The influence of coarse‐scale environmental features on current and predicted future distributions of narrow‐range endemic crayfish populations

Abstract

Summary 1. A major limitation to effective management of narrow‐range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2. Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate‐change scenarios. 3. The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 65–87% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4. Current models created using two spatial resolutions (1 and 4.5 km2) showed that fine‐resolution data more accurately represented current distributions. For three of the four species, the 1‐km2 resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1‐km2 resolution models were more accurate than 4.5‐km2 resolution models. 5. Future projected (4.5‐km2 resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low‐emission scenario, whereas two of four species would be severely restricted in range under moderate–high emissions. Discrepancies in the two emission scenarios probably relate to the exclusion of behavioural adaptations from species‐distribution models. 6. These model predictions illustrate possible impacts of climate change on narrow‐range endemic crayfish populations. The predictions do not account for biotic interactions, migration, local habitat conditions or species adaptation. However, we identified the constraining landscape features acting on these populations that provide a framework for addressing habitat needs at a fine scale and developing targeted and systematic monitoring programmes.

Predicting probability of occurrence and factors affecting distribution and abundance of three Ozark endemic crayfish species at multiple spatial scales

“…The importance of various local‐ and landscape‐scale variables was species‐ and spatial scale dependent. This is in accordance with a previous multiscale crayfish modelling study that found that finer grain data (1 versus 4.5 km 2 ) yielded more accurate distribution models and predictions (Dyer et al ., ). However, even their finer‐resolution data were relatively coarse compared to the local‐scale variables used in the present study.…”

“…Rock fragment volume is therefore a geologic measure of the percentage of rock in the soil, and not a measure of streambed particle size. The percentage of rocky soils is important to smallmouth bass in Missouri (Brewer et al ., ), as are soils and geology to crayfish distribution in the Ozarks Highlands and Ouachita Mountains (Westhoff et al ., ; Dyer et al ., ). It is unclear in our study and previous studies if the link between soils, geology and narrow‐range endemic crayfish distribution is due to direct or indirect effects.…”

“…(, ) found that heavy metals mining in the Ozark Highlands of Missouri negatively impacted crayfish survival. Others have shown that agricultural activity in the Ozark Highlands can be negatively or positively related to crayfish species presence (Westhoff, Rabeni & Sowa, ) and that soils and geology can be strongly related to crayfish distribution in the Interior Highlands (Westhoff et al ., ; Dyer et al ., ). In New Zealand streams, crayfish production did not differ between forested and agricultural streams, although crayfish growth and longevity did differ between the two stream types (Parkyn, Collier & Hicks, ).…”

Backcasting the decline of a vulnerable Great Plains reproductive ecotype: identifying threats and conservation priorities

“…Distributions are assumed to represent adaptive significance to a species and as such SDMs have been used extensively to predict distributions across terrestrial, freshwater, and marine landscapes (Elith & Leathwick, ). Understanding a species' distributions can drive conservation planning (Liang et al ., in press; Maloney et al ., ), identify areas of conservation importance (Moilanen, ), highlight factors related to species decline (e.g., Anadón et al ., ; Lötters et al ., ), and predict future distributions due to anthropogenic disturbance (Dyer et al ., ). We provide a basis for future conservation efforts for the Arkansas River shiner by quantitatively mapping the current and historic range of the species.…”

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

“…Aquatic ecosystems are hierarchically structured, with terrestrial landscape features constraining physicochemical processes and biotic structure at finer scales (Frissell, Liss, Warren, & Hurley, ). For example, hierarchical structuring of habitat use has been observed across a range of aquatic biota (e.g., fish, Brewer, Rabeni, Sowa, & Annis, ; Worthington, Brewer, Grabowski, & Mueller, ; crayfish, Dyer, Brewer, Worthington, & Bergey, ; and freshwater mussels, McRae, Allan, & Burch, ; Shea, Peterson, Conroy, & Wisniewski, ). The flow regime (i.e., timing, rate of change, duration, frequency, and magnitude of stream discharge) is also a product of the surrounding landscape (e.g., land use and topography) and largely dictates channel formation and ecological processes at multiple spatial and temporal scales (Poff, ).…”