THE SOUTHWESTERN NATURALIST 58(4): 435–439
DECEMBER 2013
PREDICTING AND MAPPING THE POTENTIAL DISTRIBUTION OF THE PAINTED DEVIL CRAYFISH, CAMBARUS LUDOVICIANUS FAXON (DECAPODA: CAMBARIDAE) REID L. MOREHOUSE,* MONICA PAPES! ,
AND
MICHAEL TOBLER
Department of Zoology, Oklahoma State University, Stillwater, OK 74078 *Correspondent:
[email protected] ABSTRACT—Oklahoma currently has 29 recognized species of crayfish. We introduce the thirtieth documented species, Cambarus ludovicianus, and use Maxent ecological niche modeling to predict potentially suitable habitat based on known distributions throughout its range. We used the Maxent algorithm with and without points of occurrence in Oklahoma to examine if the niche model predicts suitable habitat in Oklahoma where we found C. ludovicianus. Occurrences in Oklahoma were not predicted present based on the projection of the model using the previously known distribution of the species, except for the location of the Red Slough Wildlife Management Area in southeastern Oklahoma. However, including the new records in the model changes the predicted suitable habitat for the species in Oklahoma, encompassing all new locations and a large portion of eastern Oklahoma. We suggest that eastern Oklahoma and Texas represent the western edge of the range of C. ludovicianus. Further surveys in the field for C. ludovicianus at the limits of its range are required to understand detailed requirements of its habitat. RESUMEN—Oklahoma actualmente tiene 29 especies reconocidas de cangrejo de r´ıo. Presentamos la treintava especie documentada, Cambarus ludovicianus, y usamos el modelaje del nicho ecologico ´ de Maxent para predecir potencial h´abitat adecuado basados en la distribucio´ n conocida de la especie en todo su rango. Usamos el Maxent con y sin los sitios de ocurrencia en Oklahoma para examinar si el modelo del nicho predice h´abitat adecuado en Oklahoma donde encontramos C. ludovicianus. No se predijeron las ocurrencias de verdad en Oklahoma basados en la proyeccio´ n del modelo usando la distribucion ´ previamente conocida de la especie, excepto por el a´ rea de Red Slough Wildlife Management Area en el sudeste de Oklahoma. Sin embargo, al incluir los nuevos registros al modelo cambia el h´abitat adecuado pronosticado para la especie en Oklahoma, abarcando todas las nuevas localidades y una gran porcion ´ del este de Oklahoma. Sugerimos que el este de Oklahoma y Texas representan el l´ımite occidental de la distribucio´ n de C. ludovicianus. Futuros muestreos de campo para C. ludovicianus en los bordes de su rango son requeridos para entender los requisitos detallados de su h´abitat. In the past decade, the ecology and status of crayfish throughout the USA and especially Oklahoma has received increasing attention. Currently, 29 species in six genera are documented from Oklahoma (Taylor et al., 2004; Jones et al., 2005; Robison and McAllister, 2006). Diversity of crayfish in Oklahoma follows a west-to-east gradient with the majority of species occurring in the southeastern and eastern portions of the state. In Oklahoma, crayfish are comprised of multiple endemic species including two endangered species that occur in caves (Graening and Fenolio, 2005; Graening et al., 2006). The known crayfish in Oklahoma has expanded in recent years due to the increasing focus of research and efforts for conservation. Our own sampling efforts have resulted in the discovery of the painted devil crayfish (Cambarus ludovicianus) that was previously unrecorded in the state (Table
1). Morphological features of the crayfish, including the annulus ventralis, gonopods, and color patterns were consistent with the original description of C. ludovicianus by Faxon (1884). Furthermore, the collected specimens were clearly distinct from C. diogenes (devil crayfish) with the outer edge of the first abdominal segment being straight as opposed to curved (Taylor and Schuster, 2004). The new state records prompted the question of whether the occurrence of C. ludovicianus was predictable based on the known range of the species and, if so, what the potential distribution of the species in Oklahoma is. In recent years, ecological niche modeling (ENM) has been utilized to analyze the ecological requirements of species based on points of known occurrence and to predict the potential suitability of habitats and the distribution of the species (Warren and Seifert, 2011). Predictive models of spatial distributions of species are
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Table 1—Known occurrences of Cambarus ludovicianus in Oklahoma. Date of collection October 2005 March 2011 June 2011 June 2011
Location White Oak Creek, McCurtain County Cedar Creek, State Highway 259, 22km N of Broken Bow Norwood Creek, Red Slough Wildlife Management Area Norwood Creek, Red Slough Wildlife Management Area
important for a variety of applications in ecology and conservation (Graham et al., 2004). For example, ENMs have been used to forecast distributions to understand impacts of climatic change (Kharouba et al., 2009; Yates et al., 2010), predict the potential distribution of invasive species (Ward, 2007), and predict species richness and diversity at various spatial scales (Graham and Hijmans, 2006). Ecological niche modeling has only recently been applied to crayfish (Olden et al., 2006), which occupy multiple geographic regions and various habitats and are receiving increasing attention regarding their conservation (Taylor et al., 2007). In this study, we investigated factors influencing the distribution of C. ludovicianus by using a maximumentropy-ENM algorithm. We used 16 environmental variables to determine potential suitable habitat. With the new points of occurrence in Oklahoma, which extend the known distribution of C. ludovicianus westward, we were particularly interested in whether the records of occurrence in Oklahoma were ecologically similar to points of occurrence from the known range. MATERIALS AND METHODS—Cambarus ludovicianus is a member of the subgenus Lacunicambarus, a group of primary burrowers, and was first described by Faxon in 1884 as a subspecies of C. diogenes. Hobbs (1989) recognized C. ludovicianus as a distinct species and rejected the original assignment as a subspecies of C. diogenes. The species occurs from marshy lowlands to hilly pine forests. There, it can be found along edges of streams and along ditches during late spring and summer (Walls, 2009). As a primary burrower, which can occupy habitats without permanent flowing or standing water, C. ludovicianus can dig into the ground to the water table and construct characteristic chimneys at the entrance of the burrow. Burrows can be !2 m deep with multiple lateral tunnels extending into roots of trees and into other protected areas. Specimens with typical color patterns of painted devil crayfish have been collected from eastern Texas to Mississippi and up the Mississippi River valley as far north as Tennessee and Kentucky (Taylor and Schuster, 2004). In terms of conservation, C. ludovicianus appears to be currently stable (Taylor et al., 2007), and the Nature Conservancy considers C. ludovicianus to be secure (ranked G5 globally and N5 nationally, NatureServe, 2011; http://www.natureserve.org/). To model the ecological niche of C. ludovicianus, we obtained 69 records of occurrence nationwide from the databases of the Illinois Natural History Survey and the Global Biodiversity
Collector
Latitude
Longitude
Sex
M. Tobler and C. Lukhaup R. Morehouse
3483 0 3.96 00 N
9583 0 7.56 00 W
Female
34813 0 7.68 00 N
94846 0 47.28 00 W
Female
R. Morehouse
33842 0 48.60 00 N
94846 0 28.08 00 W
R. Morehouse
33845 0 18.72 00 N
94837 0 40.80 00 W
Male and female Female
Information Facility (GBIF), along with the four occurrences we documented in Oklahoma. Coordinates of latitude and longitude for records lacking them were determined with GEOLocate v.3.22. We considered 24 environmental variables as potential predictors of the distribution of C. ludovicianus. The variables included are commonly used in ENM for aquatic organisms (Dominguez-Dominguez et al., 2006; Chen et al., 2007; Costa and Schlupp, 2010). Nineteen bioclimatic variables were obtained from the WorldClim database at a 0.8-km2 spatial resolution (http://www.worldclim.org/bioclim.htm), four hydrological variables were obtained from the United States Geological Survey (HYDRO1k dataset; http://eros.usgs.gov/#/ Find_Data/Products_and_Data_Available/gtopo30/hydro/ namerica) at a 1-km2 resolution, and one variable describing type of soil from the Harmonized World Soil Database (http:// www.iiasa.ac.at/Research/LUC/External-World-soil-database/ HTML/) at a 1 km2 spatial scale. All environmental variables were resampled to 1 km2 resolution. To reduce redundancy in the environmental variables (because some environmental variables can be highly correlated), we used the Principal Components tool in the ArcGIS v.10 Spatial Analyst extension to assemble a correlation matrix for the 24 variables across our spatial extent of analysis. We retained only a single variable for variables that were correlated at r > 0.9, preferentially choosing variables that measured extremes over those that measured averages (Shepard and Burbrink, 2008). We chose to retain variables describing extremes of environmental conditions because they are more likely to set the limits of the range of organisms due to physiological constraints (Kozak and Wiens, 2006). This procedure reduced the initial dataset to 16 variables (11 WorldClim, 4 Hydro1k, and 1 Harmonized World Soils). Reducing the number of variables to those considered ecologically relevant and nonredundant makes hypothesis testing and interpretation of results more straightforward (Elith et al., 2011). Moreover, using fewer variables decreases the potential for model over-fitting (Warren and Seifert, 2011). To model the ecological niche of C. ludovicianus, we used the method of maximum-entropy (Maxent)-distribution modeling, which has been found to be the most conservative relative to other methods in regards to model overfitting (Elith et al., 2006). Maxent estimates the probability distribution for an occurrence of a species based on environmental constraints (Phillips et al., 2006). The environmental constraints are derived from the environmental variables inputted into the model in relation to the known points of occurrence of the species. Maxent requires only species-presence data (no absence data) and environmental variables (continuous or categorical)
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layers for the given study area. We used Maxent software, version 3.3.3e (http://www.cs.princeton.edu/~schapire/maxent/), which produces a probability estimate of the presence of a species that varies from 0–1, 0 being the lowest and 1 the highest probability. Validation is necessary to assess the predictive performance of the model. Ideally, an independent dataset should be used for validation; however, in many cases, including this study, an independent dataset is not available. Consequently, we followed the most commonly used approach, which is to partition the data randomly into training and testing sets, thus creating quasi-independent data for validation of models (Guisan and Thuiller, 2005). We used 80% of the data points to build the model and the remaining 20% of the data points for validation of models. To evaluate performance of models, we used a receiver-operating-characteristic (ROC) analysis, which plots sensitivity (y-axis, lack of omission error) against 1specificity (x-axis, commission error). Omission error is defined as known presences that are predicted absent, and commission error is defined as locations predicted suitable for which no presences are known. The area under the ROC curve (AUC), which is an indicator of model prediction accuracy, was calculated. The AUC ranges from 0.5 (random assignment of presences and absences) to a maximum value of 1.0 (perfect discrimination of presences and absences). The analysis was conducted for the testing dataset (20% of the data points) to assess the average performance of the resulting models with a fixed threshold of 0.10 (10% omission error), which rejects the lowest 10% of possible predicted values. We obtained two separate models using the method described for each model; one excluding the points of occurrence in Oklahoma to test whether suitable habitat would be predicted within Oklahoma, and one including the points of occurrence in Oklahoma to further predict potential suitable habitat within Oklahoma given the new state records. We also used the program ENMtools (Warren, 2010) to compare the two niche models for similarity and overlap. We used a test introduced by Warren et al. (2008) that investigates whether two ENMs are identical (niche similarity) or no more similar than expected if localities are sampled at random from the environmental background (background similarity test). The test is based on Schoener’s D, which is a metric quantifying niche overlap between two taxa. To test for background similarity, we contrasted the Schoener’s D-values obtained by comparing projections of ENM to a distribution of Schoener’s D values obtained with100 simulations comparing the ENMs generated using actual localities from the extended range (including Oklahoma) to ENMs generated from samples drawn randomly from the range occupied by the species (excluding Oklahoma).
RESULTS—The AUC values from the test datasets were high for both models of the distribution of C. ludovicianus analyzed (0.889 for model without points of collection in Oklahoma, and 0.855 for the model with points of collection in Oklahoma), suggesting high predictive power of the models. Most suitable habitat for C. ludovicianus was predicted to occur along the major river systems that meander through the study area (without, Fig. 1a, and with, Fig. 1b, points of occurrence in Oklahoma). The model without points of occurrence in Oklahoma did not predict any suitable habitat in Kansas or Oklahoma with the exception of the most southeastern
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corner of Oklahoma. Two of the new locations in Oklahoma were not predicted as suitable by the model that did not include occurrences in Oklahoma in the training dataset. The model with the new points of occurrence in Oklahoma had a predicted range reaching further west and including the eastern part of Oklahoma and a portion of southeastern Kansas. In the model without occurrences in Oklahoma, mean temperature in the driest quarter contributed the most to the model, with precipitation of the coldest quarter second, while the model with occurrences in Oklahoma had precipitation of the coldest quarter contributing the most and soil second (Table 2). Soil contributed ca. 15% to the prediction in both models, while precipitation of the coldest quarter had two times as much contribution in the model with occurrences in Oklahoma (Table 2). Schoener’s D was 0.82 indicating that the models were similar to each other and the background tests (I) supported this result with an approximate P-value of 0.612 – 0.026 SD suggesting that the models are not significantly different. DISCUSSION—Our study provides the newest addition to crayfish in Oklahoma and presents the potential distribution of a new species of crayfish at the western edge of its known range. Although C. ludovicianus is only known from four locations in Oklahoma, the niche model predicted a broader distribution in the state. Because C. ludovicianus is a primary burrowing crayfish, collection of this species can be rather difficult and labor-intensive, and the current distribution of this species may be underestimated simply due to the lack of adequate sampling. Results from the ENM that excluded the points of occurrence in Oklahoma in the training dataset did not predict suitable habitat for C. ludovicianus outside of the southeasternmost section of McCurtain County (e.g., Red Slough Wildlife Management Area). Including the points of occurrence in Oklahoma in the training dataset indicated that a large portion of eastern Oklahoma and even southeastern Kansas was potentially suitable for C. ludovicianus. Based on the ENMs, eastern Oklahoma and Texas likely represent the western edge of the range for C. ludovicianus. Patterns of precipitation in Oklahoma and Texas exhibit a pronounced east-to-west gradient (with precipitation decreasing westwards), and this may directly limit the range of the species to the west. When we included the localities from Oklahoma into the model, the environmental variables that contributed the most to the model changed. This is likely due to the localities in Oklahoma being environmentally distinct enough that they lead to a different estimation of the niche of the species. This may be expected at the edge of the distribution of a species. The two ENMs predicted similar ranges and were not significantly different with the exception of the western edge of the distribution of C. ludovicianus. Previous studies indicated that, as populations approach their
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FIG. 1—Predicted potential suitable habitat for Cambarus ludovicianus a) without and b) with the occurrences in Oklahoma in the model-training dataset. Open circles are known locations of C. ludovicianus. Darker colors represent higher habitat-suitability values based on climatic and hydrologic variables.
ecological limits, they become more fragmented and sparse (Brown et al., 1995). This suggests that, as we approach the edge of the range of a species, habitat suitability is increasingly variable. Our results also suggest that incorporating more points of occurrence to the model may cause changes in the predicted extent of the range of a species and that additional area of occurrence may overall significantly change the predicted range of the species. The results from this study highlight potential locations where C. ludovicianus may be found within Oklahoma, and we suggest that additional sampling is
required to determine the distribution of C. ludovicianus in the eastern portions of Oklahoma. It is important to note, however, that even though the ENM predicted suitable habitat throughout eastern Oklahoma, biogeographic barriers, such as the Ouachita Mountains located just to the north of our points of collection, may have prevented C. ludovicianus from colonizing potentially suitable habitat further north in Oklahoma and Kansas. We thank D. Arbour for his guidance to a known location of C. ludovicianus in Oklahoma. We also thank C. Lukhaup and L. Bergey for their assistance during the first discovery of this
Table 2—Selected environmental variables and their percentage of contribution in Maxent models for the predicted distribution of Cambarus ludovicianus without and with occurrences in Oklahoma. Contribution (%) Environmental variable
Without occurrences in Oklahoma
With occurrences in Oklahoma
Mean temperature of driest quarter Precipitation of coldest quarter Elevation Soil Slope Precipitation of wettest quarter Aspect Mean temperature of warmest quarter Mean temperature of wettest quarter Mean diurnal range Maximum temperature of warmest month Precipitation of driest quarter Precipitation of warmest quarter Minimum temperature of coldest month Topographic index Mean temperature of coldest quarter
32.1 24.1 16.1 15.4 2.8 2.7 2.0 1.9 1.2 1.2 0.2 0.2 0.1 0.1 0.0 0.0
8.2 44.0 9.1 14.6 1.7 6.8 1.0 8.3 3.2 1.0 0.8 0.3 0.8 0.2 0.1 0.0
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