Using Species Distribution Models to Guide Field Surveys for an Apparently Rare Aquatic Beetle

Abstract

Surveying for rare animals can be difficult but using models to predict suitable habitat can guide sampling efforts. We used models to predict suitable habitat for the Narrow-footed Hygrotus Diving Beetle Hygrotus diversipes (diving beetle hereafter), a dytiscid beetle that is known from 10 streams in central Wyoming. The diving beetle was a category-2 Candidate species for listing as Threatened or Endangered in the Endangered Species Act between 1989 and 1996, and was petitioned for listing in 2007, 2008, and 2013. Suitable habitat for the diving beetle was predicted using Maximum Entropy and Random Forest models in Wyoming. Both models predicted that the diving beetle was more likely to occur in intermittent streams with a gentle gradient, shallow water table, variable precipitation pattern, and high soil electrical conductivity, and in the warmest areas of Wyoming. We conducted surveys for the diving beetle at sites where the species had previously been found, and in new areas that were predicted suitable by our models to evaluate whether it is more widely distributed than indicated by previous estimates. We sampled beetles using dip nets and bottle-traps, and assessed water quality at each site. We collected the diving beetle at three sites in central Wyoming between 2010 and 2012 in small, alkaline, intermittent streams with disconnected pools. The aquatic habitat of the diving beetle is dynamic and our results suggest that annual precipitation patterns have a strong influence on the biogeography of this habitat. Our results also suggest that maintaining the hydrologic integrity of prairie streams in Wyoming is vital to the conservation of the diving beetle.

Habitat suitability of neotenic net‐winged beetles (Coleoptera: Lycidae) in China using combined ecological models, with implications for biological conservation

“…In this study, two SDMs, MaxEnt and RF, which are based on the principles of machine‐learning methods, are selected to model the potential distributions of neotenic Lycidae in view of their stable and robust performances with limited presence data (Hernandez et al, 2006; Mi et al, 2017; Williams et al, 2009). In addition, combining the predictions of two SDMs can further help target surveys to locations with high probabilities of presence as predicted by the two algorithms (Tronstad et al, 2018). Therefore, based on the objective evidence between the neotenic Lycidae and the environmental variables obtained by different SDMs, a comprehensive and accurate evaluation system for conservation strategies can be established.…”

“…In addition, combining the predictions of two SDMs can further help target surveys to locations with high probabilities of presence as predicted by the two algorithms (Tronstad et al, 2018). Therefore, based on the objective evidence between the neotenic Lycidae and the environmental variables obtained by different SDMs, a comprehensive and accurate evaluation system for conservation strategies can be established.…”

Predicting the distributions of regional endemic dragonflies using a combined model approach

“…Despite this limitation, two models in particular, maximum entropy (MaxEnt) (Phillips et al ., 2006) and Random Forest (Breiman, 2001), have consistently provided robust estimates for species with limited presence data (Hernandez et al ., 2006; Williams et al ., 2009; Mi et al ., 2017). Combining the predictions of these models may further help target surveys to locations with a high probability of presence as predicted by both algorithms (Tronstad et al ., 2018).…”

Mapping and Modeling the Distribution, Abundance, and Habitat Associations of the Endangered Fat Threeridge in the Apalachicola River System

“…This approach calls for the acquisition of detailed habitat information to study factors influencing species distribution and abundance (assumption-driven research), the development of models linking populations to their habitat (biological planning), and the development of maps predicting patterns in the ecosystem (conservation design ;USFWS 2008b). Such maps and models are commonly used to assess status and threats to species recovery, and to forecast effects of habitat alteration (Germaine et al 2014;Tronstad et al 2018). The 2003 Recovery Plan for Fat Threeridge-a management plan that provides guidance for species recovery-identifies increasing the number of viable subpopulations (i.e., occupied sites) through discovery or reintroduction, and monitoring subpopulations and their habitats, among the six primary objectives for achieving recovery (USFWS 2003).…”