Abstract
Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.
Keywords: User-defined features, Auto-features, Regularization multiplier, Species distribution, Environmental niche modelling, Parameters configuration, Maximum entropy
Introduction
Environmental niche modeling (ENM), also referred as to predictive habitat distribution modeling (e.g., Guisan & Zimmermann, 2000), or species distribution modeling (e.g., Elith & Leathwick, 2009; Miller, 2010), is a common technique increasingly used in a variety of disciplines interested in the geographical distribution of species. ENMs have been used, among other disciplines, in landscape ecology (Amici et al., 2015), biogeography (Carvalho & Lama, 2015), conservation biology (Bernardes et al., 2013; Brambilla et al., 2013), marine sciences (Bouchet & Meeuwig, 2015; Crafton, 2015), paleontology (Stigall & Brame, 2014), plant ecology (Gelviz-Gelvez et al., 2015), public health (Ceccarelli & Rabinovich, 2015) and restoration ecology (Fernández & Morales, 2016).
The basic principle behind the ENM is the use of environmental information layers and species presence, pseudo-absence or absence points to develop probabilistic maps of distribution suitability (Elith & Leathwick, 2009). ENMs are generally used for four main objectives: (1) to estimate the relative suitability of the habitat currently occupied by assessed species, (2) to estimate the relative suitability of habitat in areas where assessed species are currently not known to be present, (3) to estimate potential changes in the suitability of habitat due to environmental change scenarios, and (4) to estimate the species environmental niche (Warren & Seifert, 2011).
Among the available tools for ENM, the maximum entropy approach is one of the most widely used for predicting species distributions (Fitzpatrick, Gotelli & Ellison, 2013; Merow, Smith & Silander, 2013). The maximum entropy approach, part of the family of the machine learning methods, is currently available in the software MaxEnt (Phillips, Anderson & Schapire, 2006; https://www.cs.princeton.edu/s˜chapire/maxent/). MaxEnt can model potential species distributions by using a list of species presence-only locations and a set of environmental variables (Elith, Kearney & Phillips, 2010). Since 2004 the use of MaxEnt has grown exponentially (Fig. 1). Nowadays MaxEnt is one of the preferred methods used for predicting potential species distribution among researchers (Merow, Smith & Silander, 2013).
The simplicity and straightforward steps required to run MaxEnt seem to have tempted many researchers to use it as a black box despite the increasing evidence that using MaxEnt with default parameter settings (i.e., auto-features) will not necessarily generate the best model (e.g., Shcheglovitova & Anderson, 2013; Syfert, Smith & Coomes, 2013; Radosavljevic & Anderson, 2014). MaxEnt has two main modifiable parameters: (1) feature classes and (2) regularization multiplier. Feature class corresponds to a mathematical transformation of the different covariates used in the model to allow complex relationship to be modeled (Elith, Kearney & Phillips, 2010). The regularization multiplier is a parameter that adds new constraints, in other words is a penalty imposed to the model. The main goal is to prevent over-complexity and/or overfitting by controlling the intensity of the chosen feature classes used to build the model (Elith, Kearney & Phillips, 2010; Shcheglovitova & Anderson, 2013). We recommend look at Merow, Smith & Silander (2013) for a detailed explanation of feature classes and regularization multipliers.
Some authors have argued that the use of default parameters without providing information on this decision could mean that several of published results could be based on over-complex or over-simplistic models (Warren & Seifert, 2011; Cao et al., 2013; Merow, Smith & Silander, 2013). For example, Anderson & Gonzalez (2011) compared different MaxEnt configurations to determine the optimal configuration that minimizes overfitting. Their results showed that in several cases the optimal regularization multiplier was not the default. This is supported by other studies showing that a particular combination of feature classes and regularization multiplier provided better results than the default settings (Syfert, Smith & Coomes, 2013), and that the default configuration provided by MaxEnt is not necessarily the most appropriate, especially when dealing with small samples size (Warren & Seifert, 2011; Shcheglovitova & Anderson, 2013).
Whereas several authors have highlighted the potential problems of models generated by MaxEnt default settings and provided recommendation to deal with this issue (e.g., Warren & Seifert, 2011; Merow, Smith & Silander, 2013; Yackulic et al., 2013; Halvorsen et al., 2015), there is no information regarding the echo that these recommendations have had on current MaxEnt use, and neither on how this could be affecting published results. To help answering these questions, in this work we aimed: First, to evaluate if researchers are paying attention to recommendations regarding the importance of evaluating the best potential combination of MaxEnt’s parameters for modelling species distribution; and second, to quantify the potential differences in resulting outputs when using MaxEnt default parameters instead of evaluating different sets of parameters combinations to identify an alternative best model. To achieve our first objective we reviewed and analyzed the published literature from years 2013 to 2015, focusing our analysis in the modelling information provided by articles reporting results based on small numbers of species presence points (i.e., less than 90 presence points). For the second objective, we selected from our review results a sample of 20 case studies, and we performed the modelling process using default setting and a combination of parameters to assess the differences between the default and the alternative best model outputs.
Materials and Methods
Literature analysis
We used our own literature search protocol using the databases available through the ISI Web of Science (ISI WOS; http://webofknowledge.com/) search engine (Supplemental Information 1) by using the keywords “MaxEnt” and “species distribution” in the topic. Because many of the recommendations were published between 2011 and 2012, we restricted our search to the 2013–2015 period, assuming that if researchers were alert to recommendations these changes would be noticed on publications of following years. The search was carried out by NS Morales and V Baca-González during the months of March and April, 2016. Whereas we only used English key words for our search, we also included in our analysis the articles published in Spanish and Portuguese but with abstracts written in English. From these results we only selected studies reporting ≤90 presence species points for the modelling process. We chose this threshold value because major changes in MaxEnt auto-features parameters occurs when less than 80 presence records points are used for modelling (Phillips & Dudík, 2008; Merow, Smith & Silander, 2013), implying that a sample of 90 could easily represent less than 80 presence points for modelling due to the required sample points that needs to be set aside for validation purposes. Because for some authors the ≤90 presence species points threshold may be considered rather large for defining what a small sample size is (e.g., Phillips & Dudík, 2008; Shcheglovitova & Anderson, 2013), we attempted to overcome this potential issue by ensuring that half of the case studies (i.e., 10) used for performing our modeling analysis had less than 15 presence points.
Our preliminary literature search yielded 816 articles. From these articles, 244 reported a sample size of ≤90 presence points and were therefore used for our literature analyses (Fig. 2, Table 1, see the detailed articles list in Supplemental Information 2). Any doubt or disagreement in the classification of the articles was discussed with IC Fernández; whose opinion was taken as final decision. We reviewed the methodological information provided in the selected articles to determine the types of feature classes and regularization multiplier used for modelling process. We classified features and regularization multiplier used in each paper in three main categories: (1) user-defined parameters, (2) software default parameters, (3) and no information provided. We also evaluated if the articles provided data on the geographical coordinates of presence points used for the modelling process (i.e., lists of georeferenced presence points or species presence maps). In this context, accuracy and precision associated to such geographic information are critical input for performing but are rarely assessed and mentioned in modeling studies (e.g., Velásquez-Tibatá, Graham & Munch, 2016). We considered only those articles providing information on features, regularization multiplier and geographical coordinates as suitable for modelling analysis.
Table 1. Number of articles published during the years 2013–2015 available through the Web of Knowledge Databases.
Year | Total articles | Articles (n > 90) | Articlesa (n ≤ 90) | Articles (no info) |
---|---|---|---|---|
2013 | 246 | 176 | 65 | 5 |
2014 | 285 | 187 | 92 | 6 |
2015 | 285 | 186 | 87 | 12 |
Total | 816 | 549 | 244 | 23 |
Notes.
Only articles with sample size ≤ 90 were used for the analyses.
No info refers to articles that do not provide information about the sample size used for modelling.
Modelling analysis
To quantify the potential differences in resulting outputs when using software default parameters instead of different parameters combinations to identify an alternative best model, we first generated a list consisting on all publications providing the geographical locations of presence points used for modelling and that report having used default parameters (feature classes and regularization multiplier). These selected publications were sorted in two groups, those with less than 15, and those between 16 and 90 sample presence points. From each of these groups we randomly selected 10 articles for our analysis. If the selected articles in any of the two groups were considered too similar in terms of the number of samples and area of analysis (extent), we repeated the process until having a heterogeneous sample that increases the strength of our analysis. With this we aimed to include studies from different regions, with varying geographical extents, and differing number of species presence points. For each of these articles we collected the geographical coordinates of species presence points and performed the modelling process using default features, and a set of 72 different parameter combinations, aiming to quantify potential differences on resulting outputs when using default parameters instead of analyzing an alternative best model. For all our modelling we used the WorldClim database (http://www.worldclim.org) as our environmental variables dataset, standardizing all the analysis to a ∼1 km2 resolution grid. To select the best model parameters we compared different models with a combination of the “feature class” and “regularization multiplier”. MaxEnt provides different types of restrictions (“feature class”) in the modelling stage such as lineal (L), quadratic (Q), product (P), threshold (T), and hinge (H). We used all the possible combinations of these features (12 combinations). The used regularization multiplier values were based on Warren & Seifert (2011) and Shcheglovitova & Anderson (2013): 1, 2, 5, 10, 15, and 20. Combining features classes and regularization multipliers, we assessed a total of 72 models for each case study, plus the default auto-feature. For each case of study we selected the “best model” by using the AICc criterion, as this model selection criterion outperforms other available criterion (e.g., AUC) for comparing different models generated through MaxEnt, particularly for small sample sizes (Warren & Seifert, 2011). A detailed description of the methods used for modelling is provided in Supplemental Information 3.
Results
Literature analysis
From the 244 articles that reported a sample size ≤90 for the 2013–2015 period, 44.3% (108 articles) did not provide information about the features used for modelling, 39.8% (97 articles) reported to have used default features, and only 16.0% (39 articles) reported to have used user-defined features (Fig. 3; Supplemental Information 2). In terms of the regularization multiplier, 48.8% (119 articles) did not provide any information about the regularization multiplier used for modelling, 43.4% (106 articles) used the default regularization multiplier, and only 6.9% (19 articles) reported having used a user-defined regularization multiplier (Fig. 3; Supplemental Information 2). Considering both default parameters, merely 3.7% (9 articles) of the reviewed articles reported having used user-defined settings for both parameters (Supplemental Information 2).
Even though 70.5% (172 articles) of publications provide a list or a map with the geographical coordinates of the presence points used for modelling, and 47.1% (115 articles) reported both feature classes and regularization multipliers used for modelling; only 34.3% (84 articles) of the analyzed publications provide all three elements together (Fig. 4).
Modelling analysis
Results from our modelling analysis reveal huge potential effects of using a default parameter instead of a best model approach for identifying best suitable areas for species distribution (Table 2). Although our results show that the spatial correlation between default and best model outputs is relatively high, and that fuzzy kappa statistics show high similarity between generated maps for all assessed case studies, the total area identified as suitable for the assessed species tend to greatly differ, particularly for species covering large geographical extents (Table 2). Nevertheless, we did not find statistical evidence suggesting that outputs generated by defaults setting tend to predict larger or smaller total suitable areas than the alternative best model (p = 0.093, paired t-test for log transformed variables). However, it is not only the difference on total suitable area that differs, but also the specific areas that are identified as suitable by both modelling approaches (i.e., shared area). Whereas in average the proportion of shared areas tend to be considerably larger than the not-shared area (mean shared area ratio = 2.483), our data shows that for some cases there could be large discrepancies, with the majority of predicted suitable areas not overlaying between model outputs (i.e., shared ratio < 1) (Table 2).
Table 2. Estimation of resulting differences when using MaxEnt’s default parameters or a best model approach for modelling species distribution.
Sample size | Spatial correlation | Fuzzy Kappa | Area (Km2) | Area (Km2) | Shared/not shared ratio | Best model parameters | Source | ||
---|---|---|---|---|---|---|---|---|---|
Default | Best model | Shared | Not shared | ||||||
7 | 0.856 | 0.864 | 144,129 | 447,092 | 142,612 | 0.466 | 0.466 | T2 | Carvalho & Lama (2015) |
8 | 0.957 | 0.799 | 76 | 66 | 66 | 6.600 | 6.600 | Q5 | Fois et al. (2015) |
9 | 0.905 | 0.797 | 15,907 | 9,771 | 9,212 | 1.270 | 1.270 | LQP5 | Chunco et al. (2013) |
10 | 0.943 | 0.781 | 861 | 1,939 | 843 | 0.758 | 0.758 | Q1 | Alfaro-Saiz et al. (2015) |
11 | 0.992 | 0.943 | 122,415 | 149,775 | 121,283 | 4.094 | 4.094 | L1 | Chetan, Praveen & Vasudeva (2014) |
12 | 0.983 | 0.841 | 428,209 | 551,196 | 425,674 | 3.324 | 3.324 | L2 | Palmas-Pérez et al. (2013) |
12 | 0.836 | 0.906 | 175,166 | 174,543 | 156,798 | 4.342 | 4.342 | TQ5 | Pedersen et al. (2014) |
13 | 0.960 | 0.843 | 33,421 | 26,169 | 24,317 | 2.219 | 2.219 | TQ2 | Alamgir, Mukul & Turton (2015) |
13 | 0.995 | 0.965 | 22,013 | 26,445 | 21,820 | 4.528 | 4.528 | LQ1 | Mweya et al. (2013) |
14 | 0.948 | 0.916 | 363 | 907 | 353 | 0.625 | 0.625 | LQP1 | Meyer, Pie & Passos (2014) |
15 | 0.967 | 0.900 | 5,004 | 8,845 | 4,991 | 1.291 | 1.291 | QH2 | Urbani et al. (2015) |
16 | 0.769 | 0.652 | 13,466 | 28,948 | 12,848 | 0.768 | 0.768 | LQPT5 | De Castro Pena et al. (2014) |
26 | 0.865 | 0.847 | 5,655,316 | 7,383,714 | 5,003,914 | 1.651 | 1.651 | QP1 | Chłond, Bugaj-Nawrocka & Junkiert (2015) |
26 | 0.945 | 0.705 | 32,020 | 36,420 | 28,695 | 2.597 | 2.597 | L2 | Simo et al. (2014) |
31 | 0.937 | 0.879 | 243,764 | 248,513 | 196,113 | 1.960 | 1.960 | PT1 | Orr et al. (2014) |
49 | 0.962 | 0.880 | 135,239 | 103,330 | 100,192 | 2.624 | 2.624 | PT1 | Hu & Liu (2014) |
54 | 0.945 | 0.858 | 2,491,722 | 1,723,084 | 1,598,103 | 1.569 | 1.569 | LQPT1 | Conflitti et al. (2015) |
55 | 0.841 | 0.863 | 1,649,518 | 1,570,127 | 1,362,351 | 2.753 | 2.753 | TQ2 | Vergara & Acosta (2015) |
58 | 0.827 | 0.862 | 5,822,694 | 5,37,0521 | 4,439,531 | 1.918 | 1.918 | T1 | Aguiar et al. (2015) |
76 | 0.934 | 0.858 | 3,904,018 | 3,700,108 | 3,406,765 | 4.309 | 4.309 | TQ1 | Yu et al. (2014) |
The sample size (i.e., number of presence points) seems to not affect the degree of differences between the outputs obtained by using the default setting or by evaluating a set of parameters to select an alternative best model. In fact, our analysis show that sample size does not affect the spatial correlation (R2 = 0.026, p = 0.501), fuzzy kappa (R2 = 0.005, p = 0.770), or shared/not shared ratio (R2 = 0.004, p = 0.786) between default and best modelling outputs. Also our results do not reveal any trend showing that sample size may favor the selection of some parameter combination over others, beside the fact that for all study cases the best models tend to be associated to small regularization multipliers (Table 2). These results highlight the importance of evaluating what combination of parameters could provide the best modelling results, independently of the sample size used for modelling.
Discussion
Are we paying attention to recommendations?
Whereas there is increasing evidence that the use of MaxEnt default parameters do not always generate the best possible model output (e.g., Syfert, Smith & Coomes, 2013; Radosavljevic & Anderson, 2014), and different authors have highlighted the importance to evaluate the best combination of these parameters before deciding on the best model (see Anderson & Gonzalez, 2011; Warren & Seifert, 2011), results from our analysis indicate that researchers have been rather indifferent to these recommendations. In fact, our literature analysis shows that the use of MaxEnt default parameters for modelling species distribution with small recorded presence points seems to be the rule rather than the exception. More than 40% of the articles analyzed in our study do not provide information about the parameters configuration used to run the models, which reveals the little attention that researchers and reviewers are paying to this specific issue. Our results also reveal that among the articles that do provide information about the features and regularization multiplier used, a large proportion reported to have used the software default configuration. This preference towards using default setting has remained strong despite the variety of articles describing how MaxEnt works and should be used (Phillips & Dudík, 2008), the proper configuration process (e.g., Merow, Smith & Silander, 2013), the potential implications of not selecting the best parameters combination (e.g., Anderson & Gonzalez, 2011; Warren & Seifert, 2011; Syfert, Smith & Coomes, 2013; Radosavljevic & Anderson, 2014) and the increasing publication of approaches to select the best model by using appropriate parameters combinations (see Anderson & Gonzalez, 2011; Syfert, Smith & Coomes, 2013; Shcheglovitova & Anderson, 2013).
We did not observe any trend in the data that would suggest a change from “black box” users towards the use of user-defined parameters. Although our reviewed articles cover a relatively short period of time (2013–2015), if authors were inclined to adopt best practices for modelling we would have expected to see a trend in the data showing an increasing use of user-defined features over time. However, the only trend in our results is the increasing number of articles not providing information on the features and regularization multiplier used for modelling. We do not have a clear explanation for this trend, but we believe that it is probably due to new researchers using the modelling software without paying proper attention to current MaxEnt literature, particularly to the publications referring to the importance of analyzing parameters combination for selecting the best model (e.g., Anderson & Gonzalez, 2011; Warren & Seifert, 2011; Syfert, Smith & Coomes, 2013; Radosavljevic & Anderson, 2014).
The widespread use of default parameters is not the only caveat we found in our literature analysis. We also found a general lack of information that would allow for replicating, assessing or comparing the results from published studies. This information is not only relevant in terms of potential replication of the research, but also necessary for reviewers to evaluate if the outputs from the modelling process are reliable, or are affected among other factors by parameters used, unreliable species presence data sources, or geographically biased presence points records.
Whereas in our literature review we limited the search of articles only to the ISI WOS database, this database includes the large majority of mainstream journals dealing with species distribution modelling (Supplemental Information 2) and is often regarded as including journals with high quality standards. Therefore we consider that our results are a robust representation of the current lack of attention to recent published recommendations on how to better use MaxEnt.
Implications for research and practice
There are no doubts of the huge potential that MaxEnt has for helping understanding species distribution and for its application as a decision-making tool, which is reflected by the large diversity of disciplines that currently are using it. Nevertheless, as any modelling approach, results obtained through MaxEnt will largely depend on the quality of input data (i.e., reliability of environmental and species presence data) (Yackulic et al., 2013) and parameterization used for modelling (Warren & Seifert, 2011; Cao et al., 2013; Merow, Smith & Silander, 2013). Whereas in this work we did not evaluate if the input data used for modelling could be considered reliable or appropriate, it is important to take into account that results can be largely affected by species presence sampling bias (Kramer-Schadt et al., 2013; Syfert, Smith & Coomes, 2013; Yackulic et al., 2013) and by the geographical extent used for modelling (Merow, Smith & Silander, 2013).
For the case of parameterization (i.e., combination of features and regularization multiplier), results from our case studies strongly support the claims made by previous studies in relation that using MaxEnt default parameters may not generate the best results (e.g., Anderson & Gonzalez, 2011; Warren & Seifert, 2011; Syfert, Smith & Coomes, 2013; Radosavljevic & Anderson, 2014). In fact, in none of the 20 case studies analyzed in our work the model generated by using default parameters were selected as the best model, which is a worrying sign because an important proportion of MaxEnt published literature can be presenting modelling outputs based on over-simplistic or over-complex models. In other words, reported models can be overestimating the potential distribution of assessed species, or overfitting modelling output to the input data, therefore losing its ability to identify the optimal range of environmental conditions that are suitable for the species (Warren & Seifert, 2011; Merow, Smith & Silander, 2013).
Nevertheless, perhaps the most relevant implications of an inadequate use of MaxEnt for modelling species distribution are on the decision-making arena. When results from the modelling processes are used directly to assess species conservation or to develop conservation strategies, the areas identified as suitable for a given species could differ greatly depending on the parameters used for modelling (Anderson & Gonzalez, 2011). Whereas for our study cases we only used environmental variables gathered from the WordClim database, and therefore our models do not necessary replicate the results published by all the assessed studies, our results do show that independently of the sample size, geographical region and extent of analysis, decisions taken based on models generated by MaxEnt default setting could be strikingly different from those taken based on the best model.
Conclusions and recommendations
Results from our study may have vast implications, particularly with regard how articles are being reviewed, and the replicability and transferability of the results. We adhere to the calls from other authors to pay better attention to the potential implication of using Maxent’s default parameters when modelling species distribution, but we also suggest reviewers to carefully evaluate if the methodological approach used for modelling is reliable and well supported in recent literature. In addition, researchers need to provide as much information as possible to allow proper evaluation and increase the potential replicability and transferability of their results.
Despite the fact that there are several studies that already include several recommendations how to use and set up MaxEnt (e.g., Elith et al., 2006; Warren & Seifert, 2011; Merow, Smith & Silander, 2013; Yackulic et al., 2013; Halvorsen et al., 2015) we will try to summarize the most important points that researchers need to keep in mind for selecting the best model from the set of potential outputs generated by changing features and regularization parameters.
During the process of building the model, the authors need to determine the best possible model using an objective methodology. One approach is using a jackknife procedure similar to the one described by Shcheglovitova & Anderson (2013). The process consists in comparing different models with a combination of the parameters, “feature class” and “regularization multiplier” (see Shcheglovitova & Anderson (2013), Warren & Seifert (2011) and Supplemental Information 3 for examples). The comparison of models can be done using the corrected Akaike information criterion (AICc) available in the software ENMTOOLS version 1.4.4 (Warren & Seifert, 2011). The best model will correspond to the combination of “feature class” and “regularization multiplier” with the smallest AICc value. Although this is the methodology that we used in this work there are other methods that can be used. Another option is using a correlation analysis of the model-predicted probabilities of occurrence and presences and absences proposed by Syfert, Smith & Coomes (2013) or comparing the different map outputs using the fuzzy kappa statics based on Mestre et al. (2015).
Once the best model is selected, replication of the best model (e.g., several runs; n = 30) is needed to determine that the results are consistent. Also, it is highly recommended to validate the model output using in situ surveys especially in cases that small numbers of occurrences were used to generate the model. Although, we understand that this could be a major task when modelling large extensions of habitat or rare species distributions; these limitations must be included in the discussion and used with caution, especially for management purposes.
These simple recommendations can help to improve the applicability of resulting models, which in turn will help practitioners and decision-makers to use them more effectively as practical tools for the development of management and conservation activities. While the use of MaxEnt’s default parameter can be very useful for having a quick picture of the potential distribution of a given species, taking the necessary time to evaluate which parameters combination results in the best model could largely increase the accuracy and reliability of modelling results.
Supplemental Information
Funding Statement
The authors received no funding for this work.
Contributor Information
Narkis S. Morales, Email: narkis.moralessanmartin@mq.edu.au.
Ignacio C. Fernández, Email: ignacio.fernandez@asu.edu.
Additional Information and Declarations
Competing Interests
The authors declare there are no competing interests.
Author Contributions
Narkis S. Morales and Ignacio C. Fernández conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Victoria Baca-González performed the experiments, analyzed the data, reviewed drafts of the paper.
Data Availability
The following information was supplied regarding data availability:
The raw data is included in the manuscript and supplied as Supplemental Files.
References
- Aguiar et al. (2015).Aguiar LM de S, Da Rosa ROL, Jones G, Machado RB. Effect of chronological addition of records to species distribution maps: the case of Tonatia saurophila maresi (Chiroptera, Phyllostomidae) in South America. Austral Ecology. 2015;40:836–844. doi: 10.1111/aec.12261. [DOI] [Google Scholar]
- Alamgir, Mukul & Turton (2015).Alamgir M, Mukul SA, Turton SM. Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate. Applied Geography. 2015;60:10–19. doi: 10.1016/j.apgeog.2015.03.001. [DOI] [Google Scholar]
- Alfaro-Saiz et al. (2015).Alfaro-Saiz E, García-González ME, Del Río S, Penas Á, Rodríguez A, Alonso-Redondo R. Incorporating bioclimatic and biogeographic data in the construction of species distribution models in order to prioritize searches for new populations of threatened flora. Plant Biosystems. 2015;149:827–837. doi: 10.1080/11263504.2014.976289. [DOI] [Google Scholar]
- Amici et al. (2015).Amici V, Eggers B, Geri F, Battisti C. Habitat suitability and landscape structure: a maximum entropy approach in a mediterranean area. Landscape Research. 2015;40(2):208–225. doi: 10.1080/01426397.2013.774329. [DOI] [Google Scholar]
- Anderson & Gonzalez (2011).Anderson RP, Gonzalez Jr I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecological Modelling. 2011;222(15):2796–2811. doi: 10.1016/j.ecolmodel.2011.04.011. [DOI] [Google Scholar]
- Bernardes et al. (2013).Bernardes M, Rödder D, Nguyen TT, Pham CT, Nguyen TQ, Ziegler T. Habitat characterization and potential distribution of Tylototriton vietnamensis in northern Vietnam. Journal of Natural History. 2013;47(17–18):1161–1175. doi: 10.1080/00222933.2012.743611. [DOI] [Google Scholar]
- Bouchet & Meeuwig (2015).Bouchet PJ, Meeuwig JJ. Drifting baited stereo-videography: a novel sampling tool for surveying pelagic wildlife in offshore marine reserves. Ecosphere. 2015;6(1):1–29. doi: 10.1890/ES14-00174.1. [DOI] [Google Scholar]
- Brambilla et al. (2013).Brambilla M, Bassi E, Bergero V, Casale F, Chemollo M, Falco R, Longoni V, Saporetti F, Viganò E, Vitulano S. Modelling distribution and potential overlap between Boreal Owl Aegolius funereus and Black Woodpecker Dryocopus martius: implications for management and monitoring plans. Bird Conservation International. 2013;23(4):502–511. doi: 10.1017/S0959270913000117. [DOI] [Google Scholar]
- Cao et al. (2013).Cao Y, DeWalt RE, Robinson JL, Tweddale T, Hinz L, Pessino M. Using Maxent to model the historic distributions of stonefly species in Illinois streams: the effects of regularization and threshold selections. Ecological Modelling. 2013;259:30–39. doi: 10.1016/j.ecolmodel.2013.03.012. [DOI] [Google Scholar]
- Carvalho & Lama (2015).Carvalho AF, Lama MAD. Predicting priority areas for conservation from historical climate modelling: stingless bees from Atlantic Forest hotspot as a case study. Journal of Insect Conservation. 2015;19(3):581–587. doi: 10.1007/s10841-015-9780-7. [DOI] [Google Scholar]
- Ceccarelli & Rabinovich (2015).Ceccarelli S, Rabinovich JE. Global climate change effects on Venezuela’s vulnerability to chagas disease is linked to the geographic distribution of five triatomine species. Journal of Medical Entomology. 2015;52(6):1333–1343. doi: 10.1093/jme/tjv119. [DOI] [PubMed] [Google Scholar]
- Chetan, Praveen & Vasudeva (2014).Chetan N, Praveen KK, Vasudeva GK. Delineating ecological boundaries of Hanuman Langur species complex in Peninsular India using MaxEnt modeling approach. PLOS ONE. 2014;9:e87804. doi: 10.1371/journal.pone.0087804. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chłond, Bugaj-Nawrocka & Junkiert (2015).Chłond D, Bugaj-Nawrocka A, Junkiert Ł. Current and potential geographical distribution of Platymeris biguttatus (Linnaeus, 1767) with description of nymphs. Zoological Studies. 2015;54:9. doi: 10.1186/s40555-014-0092-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chunco et al. (2013).Chunco AJ, Phimmachak S, Sivongxay N, Stuart BL. Predicting environmental suitability for a rare and threatened species (Lao Newt, Laotriton laoensis) using validated species distribution models. PLOS ONE. 2013;8:e59853. doi: 10.1371/journal.pone.0059853. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Conflitti et al. (2015).Conflitti IM, Shields GF, Murphy RW, Currie DC. The speciation continuum: ecological and chromosomal divergence in the Simulium arcticum complex (Diptera: Simuliidae) Biological Journal of the Linnean Society. 2015;115:13–27. doi: 10.1111/bij.12480. [DOI] [Google Scholar]
- Crafton (2015).Crafton RE. Modeling invasion risk for coastal marine species utilizing environmental and transport vector data. Hydrobiologia. 2015;746(1):349–362. doi: 10.1007/s10750-014-2027-x. [DOI] [Google Scholar]
- De Castro Pena et al. (2014).De Castro Pena JC, Kamino LHY, Rodrigues M, Mariano-Neto E, De Siqueira MF. Assessing the conservation status of species with limited available data and disjunct distribution. Biological Conservation. 2014;170:130–136. doi: 10.1016/j.biocon.2013.12.015. [DOI] [Google Scholar]
- Elith et al. (2006).Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J McC M, Townsend Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006;29(2):129–151. doi: 10.1111/j.2006.0906-7590.04596.x. [DOI] [Google Scholar]
- Elith, Kearney & Phillips (2010).Elith J, Kearney M, Phillips S. The art of modelling range-shifting species. Methods in Ecology and Evolution. 2010;1(4):330–342. doi: 10.1111/j.2041-210X.2010.00036.x. [DOI] [Google Scholar]
- Elith & Leathwick (2009).Elith J, Leathwick JR. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics. 2009;40:677–697. doi: 10.1146/annurev.ecolsys.110308.120159. [DOI] [Google Scholar]
- Fernández & Morales (2016).Fernández IC, Morales NS. A spatial multicriteria decision analysis for selecting priority sites for plant species restoration: a case study from the Chilean biodiversity hotspot. Restoration Ecology. 2016;24(5):599–608. doi: 10.1111/rec.12354. [DOI] [Google Scholar]
- Fitzpatrick, Gotelli & Ellison (2013).Fitzpatrick MC, Gotelli NJ, Ellison AM. MaxEnt versus MaxLike: empirical comparisons with ant species distributions. Ecosphere. 2013;4(5):1–15. doi: 10.1890/ES13-00066.1. [DOI] [Google Scholar]
- Fois et al. (2015).Fois M, Fenu G, Cuena Lombraña A, Cogoni D, Bacchetta G. A practical method to speed up the discovery of unknown populations using Species Distribution Models. Journal for Nature Conservation. 2015;24:42–48. doi: 10.1016/j.jnc.2015.02.001. [DOI] [Google Scholar]
- Gelviz-Gelvez et al. (2015).Gelviz-Gelvez SM, Pavón NP, Illoldi-Rangel P, Ballesteros-Barrera C. Ecological niche modeling under climate change to select shrubs for ecological restoration in Central Mexico. Ecological Engineering. 2015;74:302–309. doi: 10.1016/j.ecoleng.2014.09.082. [DOI] [Google Scholar]
- Guisan & Zimmermann (2000).Guisan A, Zimmermann N. Predictive habitat distribution models in ecology. Ecological Modelling. 2000;135(2–3):147–186. doi: 10.1016/S0304-3800(00)00354-9. [DOI] [Google Scholar]
- Halvorsen et al. (2015).Halvorsen R, Mazzoni S, Bryn A, Bakkestuen V. Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt. Ecography. 2015;38:172–183. doi: 10.1111/ecog.00565. [DOI] [Google Scholar]
- Hu & Liu (2014).Hu J, Liu Y. Unveiling the conservation biogeography of a data-deficient endangered bird species under climate change. PLOS ONE. 2014;9:e84529. doi: 10.1371/journal.pone.0084529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kramer-Schadt et al. (2013).Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions. 2013;19(11):1366–1379. doi: 10.1111/ddi.12096. [DOI] [Google Scholar]
- Merow, Smith & Silander (2013).Merow C, Smith MJ, Silander JA. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography. 2013;36(10):1058–1069. doi: 10.1111/j.1600-0587.2013.07872.x. [DOI] [Google Scholar]
- Mestre et al. (2015).Mestre F, Pita R, Paupério J, Martins FMS, Alves PC, Mira A, Beja P. Combining distribution modelling and non-invasive genetics to improve range shift forecasting. Ecological Modelling. 2015;297:171–179. doi: 10.1016/j.ecolmodel.2014.11.018. [DOI] [Google Scholar]
- Meyer, Pie & Passos (2014).Meyer ALS, Pie MR, Passos FC. Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. American Journal of Primatology. 2014;76:551–562. doi: 10.1002/ajp.22247. [DOI] [PubMed] [Google Scholar]
- Miller (2010).Miller J. Species distribution modeling. Geography Compass. 2010;4(6):490–509. doi: 10.1111/j.1749-8198.2010.00351.x. [DOI] [Google Scholar]
- Moher et al. (2009).Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLOS Medicine. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mweya et al. (2013).Mweya CN, Kimera SI, Kija JB, Mboera LEG. Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa. Infection Ecology & Epidemiology. 2013;3:21748. doi: 10.3402/iee.v3i0.21748. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Orr et al. (2014).Orr MC, Koch JB, Griswold TL, Pitts JP. Taxonomic utility of niche models in validating species concepts: a case study in Anthophora (Heliophila) (Hymenoptera: Apidae) Zootaxa. 2014;3846(3) doi: 10.11646/zootaxa.3846.3.5. [DOI] [PubMed] [Google Scholar]
- Palmas-Pérez et al. (2013).Palmas-Pérez S, Krömer T, Dressler S, Arévalo-Ramírez JA. Diversidad y distribución de Marcgraviaceae en México. Revista Mexicana de Biodiversidad. 2013;84:170–183. [Google Scholar]
- Pedersen et al. (2014).Pedersen UB, Midzi N, Mduluza T, Soko W, Stensgaard A-S, Vennervald BJ, Mukaratirwa S, Kristensen TK. Modelling spatial distribution of snails transmitting parasitic worms with importance to human and animal health and analysis of distributional changes in relation to climate. Geospatial Health. 2014;8(2) doi: 10.4081/gh.2014.23. [DOI] [PubMed] [Google Scholar]
- Phillips, Anderson & Schapire (2006).Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006;190(3–4):231–259. doi: 10.1016/j.ecolmodel.2005.03.026. [DOI] [Google Scholar]
- Phillips & Dudík (2008).Phillips SJ, Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 2008;31(2):161–175. doi: 10.1111/j.0906-7590.2008.5203.x. [DOI] [Google Scholar]
- Radosavljevic & Anderson (2014).Radosavljevic A, Anderson RP. Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography. 2014;41(4):629–643. doi: 10.1111/jbi.12227. [DOI] [Google Scholar]
- Shcheglovitova & Anderson (2013).Shcheglovitova M, Anderson RP. Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecological Modelling. 2013;269:9–17. doi: 10.1016/j.ecolmodel.2013.08.011. [DOI] [Google Scholar]
- Simo et al. (2014).Simo M, Guerrero J, Giuliani L, Castellano I, Acosta L. A predictive modeling approach to test distributional uniformity of Uruguayan harvestmen (Arachnida: Opiliones) Zoological Studies. 2014;53:50. [Google Scholar]
- Stigall & Brame (2014).Stigall AL, Brame H-MR. Relating environmental change and species stability in Late Ordovician seas. GFF. 2014;136(1):249–253. doi: 10.1080/11035897.2013.852619. [DOI] [Google Scholar]
- Syfert, Smith & Coomes (2013).Syfert MM, Smith MJ, Coomes DA. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLOS ONE. 2013;8(7):e55158. doi: 10.1371/journal.pone.0055158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Urbani et al. (2015).Urbani F, D’Alessandro P, Frasca R, Biondi M. Maximum entropy modeling of geographic distributions of the flea beetle species endemic in Italy (Coleoptera: Chrysomelidae: Galerucinae: Alticini) Zoologischer Anzeiger. 2015;258:99–109. doi: 10.1016/j.jcz.2015.08.002. [DOI] [Google Scholar]
- Velásquez-Tibatá, Graham & Munch (2016).Velásquez-Tibatá J, Graham CH, Munch SB. Using measurement error models to account for georeferencing error in species distribution models. Ecography. 2016;39:305–316. doi: 10.1111/ecog.01205. [DOI] [Google Scholar]
- Vergara & Acosta (2015).Vergara J, Acosta LE. More on the Mesopotamian-Yungas disjunction in subtropical and temperate Argentina: bioclimatic distribution models of the harvestman Discocyrtus dilatatus (Opiliones: Gonyleptidae) Zoologia (Curitiba) 2015;32:445–456. [Google Scholar]
- Warren & Seifert (2011).Warren DL, Seifert SN. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications. 2011;21:335–342. doi: 10.1890/10-1171.1. [DOI] [PubMed] [Google Scholar]
- Yackulic et al. (2013).Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S. Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution. 2013;4(3):236–243. doi: 10.1111/2041-210x.12004. [DOI] [Google Scholar]
- Yu et al. (2014).Yu J, Wang C, Wan J, Han S, Wang Q, Nie S. A model-based method to evaluate the ability of nature reserves to protect endangered tree species in the context of climate change. Forest Ecology and Management. 2014;327:48–54. doi: 10.1016/j.foreco.2014.04.020. [DOI] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The following information was supplied regarding data availability:
The raw data is included in the manuscript and supplied as Supplemental Files.