Use Of Niche Models Research Articles

Theoretical Basis, Future Directions, and Challenges for Ecological Niche Models

Based on ecological niche theory, ecological niche models (ENMs) apply statistical and machine learning theories toanalyze occurrence data and to build character functions that estimate the possible shape of the realized niche of thespecies within the niche space. In the last two decades, the use of ENMs to predict the potential distribution ofspecies or to quantify niche similarity has become increasingly important in the fields of ecology, biogeography andevolutionary biology. Proceeding from the concept of the ecological niche, this article discusses the theoretical basisof ENMs in detail, and outlines the debate on the implementation and interpretation of ENMs currently. Additionally,we address important issues that need attention when ecologists work with ENMs, and identify relevant issues,challenges and future research directions in this field. Particularly, we recommend that anthropogenic influences onthe ecological niche of species should be considered in the future. Finally, we hope the viewpoints and debatesinvolved in this paper can resonate among the ecologists.

Human settlement evaluation in mountain areas based on remote sensing, GIS and ecological niche modeling

The Qinghai-Tibet Plateau is the world’s highest and largest plateau. Due to increasing demands for environment exploration and tourism, a large transitional area is required for altitude adaptation. Hehuang valley, which locates in the transition zone between the Loess Plateau and the Qinghai-Tibet Plateau, has convenient transportation and relatively low elevation. Our question is whether the geographic conditions here are appropriate for adapted stay before going into the Qinghai-Tibet Plateau. Therefore, in this study, we examined the potential use of ecological niche modeling (ENM) for mapping current and potential distribution patterns of human settlements. We chose the Maximum Entropy Method (Maxent), an ENM which integrates climate, remote sensing and geographical data, to model distributions and assess land suitability for transition areas. After preprocessing and selection, the correlation between variables and spatial autocorrelation input data were removed and 106 occurrence points and 9 environmental layers were determined as the model inputs. The threshold-independent model performance was reasonable according to 10 times model running, with the area under the curve (AUC) values being 0.917 ± 0.01, and 0.923 ± 0.002 for test data. Cohen’s kappa coefficient of model performance was 0.848. Results showed that 82.22% of the study extent was not suitable for human settlement. Of the remaining areas, highly suitable areas accounted for 1.19%, moderately for 5.3% and marginally for 11.28%. These suitable areas totaled 418.79 km2, and 86.25% of the sample data was identified in the different gradient of suitable area. The decisive environmental factors were slope and two climate variables: mean diurnal temperature range and temperature seasonality. Our model showed a good performance in mapping and assessing human settlements. This study provides the first predicted potential habitat distribution map for human settlement in Ledu County, which could also help in land use management.

Ecological niche modelling of bank voles in Western Europe.

The bank vole (Myodes glareolus) is the natural host of Puumala virus (PUUV) in vast areas of Europe. PUUV is one of the hantaviruses which are transmitted to humans by infected rodents. PUUV causes a general mild form of hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE). Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover influences disease transmission by controlling both the spatial distribution of vectors or hosts, as well as by facilitating the human contact with them. In this study the use of ecological niche modelling (ENM) for predicting the geographical distribution of bank vole population on the basis of spatial climate information is tested. The Genetic Algorithm for Rule-set Prediction (GARP) is used to model the ecological niche of bank voles in Western Europe. The meteorological data, land cover types and geo-referenced points representing the locations of the bank voles (latitude/longitude) in the study area are used as the primary model input value. The predictive accuracy of the bank vole ecologic niche model was significant (training accuracy of 86%). The output of the GARP models based on the 50% subsets of points used for testing the model showed an accuracy of 75%. Compared with random models, the probability of such high predictivity was low (χ2 tests, p < 10−6). As such, the GARP models were predictive and the used ecologic niche model indeed indicates the ecologic requirements of bank voles. This approach successfully identified the areas of infection risk across the study area. The result suggests that the niche modelling approach can be implemented in a next step towards the development of new tools for monitoring the bank vole’s population.

Use of ecological niche models to predict the distribution of invasive species: a scientometric analysis

We conducted a scientometric analysis to determine the main trends and gaps of studies on the use of ecological niche models (ENMs) to predict the distribution of invasive species. We used the database of the Thomson Institute for Scientific Information (ISI). We found 190 papers published between 1991 and 2010 in 82 journals. The number of papers was low in the 1990s, but began to increase after 2003. One-third of the papers were published by researchers from the United States of America, and consequently, the USA was also the most studied region. The majority of studies were carried out in terrestrial environments, while only a few investigated aquatic systems, probably because important aquatic predictor variables are scarce or unavailable for most regions in the world. Species-occurrence records were mainly composed of presence-only records, and almost 70% of the studies were carried out with plants and insects. Twenty-three different distribution modelling methods were used. The Genetic Algorithm for Rule-set Production (GARP) was used most often. Our scientometric analysis showed a growing interest in the use of ENMs to predict the distribution of invasive species, especially in the last decade, which is probably related to the increase in species introductions worldwide. Among some important gaps that need to be filled, the relatively small number of studies conducted in developing countries and in aquatic environments deserves careful attention.

Ecological Niche Conservatism in Doucs (Genus Pygathrix)

The impressive diversity and unique distribution of primate taxa in Southeast Asia creates a region of interesting biogeography and evolutionary history that remains poorly understood. The three species in the Asian colobine genus Pygathrix (P. cinerea, P. nemaeus, and P. nigripes) appear to follow the unique distribution pattern, replacing one another along the north–south axis. However, the evolutionary history and taxonomic status within Pygathrix is currently debated. We constructed climate-based ecological niche models (ENMs) for the three species, using six environmental variables, to confirm that the bioclimate envelopes of Pygathrix cinerea, P. nemaeus, and P. nigripes follow the north–south gradient. We also used the ENMs to test whether the three species lack ecological exchangeability, meaning differentiation among factors that define the fundamental niche, and whether they exhibit ecological niche conservatism, or the tendency for related species to differ less ecologically than might be expected by the climate available to each species. Our ENMs for Pygathrix followed a north–south gradient as expected, with P. nemaeus extending from ca. 14°N to 21°N, P. cinerea from 14°N to 16°N, and P. nigripes from 11°N to 13.5°N. This study presents the first use of ENMs for doucs that yield significant predictive ability based only on climatic variables. The models are significantly different for all three species, supporting a lack of ecological exchangeability between them, as well as the recent elevation of Pygathrix cinerea to species status. Importantly, Pygathrix cinerea and P. nemaeus show evidence of ecological niche conservatism, which lends support to the occurrence of an allopatric speciation event. This result also suggests that the current overlapping distribution of Pygathrix cinerea and P. nemaeus is the result of secondary contact. The north–south distribution gradient, which exists in similar form among other groups of sister taxa in the region, may be attributed to a zoogeographic barrier, vicariance, or current or historical climatic shifts.

A field-based analysis of the accuracy of niche models applied to the fossil record

The use of ecological niche modeling (ENM) to estimate the geographic ranges of species is widely employed with modern fauna and is becoming more widespread in paleontology. Herein, field validation is utilized to assess the predictive accuracy of ENM methods for Paleozoic brachiopod species. This study represents the first field validation analysis of ENM methods in the fossil record. Previously published species distributions models for 8 Late Ordovician brachiopod species from the Cincinnati, Ohio region (United States) developed using GARP (Genetic Algorithm using Rule-set Prediction) were assessed for accuracy by comparing species occurrence data from a newly available set of 18 localities with the original species distribution models. Based on this data, the statistical significance of the original model set was assessed; 18 of the 22 original models were demonstrated to be statistically significant, based on field validation. Of the 140 individual species occurrences assessed, 60.8% were accurately predicted, 9.2% exhibited over prediction, and 30% exhibited under prediction. Accurate results were more common for species modeled from the greatest number of species occurrence points. The least accurate species models developed were for eurytopic species or those for which taxonomic affinities are unclear. Results indicate that with ample outcrop, well-defined stratigraphy, and sufficient fossil occurrence data, ENM methods could be successfully applied to many intervals in Earth history.

Is Cassava the Answer to African Climate Change Adaptation?

This paper examines the impacts of climate change on cassava production in Africa, and questions whether cassava can play an important role in climate change adaptation. First, we examine the impacts that climate change will likely have on cassava itself, and on other important staple food crops for Africa including maize, millets, sorghum, banana, and beans based on projections to 2030. Results indicate that cassava is actually positively impacted in many areas of Africa, with −3.7% to +17.5% changes in climate suitability across the continent. Conversely, for other major food staples, we found that they are all projected to experience negative impacts, with the greatest impacts for beans (−16% ± 8.8), potato (−14.7 ± 8.2), banana (−2.5% ± 4.9), and sorghum (−2.66% ± 6.45). We then examined the likely challenges that cassava will face from pests and diseases through the use of ecological niche modeling for cassava mosaic disease, whitefly, brown streak disease and cassava mealybug. The findings show that the geographic distribution of these pests and diseases are projected to change, with both new areas opening up and areas where the pests and diseases are likely to leave or reduce in pressure. We finish the paper by looking at the abiotic traits of priority for crop adaptation for a 2030 world, showing that greater drought tolerance could bring some benefits in all areas of Africa, and that cold tolerance in Southern Africa will continue to be a constraint for cassava despite a warmer 2030 world, hence breeding needs to keep a focus on this trait. Importantly, heat tolerance was not found to be a major priority for crop improvement in cassava in the whole of Africa, but only in localized pockets of West Africa and the Sahel. The paper concludes that cassava is potentially highly resilient to future climatic changes and could provide Africa with options for adaptation whilst other major food staples face challenges.

Use of niche models in invasive species risk assessments

Risk maps summarizing landscape suitability of novel areas for invading species can be valuable tools for preventing species’ invasions or controlling their spread, but methods employed for development of such maps remain variable and unstandardized. We discuss several considerations in development of such models, including types of distributional information that should be used, the nature of explanatory variables that should be incorporated, and caveats regarding model testing and evaluation. We highlight that, in the case of invasive species, such distributional predictions should aim to derive the best hypothesis of the potential distribution of the species by using (1) all distributional information available, including information from both the native range and other invaded regions; (2) predictors linked as directly as is feasible to the physiological requirements of the species; and (3) modelling procedures that carefully avoid overfitting to the training data. Finally, model testing and evaluation should focus on well-predicted presences, and less on efficient prediction of absences; a k-fold regional cross-validation test is discussed.

Predicting the potential distribution of an endangered cryptic subterranean mammal from few occurrence records

Knowledge of geographical distributions and habitat preferences are central to the conservation and management of threatened species. Ecological niche models can be used to map potentially suitable habitat, making them valuable tools in conservation biology. These models can assist in searching for new populations of species with poorly known distributions and can also be used to guide area selection for systematic biodiversity planning. These models have traditionally required ten or more occurrence records for calibration and this has limited the use of these tools in conservation biology, since many threatened species are known from fewer records. However, recently models have been successfully calibrated using few records. To illustrate this, we developed an ecological niche model to map the potential distribution of the endangered Juliana's golden mole ( Neamblysomus julianae) in South Africa so that it could be used in searching for unrecorded populations and for area selection in systematic biodiversity plans. A model was calibrated in Maxent using only four occurrence records. Predictor variables included various bioclimatic, soil and vegetation types. The model identified limited suitable habitat within the map region. The first model facilitated the identification of two previously unrecorded populations. A second model was calibrated using the two additional occurrence records. This increased the proportion of correctly predicted presence records. Jackknife analyses indicated that the models were successful at predicting known presences as suitable. This paper demonstrates the use of ecological niche modelling to conservation of a cryptic endangered species with a poorly known distribution and discusses issues that are relevant for the application of this approach to other species.

How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end

The use of ecological niche models (ENM) to generate potential geographic distributions of species has rapidly increased in ecology, conservation and evolutionary biology. Many methods are available and the most used are Maximum Entropy Method (MAXENT) and the Genetic Algorithm for Rule Set Production (GARP). Recent studies have shown that MAXENT perform better than GARP. Here we used the statistics methods of ROC - AUC (area under the Receiver Operating Characteristics curve) and bootstrap to evaluate the performance of GARP and MAXENT in generate potential distribution models for 39 species of New World coral snakes. We found that values of AUC for GARP ranged from 0.923 to 0.999, whereas those for MAXENT ranged from 0.877 to 0.999. On the whole, the differences in AUC were very small, but for 10 species GARP outperformed MAXENT. Means and standard deviations for 100 bootstrapped samples with sample sizes ranging from 3 to 30 species did not show any trends towards deviations from a zero difference in AUC values of GARP minus AUC values of MAXENT. Ours results suggest that further studies are still necessary to establish under which circumstances the statistical performance of the methods vary. However, it is also important to consider the possibility that this empirical inductive reasoning may fail in the end, because we almost certainly could not establish all potential scenarios generating variation in the relative performance of models.

Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot

Ecological niche modeling (ENM) has become an important tool in conservation biology. Despite its recent success, several basic issues related to algorithm performance are still being debated. We assess the ability of two of the most popular algorithms, GARP and Maxent, to predict distributions when sampling is geographically biased. We use an extensive data set collected in the Brazilian Cerrado, a biodiversity hotspot in South America. We found that both algorithms give richness predictions that are very similar to other traditionally used richness estimators. Also, both algorithms correctly predicted the presence of most species collected during fieldwork, and failed to predict species collected only in very few cases (usually species with very few known localities, i.e., <5). We also found that Maxent tends to be more sensitive to sampling bias than GARP. However, Maxent performs better when sampling is poor (e.g., low number of data points). Our results indicates that ENM, even when provided with limited and geographically biased localities, is a very useful technique to estimate richness and composition of unsampled areas. We conclude that data generated by ENM maximize the utility of existing biodiversity data, providing a very useful first evaluation. However, for reliable conservation decisions ENM data must be followed by well-designed field inventories, especially for the detection of restricted range, rare species.

Niche shift versus niche conservatism? Climatic characteristics of the native and invasive ranges of the Mediterranean house gecko (Hemidactylus turcicus)

ABSTRACTAim The use of ecological niche models (ENMs) to predict potential distributions of species is steadily increasing. A necessary assumption is that climatic niches are conservative, but recent findings of niche shifts during biological invasion indicate that this assumption is not always valid. Selection of predictor variables may be one reason for the observed shifts. In this paper we assess differences in climatic niches in the native and invaded ranges of the Mediterranean house gecko (Hemidactylus turcicus) in terms of commonly applied climate variables in ENMs. We analyse which variables are more conserved versus relaxed (i.e. subject to niche shift). Furthermore, we study the predictive power of different sets of climate variables.Location The Mediterranean region and North America.Methods We developed models using Maxent and various subsets of variables out of 19 bioclimatic layers including: (1) two subsets comprising almost all variables excluding only highly collinear ones; (2) two subsets with minimalistic variable sets of water availability and energy measures; (3) two subsets focused on temperature‐related parameters; (4) two subsets with precipitation‐related parameters; and (5) one subset comprising variables combining temperature and precipitation characteristics. Occurrence data from the native Mediterranean range were used to predict the potential introduced range in North America and vice versa. Degrees of niche similarity and conservatism were assessed using both Schoener's index and Hellinger distances. The significance of the results was tested using null models.Results The degree of niche similarity and conservatism varied greatly among the predictors and variable sets applied. Shifts observed in some variables could be attributed to active habitat selection while others apparently reflected background effects.Main conclusions The study was based on comprehensive occurrence data from all regions where Hemidactylus turcicus is present in Europe and North America, providing a robust foundation. Our results clearly indicate that the degree of conservatism of niches in H. turcicus largely varies among predictors and variable sets applied. Therefore, the extent of niche conservatism of variables applied should always be tested in ENMs. This has an important impact on studies of biological invasion, impacts of climate change and niche evolution.

Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling

AbstractThe availability of user‐friendly software and publicly available biodiversity databases has led to a rapid increase in the use of ecological niche modelling to predict species distributions. A potential source of error in publicly available data that may affect the accuracy of ecological niche models (ENMs), and one that is difficult to correct for, is incorrect (or incomplete) taxonomy. Here we remind researchers of the need for careful evaluation of database records prior to use in modelling, especially when the presence of cryptic species is suspected or many records are based on indirect evidence. To draw attention to this potential problem, we construct ENMs for the North American Sasquatch (i.e. Bigfoot). Specifically, we use a large database of georeferenced putative sightings and footprints for Sasquatch in western North America, demonstrating how convincing environmentally predicted distributions of a taxon’s potential range can be generated from questionable site‐occurrence data. We compare the distribution of Bigfoot with an ENM for the black bear, Ursus americanus, and suggest that many sightings of this cryptozoid may be cases of mistaken identity.

Identifying important areas for butterfly conservation in Italy

AbstractThe current study combines the use of niche modelling with a site prioritization method to identify important areas for butterfly conservation in Italy. A novel machine learning method (bagging predictors) was used to predict the distribution of 232 species of butterflies across the Italian Peninsula. The results of the models were used to identify high‐value sites with a multispecies prioritization method called zonation. In order to identify important areas for species of conservation concern, we incorporated a species weighting scheme to zonation analyses. We also used the results of the zonation analyses to identify a series of management landscapes on the basis of the similarity in species composition among sites. The basic zonation showed that most important areas for butterfly conservation are located in the Alps, the Appennine, the Apulia region and in the island of Sardinia. The inclusion of a species weighting scheme in the zonation analyses revealed the importance of two new areas located in Southern Italy and emphasized the importance of the Alps for species of conservation concern. The landscape identification procedure selected a series of landscapes, which provide protection to a full range of species ranging from the Alps to Mediterranean areas. Our study shows that the areas selected in our analyses should be given high priority in future conservation plans and monitoring schemes.

Using ecological niche modeling for quantitative biogeographic analysis: a case study of Miocene and Pliocene Equinae in the Great Plains

The subfamily Equinae in the Great Plains region of North America underwent a dramatic radiation and subsequent decline as climate changed from warm and humid in the middle Miocene to cooler and more arid conditions during the late Miocene. Here we use ecological niche modeling (ENM), specifically the GARP (Genetic Algorithm using Rule-set Prediction) modeling system, to reconstruct the geographic distribution of individual species during two time slices from the middle Miocene through early Pliocene. This method combines known species occurrence points with environmental parameters inferred from sedimentological variables to model each species' fundamental niche. The geographic range of each species is then predicted to occupy the geographic area within the study region wherever the set of environmental parameters that constrain the fundamental niche occurs. We analyze changes in the predicted distributions of individual species between time slices in relation to Miocene/Pliocene climate change. Specifically, we examine and compare distribution patterns for two time slices that span the period from the mid-Miocene (Barstovian) Climatic Optimum into the early Pliocene (Blancan) to determine whether habitat fragmentation led to speciation within the clade and whether species survival was related to geographic range size. Patchy geographic distributions were more common in the middle Miocene when speciation rates were high. During the late Miocene, when speciation rates were lower, continuous geographic ranges were more common. Equid species tracked their preferred habitat within the Great Plains region as well as regionally throughout North America. Species with larger predicted ranges preferentially survived the initial cooling event better than species with small geographic ranges. As climate continued to deteriorate in the late Miocene, however, range size became irrelevant to survival, and extinction rates increased for species of all range sizes. This is the first use of ENM and GARP in the continental fossil record. This powerful quantitative biogeographic method offers great promise for studies of other taxa and geologic intervals.

Use of ecological niche modelling to predict distributions of freshwater fish species in Kansas

Abstract – An essential innovation in aquatic biodiversity research would be a robust approach to accurately predict species’ potential distributions. In this paper, I conduct an analysis to test the efficacy of ecological niche modelling for predicting fish species’ potential distributions using an artificial‐intelligence algorithm, the Genetic Algorithm for Rule‐Set Prediction (GARP). Models of species’ ecological niches are developed using GARP, and projected onto geography to predict species distributions. To test the validity of this approach, I used freshwater fish distribution data for twelve fish species occurring in Kansas. These taxa were chosen to represent phylogenetic, distribution, and habit diversity. I subset these data by omitting half of the counties from model building, and test models using the omitted counties. Models were tested using Receiver Operating Characteristic (ROC) analyses. Of the species tested, all were statistically significant with the models showing excellent predictive ability. Omission errors across taxa ranged from 0 to 17%. This inferential capacity opens doors to many synthetic analyses based on primary point‐occurrence data.