable habitat for endangered species (Elith and Leathwick, 2009). SDMs have recently been implemented in freshwater ecosystems to identify vulnerability to climate change (Domisch et al, 2013a,b), determine loss of genetic diversity (Bálint et al, 2011) and to guide conservation measures (Dauwalter and Rahel, 2008).…”