Corresponding author: Catherine S. Jarnevich ( email@example.com )
Academic editor: Tim Blackburn
© 2017 Catherine S. Jarnevich, Nicholas E. Young, Trevor R. Sheffels, Jacoby Carter, Mark D. Sytsma, Colin Talbert.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Jarnevich CS, Young NE, Sheffels TR, Carter J, Sytsma MD, Talbert C (2017) Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: an example with coypu (Myocastor coypus). NeoBiota 32: 107-125. https://doi.org/10.3897/neobiota.32.8884
Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.
Ecophysiological model, correlative model, coypu, nutria, climate change
Understanding species distributions and forecasting potential distributional changes with changing climates is a common goal in ecology. Invasive species provide a unique opportunity to evaluate factors controlling distribution using introduction information to evaluate different hypotheses. Species distribution models (SDM) have a wide range of applications ranging from conservation to invasive species management. There are several different approaches to developing SDMs, including mathematical models, defined a priori, that causally relate a species presence to the environment, and statistical models based on direct correlations between observations of the species and the environment (
Correlative models assume that the species being modeled is in equilibrium with its environment, that the current distribution represents basic habitat requirements of the species, and that these requirements are constant through time (
The coypu (Myocastor coypus [Molina, 1782]) is a large, semi-aquatic, invasive rodent native to South America south of 23° latitude (
The coypu is a generalist herbivore, with a diet that includes all types of plant material, including leaves, stems, roots, and bark (
Using coypu as a test case, we examined and compared the utility of using a very simplistic ecophysiological based model versus a correlative model to predict current and future coypu distribution. We used independent regional and global distribution information to validate the two approaches. Specifically, our objectives were to: 1) evaluate the relationship between known physiological limitations and geographical distribution, 2) evaluate a hypothesized physiological limitation using native range and introduction success information, 3) predict future distribution based on climate change scenarios, and 4) evaluate the benefit of using both modeling approaches. Given the economic and ecological impacts of coypu in invaded ranges, secondary objectives were to develop a current model of potential suitable habitat for coypu within the USA and globally and to investigate possible distribution changes under potential climate change to inform management strategies.
Global occurrence records for coypu were downloaded from the Global Biodiversity Information Facility (GBIF; gbif.org; March 4, 2011). The data were inspected, and records with a resolution greater than 30 minutes, our model resolution, were removed due to accuracy issues. We also removed presence locations in countries or states with a status of never established or extinct, retaining only those with a status of country of origin, escape or release, range expansion, or eradicated as defined by a global review of coypu distribution (
For the USA, we used monthly mean, minimum and maximum temperature data at a 4-km spatial resolution between 2003 and 2007 for our analyses (PRISM Group 2007). This time frame is biologically meaningful in that it matches the average lifespan of an individual, and is data-driven in that it matches the time frame of the sub-watershed scale (hydrologic unit code [HUC] 12) data used to validate the model in the Pacific Northwest, USA. Global environmental data were obtained from WorldClim (
We developed ecophysiological based models at the continental USA and the global scale, based on known physiological constraints on coypu. This species has known winter temperature tolerances that are thought to be the primary limiting factors on their distribution, at least in temperate regions (
For the continental USA we developed two different ecophysiological based models using monthly climate data from the PRISM data set at 4-km resolution; one using a five-year period (2003 to 2007) and another using a three-year period (2005 to 2007), hereafter referred to as US 5yr and US 3yr. We used two different time periods to assess the importance of inter-annual climatic variability on predicted distribution. We calculated the number of months within each time period that had a minimum temperature of less than 0 °C and a maximum temperature of less than 5 °C. Given the negative relationship between coypu populations and sequences of freezing days, we defined any month with average values meeting these criteria as unsuitable for coypu survival. To address the water limitation we developed a layer of arid locations by identifying locations in the USA with annual precipitation less than 250 mm, based on PRISM average annual precipitation from 2003 to 2007.
For the global ecophysiological model, we used WorldClim monthly data averaged from 1950 to 2000 at a 30 arc second resolution (~1 km), hereafter referred to as Global 50yr. Unsuitable environments were defined as locations with any month meeting the criteria of average minimum temperature less than 0 °C and average maximum temperature less than 5 °C. We again masked out arid regions, defined as areas with annual precipitation less than 250 mm based on the WorldClim average annual precipitation layer.
We used the VisTrails software (
USA state assessment of the five models. The assessment includes the number of USA states classified with at least some suitable (1) or no suitable (0) coypu (Myocastor coypus [Molina, 1782]) habitat for each coypu status class (never established/ extinct, present, no data, or eradicated) as defined by
The number of environmental variables from the global WorldClim data set used in the GLM was limited to six based on the known physiology of coypu and included mean diurnal range, maximum temperature of the warmest month, minimum temperature of the coldest month, annual precipitation, precipitation seasonality, and precipitation of warmest quarter. Environmental variables were reduced by removing one of each pair of highly correlated environmental variables (maximum of Spearman rank coefficient, Pearson’s product moment or Kendall tau rank; |r| > 0.7 following the recommendation of
Using a threshold defined as maximizing sensitivity plus specificity divided by two, we created binary predictions of suitable and unsuitable habitat for the correlative models.
The models were evaluated using zonal statics at two scales; sub-watershed hydrologic unit code (HUC12) and the USA state boundaries. Standardized spatial surveys completed by on-the-ground fish and wildlife biologists for Oregon and Washington provided coypu density estimates at the HUC12 level and were used as an independent model validation (
We evaluated the global models using two additional methods. Similar to the state level evaluation, we used country level zonal statistics compared to the coypu status identified by
We applied our ecophysiological based rule-set to future climate data. We obtained historic data from the Maurer data set (
Model correct classification by coypu density class. Numbers represent the percent of sub-watersheds (Hydrologic Unit Code 12s) in Washington and Oregon classified as suitable by each model (generalized linear models [GLM]; row) and coypu density class (>100, 11–100, 1–10, and 0 individuals; column).
For the ecophysiological based models, we produced layers with the number of months for each cell that did not meet the required temperature criteria. For the US 5yr model, the number of months with unsuitable temperature conditions ranged from 0 to 41, while for the US 3yr model the maximum number of unsuitable months was 28.
The GLM country model retained all six environmental variables in model fitting, while the GLM targeted model dropped average annual precipitation and mean diurnal range. Average minimum temperature of the coldest month was the most important predictor in both models, with a logistic shape where suitability began to steeply increase from zero around -10 °C and climbing to 1.3 °C before reaching an index value defined as suitable. Both models retained temperature of the warmest month, with a generally positive relationship when considered with other variables. However, a function considering that predictor alone revealed a hump shaped relationship. Internal cross validation produced good assessment metrics for both models. The GLM country model had a cross-validation area under the receiver operating characteristic curve (AUC) of 0.94 and a true skill statistic (TSS) of 0.76, while the GLM targeted model had a cross-validation AUC value of 0.91 and TSS of 0.70. To produce binary maps, the GLM country threshold was 0.14 and the GLM targeted threshold was 0.44.
All models performed well when compared to the HUC12 coypu density data (Table
The ecophysiological based ensemble model results show greatest agreement in suitability in the southeastern USA from Texas to North Carolina and along the Pacific coast from Washington to southern California (Fig.
Model predictions for Myocastor coypus [Molina, 1782] for a an ensemble of US 3yr, US 5yr, and global 50yr b an ensemble of GLM country and GLM targeted c an ensemble of all five models d number of months classified as unsuitable using the Maurer observed climate data for 2001 to 2010 e the number of GCMs defining each pixel as suitable (ensemble of the 29 binary downscaled GCMs using the Maurer dataset as the reference) f ensemble of the 31 downscaled GCMs average from 2040 to 2050. All maps are overlaid with USA state population status according to
At the global scale, global 50yr, GLM targeted, and GLM country models had varying levels of performance when compared to country level classification by
Evaluation metrics for global extent models. Evaluation metrics include percent correctly classified, sensitivity and specificity for global models of coypu (Myocastor coypus [Molina, 1782]) habitat suitability (global 50yr: ecophysiological based model based using average monthly temperature for 1950 to 2000, generalized linear model [GLM] country: GLM model using coypu presence locations and random background locations from countries containing coypu locations, and GLM targeted: GLM model using coypu presence locations and targeted background consisting of muskrat locations), evaluated using country level classification according to
|Global 50yr||GLM country||GLM targeted|
|Percent correctly classified||0.7||0.76||0.76||0.76||0.67||0.68|
An ensemble of the three global models had high agreement in coypu suitability for regions with established invasions (Fig.
Global predictions of habitat suitability for coypu (Myocastor coypus [Molina, 1782]). Ensemble predictions using three models at the global scale including an ecophysiological based model based on average monthly climate data from WorldClim, a correlative model using country background and a correlative model using a taxonomically targeted background approach. Maps are in Mollweide projection.
The GLM country model predicted the least amount of tropical areas as suitable, with the GLM targeted model and the global 50yr model being more similar. However, many of these areas had novel environmental conditions. In dry areas such as North Africa, however, the global 50yr model did not predict suitable habitat due to the added arid region mask. The GLM country model excluded some of these dry areas, while the GLM targeted model included almost all of them.
Model evaluations from HUC coypu density for the northwestern USA show very little difference between ecophysiological based and correlative models (Table
There is substantial variation in potential future climate from year to year as well as between GCMs and RCPs (Fig.
Amount of suitable habitat for coypu by year starting in 1950 and extending to 2100. Amount of suitable habitat is defined as thousands of km2 within the continental USA without any months where average minimum temperature was <0 °C while average maximum temperature was also <5 °C. The solid black line from 1950 to 2013 is the Maurer observed dataset, the historical data is the 12 General Circulation Models (GCMs) calibrated between 1950 and 2013 using the Maurer dataset, and the projected climate by the GCMs with the average amount of predicted suitable habitat (solid line) and variation in predicted suitable habitat (solid colored area) for the four different representative concentration pathways (RCPs) describing possible climate futures by the GCMs. The solid vertical bars indicate the time periods for which we created geographic maps of predicted suitable habitat.
Despite the fact that our ecophysiological based model is relatively simplistic and is based on physiological data from one location, it showed overall agreement with current knowledge of coypu distribution in local regions (e.g., the Pacific Northwest of the USA) and globally. For endotherms, prolonged exposure to thermal stress can decrease fitness and our relatively coarse temporal scale of monthly climate data accounts for extended periods of potentially stressful cold temperatures. There are also likely microclimatic factors that influence coypu distribution at local scales, especially in arid regions where there may be narrow suitable habitat along riparian areas. These results concur with previous research that winter temperatures may limit coypu distribution, at least in the invaded range (
While minimum temperature thresholds have been identified for coypu, thermal tolerance at high temperature has not been studied. This tolerance could be another limiting factor in locations such as the Amazon and portions of Africa where the models did not match known distributions. Examining tropical climate designation using WorldClim climate data to hypothesize an upper thermal limit matched well with known coypu distribution (Fig.
Tropical areas in relation to coypu presence. Areas defined as tropical are shown in a South America where coypu are native south of -23° latitude b Kenya where coypu have only been reported around Lake Naivasha, and c Florida, USA where coypu have not been reported in the southern part. Maps are in Mollweide projection.
The baseline dataset (PRISM or WorldClim) and time frame used (3 year, 5 year, 10 year) made a difference in the predictions of current suitable habitat. Climate is not in equilibrium (Fig.
The model comparisons also are consistent with other studies that produced both ecophysiological based and correlative models (e.g.,
Future research could incorporate additional factors into the ecophysiological based model, such as an upper thermal limit. For the correlative model, obtaining more locations from the native range may improve model performance. We know there was particularly poor coverage in our observation data for this region. Finer temporal resolution of global climate data may improve all global models, as 50 year averages do not capture the extremes that may be important for species with distributions limited by thermoregulatory processes.
Overall, the national and global models for suitable coypu habitat performed well. By utilizing two different approaches (correlative and ecophysiological based) that produced similar projected distributions, we have more confidence in our results than we would using a single method. With these models we can now predict where coypu are likely to invade given climatic changes and regional hydrologic networks. These predictions can help focus early detection efforts by identifying areas to monitor for and potentially eradicate nacent coypu populations. Furthermore, the models can provide specific information about which areas might be invaded based on recent weather trends and hydrologic pathways. This is important because it has been demonstrated that the costs of early intervention with respect to a coypu invasion are much less than the costs of the damage they do and control efforts once their populations become established (
The authors would like to thank the U.S. Geological Survey Invasive Species Program for support. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Suppl. material