Next Article in Journal
Attention-Based Human Age Estimation from Face Images to Enhance Public Security
Previous Article in Journal
A New Odd Beta Prime-Burr X Distribution with Applications to Petroleum Rock Sample Data and COVID-19 Mortality Rate
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Data
Volume 8
Issue 9
10.3390/data8090144
data-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Data Descriptor

Potential Range Map Dataset of Indian Birds

by
Arpit Deomurari
1,*,
Ajay Sharma
2,
Dipankar Ghose
3 and
Randeep Singh
1,*
1
Amity Institute of Forestry and Wildlife, Amity University, Noida 201313, India
2
College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, USA
3
World Wide Fund for Nature-India (WWF-India), New Delhi 110003, India
*
Authors to whom correspondence should be addressed.
Data 2023, 8(9), 144; https://doi.org/10.3390/data8090144
Submission received: 7 August 2023 / Revised: 9 September 2023 / Accepted: 18 September 2023 / Published: 21 September 2023
Browse Figure
Review Reports Versions Notes

Abstract

:
Conservation management heavily relies on accurate species distribution data. However, distributional information for most species is limited to distributional range maps, which could not have enough resolution to take conservation action and know current distribution status. In many cases, distribution maps are difficult to access in proper data formats for analysis and conservation planning of species. In this study, we addressed this issue by developing Species Distribution Models (SDMs) that integrate species presence data from various citizen science initiatives. This allowed us to systematically construct current distribution maps for 1091 bird species across India. To create these SDMs, we used MaxEnt 3.4.4 (Maximum Entropy) as the base for species distribution modelling and combined it with multiple citizen science datasets containing information on species occurrence and 29 environmental variables. Using this method, we were able to estimate species distribution maps at both a national scale and a high spatial resolution of 1 km2. Thus, the results of our study provide species current species distribution maps for 968 bird species found in India. These maps significantly improve our knowledge of the geographic distribution of about 75% of India’s bird species and are essential for addressing spatial knowledge gaps for conservation issues. Additionally, by superimposing the distribution maps of different species, we can locate hotspots for bird diversity and align conservation action.
Dataset: https://zenodo.org/record/8221113
Dataset License: CC BY-NC-SA.

1. Summary

Distributional information is crucial for conservation planning of species. However, because of the vast distributional range of species and the consequences of habitat loss and climate change, it is exceedingly difficult to monitor changes in the range of most species and plan conservation measures.
For many species, expert range maps created using expert knowledge and secondary literature or as part of threat assessments may effectively identify their coarse ranges. But at smaller geographical resolutions, such as below 100 km, their false presence rates are exorbitantly high and significantly overstate actual distribution [1,2,3]. Also, most species range maps often exaggerate the real distribution of a species by including regions of appropriate habitat [2,3,4]. Past studies have revealed that the range maps for the species can be overestimates of their distribution ranges, hence further underlining the need for more accurate species distribution range maps required at a higher resolution for conservation planning [2,3,4,5,6].
Species presence-only data records from museum samples and records of citizen science data [7,8], are fine-scale information of species distribution also serve as the basis for a various of spatial analyses. The most important applications of such data records is in correlative species distribution models (SDMs) [9,10,11,12]. In such models the presence records have been extensively used to quantify species-habitat or environment relationships to identify the appropriate species niche, and predict distributions [13,14,15,16,17]. In view of this SDMs tools are significantly used to decrease the uncertainty in distribution projections of species. However these data sets are often susceptible towards sampling bias [18,19,20,21]. To Improve the overall accuracy of SDMs, it requires proper handling of presence records or data of species to assess the impacts of spatial sampling biases and reduction using procedure i.e., deployed data cleaning and thinning techniques [20,22,23,24,25,26].
Species distribution models have become one of the most common ways to infer distributions from presence-only data provided by citizen science initiatives. Numerous studies have used SDMs to estimate species ranges and inhabited areas for IUCN Red List evaluations [4,17,27,28,29,30,31]. Several countries have published national-level biodiversity assessments using SDMs [32,33,34,35,36,37,38,39] with only a small number of species or groups in India have their distribution evaluated for various applications; including; for example for birds [4,40,41,42,43,44,45,46,47,48,49,50,51,52], for plants [53,54,55,56,57,58,59,60,61,62,63,64], for mammals [65,66,67,68,69,70,71], for invasive and pest species [72,73,74,75,76,77].
In this study, we aimed to use citizen science occurrence data of bird species reported in India to construct distribution range maps at the national level fine scale using MaxEnt-based species distribution modelling. These range maps are converted into binary presence-absence raster maps for broader use. The available data give information on the possible geographical distribution of birds in India. Different stakeholders, such as policymakers, academics, international and local non-government organizations, government organizations, and birder groups interested in preserving, conserving, and studying Indian birds, will find this dataset valuable. Inadequate awareness of the regional distribution of avian biodiversity impedes decision-making for bird conservation in India, which is one of the goals of making this data available. Authors have used this dataset to assess climate change impacts on Indian birds [78].

2. Data Description

This dataset aims to offer an easy-to-use resource that will allow non-specialists from various user groups to get fast insights into the current distribution of Indian bird species, hence contributing to the enhancement of usability. As a result, the dataset is provided in the ubiquitous Geo tiff raster geodata format to give information in a single file and facilitate simple use with all available GIS software.
As an example, we present the distribution map of four birds species: (a) Rufous-faced warbler (Abroscopus albogularis, (Moore, 1854)), (b) Indian courser (Cursorius coromandelicus, (Gmelin, JF, 1789)), (c) Red-necked falcon (Falco chicquera, Daudin, 1800) and (d) Spot-bellied eagle-owl (Bubu nipalensis, (Hodgson, 1836)) in Figure 1. These datasets contain the spatial distribution data for 968 birds along with the metadata. Details like species, sample size, MaxEnt Model validation results, etc., are included in the metadata table (Supplementary File S1). The dataset contains range maps in Geo Tiff raster format as presence absence maps covering the geographic area of India. Each raster map is approximately 1 km in resolution and in WGS 1984 datum.

3. Methods

3.1. Species Presence Data

We have utilized online, open-access citizen science databases (Global Biodiversity Information Facility GBIF; https://www.gbif.org/ (accessed on 25 September 2021)) [79] and eBird https://ebird.org (accessed on 25 December 2021) [8]. These databases have presence-only records of bird species occurring in India compiled by citizens during bird watching. This comprises ~28.4 million record locations of 1344 bird species in India across the Indian Sub-continent. We used only data from 1950 onwards to match the temporal duration of climatic data [80,81]. We also removed inaccurate species presence records using comprehensive range maps of species compiled by Birdlife International and Handbook of the Birds of the World [82], while keeping genuine species records through expert evaluation. To decrease the risk of errors in species identification and location, we used the research-grade presence-only occurrence data of each species using citizen science platforms like eBird and iNaturalist, where each record is reviewed by an experienced reviewer [83,84,85].
Citizen science data suffers from sampling biases [85,86]. We removed all the duplicates and low precision coordinates, and kept only unique records occurring within a 1 × 1 km2 cell to fit into the similar spatial resolution of the climatic data. We further used rarefication on occurrence using “SpThin” [87,88] package in R 3.4.0 [89]. We also eliminated species with fewer than thirty independent localities [90,91]. Furthermore, species with smaller sampling areas (i.e., n < 10,000 km2) were removed from further analysis [78]. This includes species with small range areas, e.g., small range, pelagic, coastal, or island species. We have also removed species with less than 30 presence records for further modelling.
The Sampling errors or biases in the geographic positioning co-ordinates and incomplete information about the species in biodiversity studies may have serious concerns that must be addressed [22,92,93,94]. Thus, we used the “sampbias” package [95] in R [89] to measure the impact of sample error or biases via procedure of data cleaning i.e., removing duplicate, incorrect or incomplete data. The findings of our data cleaning procedure suggested that our processed datasets have less sampling errors or bias than the initial datasets. Figure S1 in Supplementary File S2 has further information on bias correction.
After removing the biases and inconsistencies in species presence records, the corrected final presence occurrence database consists of ~1.9 million independent records of 1091 terrestrial avian species out of 1344 species. We used ~1.9 million location to develop models for of 1091 species [78,80,81].

3.2. Climate Data

The generation of SDMs is contingent on a variety of environmental conditions associated with the places where certain bird species exist. We compiled 29 environmental variables (EVs), which include 19 bioclimatic variables that summarized temperature and precipitation downloaded from WorldClim 1.4 layers [96], five variables related to topography [96], and five variables from ENVIREM [http://envirem.github.io/ accessed on 9 September 2023] [97]. The Supplementary Files S2 Table S1 contain a list of the total 29 EVs used in SDMs. The topographic and ENVIREM variables are the are proximally correlated with species’ physiological requirements (e.g., microclimate, edaphic conditions) [66,98,99,100,101,102]. Because MaxEnt’s built-in variable selection is dependable due to L1-regularization and is insensitive to correlation among variables, we preserved all 29 variables for SDM. If additional variable selection methods are imposed before MaxEnt is run for all species under consideration, the model’s accuracy could be compromised [103,104].

3.3. Species Distribution Modelling

In this study, we used MaxEnt 3.4.4 platform to predict the species distribution [105]. Using presence-only data, it uses a machine learning approach to produce reliable results [106]. To determine the model calibration region, we applied the minimal convex polygon (MCP) method to species occurrence data with a buffer of two-degree [106,107].
By considering locations of occurrence of all bird species across the Indian sub-continent, we used the target-group background selection strategy [22,24,108] to diminish the impact of spatial sampling bias [78,109,110,111]. The background data define the study’s environmental dimensions, while the presence data indicate conditions likely to be associated with species occurrence.
We used “ENMeval” [112,113] package in R [89] to fine-tuned MaxEnt models, which helps in choosing model parameters exhibiting the greatest performance.
By using the checkerboard2 approach to segment the occurrence data, we were able to do 4-fold cross-validations. We used ENMeval to fine-tune 48 distinct species models with RM (Regularization Multiplier) values ranging from 0.5 to 4.0 (in the increments of 0.5) and six distinct Feature Classes (FCs). The Feature Classes (FCs) combinations were L, LQ, H, LQH, LQHP and LQHPT, where L = linear, Q = quadratic, H = hinge, P = product, and T = threshold).
The test omission rate of the top model we deployed was the lowest, while the validation area under the AUC curve (receiver operating characteristic curve was the largest. [16,88]. In Supplementary File S1, details of the ideal model tuning parameters are given. We opt for the MaxEnt’s Cloglog output format because it reduces the impacts of sample selection bias, which can enhance model performance [105].
To create ‘present/absence’ binary maps from Cloglog raster outputs, we used the 10th percentile training presence threshold [88,91,114,115]. The 10th percentile training presence threshold improves species distributions and decreases overpredictions in final binary maps [115]. All the data needed for species distribution models is in Supplementary File S1.
To measure the effectiveness of species distribution models (SDMs), we evaluated the final models using multiple threshold-dependent and independent criteria [116,117,118,119]. We derived model training and validation AUC (AUCTRAIN, AUCVAL) and estimated the difference between the two (AUCDIFF). This difference is expected to be large in overfitted models [119]. We also calculated ORMTP (‘Minimum Training Presence’ omission rate) and OR10 (training omission rate of 10%) to quantify model overfitting [88,116,117,120]. Using the R package “kuenm” [121], the AUC ratio (pAUC Ratio) was calculated based on the partial ROC performance metric. We also calculated the Continuous Boyce Index (CBIVAL, CBITRAIN) for training and validation data. This index is a measure of the variation of the model predictions from the randomly distributed presence observations across the prediction gradients [122].
We retained data of 968 species out of total 1149 species having AUCTRAIN and CBITRAIN greater than 0.7, indicating appropriate model performance and better model abilities to discriminate between conditions of occurrence area and those of background area [116,123].
These resulting models of 968 bird species demonstrated mean AUCTRAIN = 0.86 and AUCVAL = 0.85. We also estimated the mean pAUC Ratio = 1.95, indicating that models performed better than the random models. We obtained mean CBIVAL = 0.89 and mean CBITRAIN = 0.97, indicating excellent model performance.
Information used for model validation and evaluation for species distribution models is provided in Supplementary File S1.

4. Potential Constraints and Future Directions

Our method is based on universal premises seen in all multi-species studies that use species distribution models. These models initially assume that species are in balance with the environment and that all relevant climatic parameters that may have an impact on species existence are taken into account in order to compute climatic tolerance from the observed distribution of the species. The primary disadvantages of this approach include the probable removal of crucial climatic variables from models and the potential impact of several other factors, such as habitat loss, hunting, and exploitation, on the existing and future distribution of bird species. Because of this, species distribution model assumptions are frequently broken [124].
The assumption that different species adapt to climate change individually is another weakness of species distribution models, which ignore interspecies interactions because species interactions both within and across trophic levels may significantly affect whether a particular taxon can persist in its current range or colonize new areas [125,126].
All species distribution models incorporate some degree of uncertainty. We attempted to lessen the sampling bias by target background selection strategy combined with rarefication of presence-only data and exploited ~70 years of presence only data to decrease temporal sampling disparities. This work might be regarded as among the earliest efforts in India to undertake a comprehensive assessment of distributions of birds based on presence only data. The increasing popularity of bird watching via citizen science projects and the enhancement of data quality and quantity provide unparalleled availability of bird distribution data for various purposes. We hope that future studies will regularly update analysis as done in this study using more data as they become available that will help meet diverse difficulties encountered in biodiversity protection.
This dataset is available at a coarse resolution of 1 km2. Hence, it will be more suitable for large-scale or national level analysis like conservation area prioritization and hotspot mapping. This dataset might not be ideal for local level analysis or planning purposes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/data8090144/s1, Supplementary File S1, Table S1: All species distribution models specifics, including data, model assessments, and parameters used for validation.; Supplementary File S2, Table S1: A complete list of environmental variables used in MaxEnt modelling; Supplementary File S2, Figure S1: Results of bias correction for presence data used in our study using “sampbias” packages. References [96,97,127] are cited in the supplementary file.

Author Contributions

Conceptualization, A.D.; data curation and methodology, A.D.; software, A.D.; validation, A.D. and R.S.; resources, A.D.; writing—original draft preparation, A.D.; writing—review and editing, R.S., A.S. and D.G.; supervision, R.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All original data are available from the respective portals, Global Biodiversity Information Facility [GBIF; https://www.gbif.org/], eBird [https://ebird.org/home] and Worldclim [https://www.worldclim.org/]. An open-access data repository for this study and the codes for data analyses can be accessed at https://github.com/arpitdeomurari/IndianBirds ClimateChange. The dataset is hosted on zenodo.org [https://zenodo.org/record/8221113] accessed on 20 September 2023.

Acknowledgments

We thank over 30,000 birdwatchers who contributed to eBird [http://ebird.org/india] and eBird volunteer editors for data curation [listed at http://ebird.org/india/about].

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ellis-Soto, D.; Merow, C.; Amatulli, G.; Parra, J.L.; Jetz, W. Continental-Scale 1 Km Hummingbird Diversity Derived from Fusing Point Records with Lateral and Elevational Expert Information. Ecography 2021, 44, 640–652. [Google Scholar] [CrossRef]
  2. Hurlbert, A.H.; Jetz, W. Species Richness, Hotspots, and the Scale Dependence of Range Maps in Ecology and Conservation. Proc. Natl. Acad. Sci. USA 2007, 104, 13384–13389. [Google Scholar] [CrossRef] [PubMed]
  3. Hurlbert, A.H.; White, E.P. Disparity between Range Map- and Survey-Based Analyses of Species Richness: Patterns, Processes and Implications. Ecol. Lett. 2005, 8, 319–327. [Google Scholar] [CrossRef]
  4. Ramesh, V.; Gopalakrishna, T.; Barve, S.; Melnick, D.J. Citizen Science Driven Species Distribution Models Estimate Drastically Smaller Range Sizes and Higher Threat Levels for Western Ghats Endemic Birds. Biol. Conserv. 2017, 210, 205–221. [Google Scholar] [CrossRef]
  5. Duan, H.; Yu, X.; Xia, S.; Liu, Y. Comparison of IUCN and Species Distribution Modeling-Estimated Ranges of Shorebirds in Coastal Mainland China. Glob. Ecol. Conserv. 2022, 38, e02236. [Google Scholar] [CrossRef]
  6. Higino, G.T.; Banville, F.; Dansereau, G.; Muñoz, N.R.F.; Windsor, F.; Poisot, T. Mismatch between IUCN Range Maps and Species Interactions Data Illustrated Using the Serengeti Food Web. PeerJ 2023, 11, e14620. [Google Scholar] [CrossRef] [PubMed]
  7. Amano, T.; Lamming, J.D.L.; Sutherland, W.J. Spatial Gaps in Global Biodiversity Information and the Role of Citizen Science. BioScience 2016, 66, 393–400. [Google Scholar] [CrossRef]
  8. eBird. eBird: An Online Database of Bird Distribution and Abundance [Web Application]. Available online: http://www.ebird.org (accessed on 25 December 2021).
  9. Elith, J.; Graham, H.C.; Anderson, P.R.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
  10. Elith, J.; Leathwick, J.R. Species Distribution Models: Ecological Explanation and Prediction across Space and Time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]
  11. Guisan, A.; Thuiller, W. Predicting Species Distribution: Offering More than Simple Habitat Models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef]
  12. Phillips, S.J.; Dudík, M.; Schapire, R.E. A Maximum Entropy Approach to Species Distribution Modeling. In Proceedings of the Twenty-First International Conference on Machine Learning—ICML ’04, Banff, AB, Canada, 4–8 July 2004; Volume 69, p. 83. [Google Scholar]
  13. Bazzichetto, M.; Malavasi, M.; Bartak, V.; Acosta, A.T.R.; Rocchini, D.; Carranza, M.L. Plant Invasion Risk: A Quest for Invasive Species Distribution Modelling in Managing Protected Areas. Ecol. Indic. 2018, 95, 311–319. [Google Scholar] [CrossRef]
  14. Blair, M.E.; Le, M.D.; Xu, M. Species Distribution Modeling to Inform Transboundary Species Conservation and Management under Climate Change: Promise and Pitfalls. Front. Biogeogr. 2022, 14, 1–11. [Google Scholar] [CrossRef]
  15. Domic, A.I.; Capriles, J.M. Distribution Shifts in Habitat Suitability and Hotspot Refugia of Andean Tree Species from the Last Glacial Maximum to the Anthropocene. Neotrop. Biodivers. 2021, 7, 297–309. [Google Scholar] [CrossRef]
  16. Kass, J.M.; Anderson, R.P.; Espinosa-Lucas, A.; Juárez-Jaimes, V.; Martínez-Salas, E.; Botello, F.; Tavera, G.; Flores-Martínez, J.J.; Sánchez-Cordero, V. Biotic Predictors with Phenological Information Improve Range Estimates for Migrating Monarch Butterflies in Mexico. Ecography 2020, 43, 341–352. [Google Scholar] [CrossRef]
  17. Syfert, M.M.; Joppa, L.; Smith, M.J.; Coomes, D.A.; Bachman, S.P.; Brummitt, N.A. Using Species Distribution Models to Inform IUCN Red List Assessments. Biol. Conserv. 2014, 177, 174–184. [Google Scholar] [CrossRef]
  18. Ariño, A.H.; Chavan, V.; Faith, D.P. Assessment of User Needs of Primary Biodiversity Data: Analysis, Concerns, and Challenges. Biodivers. Inform. 2015, 8, 59–93. [Google Scholar] [CrossRef]
  19. Barry, S.; Elith, J. Error and Uncertainty in Habitat Models. J. Appl. Ecol. 2006, 43, 413–423. [Google Scholar] [CrossRef]
  20. Beck, J.; Böller, M.; Erhardt, A.; Schwanghart, W. Spatial Bias in the GBIF Database and Its Effect on Modeling Species’ Geographic Distributions. Ecol. Inform. 2014, 19, 10–15. [Google Scholar] [CrossRef]
  21. Meyer, C.; Weigelt, P.; Kreft, H. Multidimensional Biases, Gaps and Uncertainties in Global Plant Occurrence Information. Ecol. Lett. 2016, 19, 992–1006. [Google Scholar] [CrossRef]
  22. Barber, R.A.; Ball, S.G.; Morris, R.K.A.; Gilbert, F. Target-Group Backgrounds Prove Effective at Correcting Sampling Bias in Maxent Models. Divers. Distrib. 2022, 28, 128–141. [Google Scholar] [CrossRef]
  23. Boakes, E.H.; McGowan, P.J.K.; Fuller, R.A.; Chang-Qing, D.; Clark, N.E.; O’Connor, K.; Mace, G.M. Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data. PLoS Biol. 2010, 8, e1000385. [Google Scholar] [CrossRef] [PubMed]
  24. Botella, C.; Joly, A.; Monestiez, P.; Bonnet, P.; Munoz, F. Bias in Presence-Only Niche Models Related to Sampling Effort and Species Niches: Lessons for Background Point Selection. PLoS ONE 2020, 15, e0232078. [Google Scholar] [CrossRef] [PubMed]
  25. Costa, G.C.; Nogueira, C.; Machado, R.B.; Colli, G.R. Sampling Bias and the Use of Ecological Niche Modeling in Conservation Planning: A Field Evaluation in a Biodiversity Hotspot. Biodivers. Conserv. 2010, 19, 883–899. [Google Scholar] [CrossRef]
  26. El-Gabbas, A.; Dormann, C.F. Improved Species-Occurrence Predictions in Data-Poor Regions: Using Large-Scale Data and Bias Correction with down-Weighted Poisson Regression and Maxent. Ecography 2018, 41, 1049–1231. [Google Scholar] [CrossRef]
  27. Breiner, F.T.; Guisan, A.; Nobis, M.P.; Bergamini, A. Including Environmental Niche Information to Improve IUCN Red List Assessments. Divers. Distrib. 2017, 23, 484–495. [Google Scholar] [CrossRef]
  28. McClure, C.J.W.; Dunn, L.; Buechley, E.R.; Juergens, P.; Oleyar, D.; Goodrich, L.J.; Therrien, J.-F. Conservation Assessment of Raptors within the USA and Canada. Biol. Conserv. 2022, 272, 109633. [Google Scholar] [CrossRef]
  29. Morales, N.S.; Fernández, I.C.; Carrasco, B.; Orchard, C. Combining Niche Modelling, Land-Use Change, and Genetic Information to Assess the Conservation Status of Pouteria Splendens Populations in Central Chile. Int. J. Ecol. 2015, 2015, 612194. [Google Scholar] [CrossRef]
  30. Papeş, M.; Gaubert, P. Modelling Ecological Niches from Low Numbers of Occurrences: Assessment of the Conservation Status of Poorly Known Viverrids (Mammalia, Carnivora) across Two Continents. Divers. Distrib. 2007, 13, 890–902. [Google Scholar] [CrossRef]
  31. Pena, J.C.C.; Kamino, L.H.Y.; Rodrigues, M.; Mariano-Neto, E.; de Siqueira, M.F. Assessing the Conservation Status of Species with Limited Available Data and Disjunct Distribution. Biol. Conserv. 2014, 170, 130–136. [Google Scholar] [CrossRef]
  32. Araújo, M.B.; Thuiller, W.; Pearson, R.G. Climate Warming and the Decline of Amphibians and Reptiles in Europe. J. Biogeogr. 2006, 33, 1712–1728. [Google Scholar] [CrossRef]
  33. Beresford, A.E.; Buchanan, G.M.; Donald, P.F.; Butchart, S.H.M.; Fishpool, L.D.C.; Rondinini, C. Poor Overlap between the Distribution of Protected Areas and Globally Threatened Birds in Africa. Anim. Conserv. 2011, 14, 99–107. [Google Scholar] [CrossRef]
  34. Coetzee, B.W.T.; Robertson, M.P.; Erasmus, B.F.N.; van Rensburg, B.J.; Thuiller, W. Ensemble Models Predict Important Bird Areas in Southern Africa Will Become Less Effective for Conserving Endemic Birds under Climate Change. Glob. Ecol. Biogeogr. 2009, 18, 701–710. [Google Scholar] [CrossRef]
  35. Gill, J.A.; Alves, J.A.; Gunnarsson, T.G. Mechanisms Driving Phenological and Range Change in Migratory Species. Philos. Trans. R. Soc. B 2019, 374, 20180047. [Google Scholar] [CrossRef] [PubMed]
  36. Huntley, B.; Collingham, Y.C.; Willis, S.G.; Green, R.E. Potential Impacts of Climatic Change on European Breeding Birds. PLoS ONE 2008, 3, e1439. [Google Scholar] [CrossRef]
  37. Scott, D.; Lemieux, C. Climate Change and Protected Area Policy and Planning in Canada. For. Chron. 2005, 81, 696–703. [Google Scholar] [CrossRef]
  38. Sintayehu, D.W. Impact of Climate Change on Biodiversity and Associated Key Ecosystem Services in Africa: A Systematic Review. Ecosyst. Health Sustain. 2018, 4, 225–239. [Google Scholar] [CrossRef]
  39. Keller, V.; Herrando, S.; Voříšek, P.; Franch, M.; Kipson, M.; Milanesi, P.; Martí, D.; Anton, M.; Klvaňová, A.; Kalyakin, M.V.; Bauer, H.-G.; Foppen, R.P.B. European Breeding Bird Atlas 2: Distribution, Abundance and Change; European Bird Census Council & Lynx Edicions: Barcelona, Spain, 2020. [Google Scholar]
  40. Chhetri, B.; Badola, H.K.; Barat, S. Predicting Climate-Driven Habitat Shifting of the near Threatened Satyr Tragopan (Tragopan Satyra; Galliformes) in the Himalayas. Avian Biol. Res. 2018, 11, 221–230. [Google Scholar] [CrossRef]
  41. Chhetri, B.; Badola, H.K.; Barat, S. Modelling Climate Change Impacts on Distribution of Himalayan Pheasants. Ecol. Indic. 2021, 123, 107368. [Google Scholar] [CrossRef]
  42. Jha, K.K.; Jha, R. Study of Vulture Habitat Suitability and Impact of Climate Change in Central India Using MaxEnt. J. Resour. Ecol. 2021, 12, 30–42. [Google Scholar] [CrossRef]
  43. Jose, V.S.; Nameer, P.O. The Expanding Distribution of the Indian Peafowl (Pavo Cristatus) as an Indicator of Changing Climate in Kerala, Southern India: A Modelling Study Using MaxEnt. Ecol. Indic. 2020, 110, 105930. [Google Scholar] [CrossRef]
  44. Menon, S.; Peterson, A.T. Projected Climate Change Effects on Nuthatch Distribution. Raffles Bull. Zool. 2009, 57, 569–575. [Google Scholar]
  45. Singh, H.; Kumar, N.; Kumar, M.; Singh, R. Modelling Habitat Suitability of Western Tragopan (Tragopan Melanocephalus) a Range-Restricted Vulnerable Bird Species of the Himalayan Region, in Response to Climate Change. Clim. Risk Manag. 2020, 29, 100241. [Google Scholar] [CrossRef]
  46. Sreekumar, E.R.; Nameer, P.O. Impact of Climate Change on Two High-Altitude Restricted and Endemic Flycatchers of The Western Ghats, India. Curr. Sci. 2021, 121, 1335. [Google Scholar] [CrossRef]
  47. Sreekumar, E.R.; Nameer, P.O. A MaxEnt Modelling Approach to Understand the Climate Change Effects on the Distributional Range of White-Bellied Sholakili Sholicola Albiventris (Blanford, 1868) in the Western Ghats, India. Ecol. Inform. 2022, 70, 101702. [Google Scholar] [CrossRef]
  48. Sutton, L.J.; McClure, C.J.W.; Kini, S.; Leonardi, G. Climatic Constraints on Laggar Falcon (Falco Jugger) Distribution Predicts Multidirectional Range Movements under Future Climate Change Scenarios. J. Raptor Res. 2020, 54, 1–17. [Google Scholar] [CrossRef]
  49. Yousefi, M.; Ahmadi, M.; Nourani, E.; Rezaei, A.; Kafash, A.; Khani, A.; Sehhatisabet, M.E.; Adibi, M.A.; Goudarzi, F.; Kaboli, M. Habitat Suitability and Impacts of Climate Change on the Distribution of Wintering Population of Asian Houbara Bustard Chlamydotis Macqueenii in Iran. Bird Conserv. Int. 2017, 27, 294–304. [Google Scholar] [CrossRef]
  50. Peterson, A.; Papeş, M. Potential Geographic Distribution of the Bugun Liocichla Liocichla Bugunorum, a Poorly-Known Species from North-Eastern India. Indian Birds 2006, 2, 146–149. [Google Scholar]
  51. Kaul, R.; Kalsi, R.S.; Singh, R.; Basnet, H.; Awan, M.N. Cheer Pheasant (Catreus Wallichii) and the Conservation Paradox: Importance of Unprotected Areas. Diversity 2022, 14, 785. [Google Scholar] [CrossRef]
  52. Singh, P.; Saran, S.; Kocaman, S. Role of Maximum Entropy and Citizen Science to Study Habitat Suitability of Jacobin Cuckoo in Different Climate Change Scenarios. ISPRS Int. J. Geo-Inf. 2021, 10, 463. [Google Scholar] [CrossRef]
  53. Chitale, V.S.; Behera, M.D.; Roy, P.S. Future of Endemic Flora of Biodiversity Hotspots in India. PLoS ONE 2014, 9, e115264. [Google Scholar] [CrossRef]
  54. Ghosh, B.G.; Garai, S.; Rahaman, S.M.; Khatun, M.; Mohammad, N.; Mishra, Y.; Ranjan, A.; Tiwari, S. Assessing Potential Habitat Distribution Range of the Endangered Tree Species Pterocarpus Marsupium Roxb. Under the Climate Change Scenario in India. Trees For. People 2021, 6, 100124. [Google Scholar] [CrossRef]
  55. Hebbar, K.B.; Abhin, P.S.; Sanjo Jose, V.; Neethu, P.; Santhosh, A.; Shil, S.; Prasad, P.V.V. Predicting the Potential Suitable Climate for Coconut (Cocos nucifera L.) Cultivation in India under Climate Change Scenarios Using the MaxEnt Model. Plants 2022, 11, 731. [Google Scholar] [CrossRef]
  56. Kailash, B.R.; Charles, B.; Ravikanth, G.; Setty, S.; Kadirvelu, K. Identifying the Potential Global Distribution and Conservation Areas for Terminalia Chebula, an Important Medicinal Tree Species under Changing Climate Scenario. Trop. Ecol. 2022, 63, 584–595. [Google Scholar] [CrossRef]
  57. Maikhuri, R.K.; Phondani, P.C.; Dhyani, D.; Rawat, L.S.; Jha, N.K.; Kandari, L.S. Assessment of Climate Change Impacts and Its Implications on Medicinal Plants-Based Traditional Healthcare System in Central Himalaya, India. Iran. J. Sci. Technol. Trans. Sci. 2018, 42, 1827–1835. [Google Scholar] [CrossRef]
  58. Manish, K.; Telwala, Y.; Nautiyal, D.C.; Pandit, M.K. Modelling the Impacts of Future Climate Change on Plant Communities in the Himalaya: A Case Study from Eastern Himalaya, India. Model. Earth Syst. Environ. 2016, 2, 92. [Google Scholar] [CrossRef]
  59. Priti, H.; Aravind, N.A.; Uma Shaanker, R.; Ravikanth, G. Modeling Impacts of Future Climate on the Distribution of Myristicaceae Species in the Western Ghats, India. Ecol. Eng. 2016, 89, 14–23. [Google Scholar] [CrossRef]
  60. Mathur, M.; Mathur, P.; Purohit, H. Ecological Niche Modelling of a Critically Endangered Species Commiphora Wightii (Arn.) Bhandari Using Bioclimatic and Non-Bioclimatic Variables. Ecol. Process. 2023, 12, 8. [Google Scholar] [CrossRef]
  61. Irfan-Ullah, M.; Amarnath, G.; Murthy, M.S.R.; Peterson, A.T. Mapping the Geographic Distribution of Aglaia Bourdillonii Gamble (Meliaceae), an Endemic and Threatened Plant, Using Ecological Niche Modeling. Biodivers. Conserv. 2007, 16, 1917–1925. [Google Scholar] [CrossRef]
  62. Paul, A.; Khan, M.L.; Das, A.K.; Dutta, P.K. Diversity and Distribution of Rhododendrons in Arunachal Himalaya, India. J. Am. Rhododendr. Soc. 2010, 3, 200–205. [Google Scholar]
  63. Bobrowski, M.; Gerlitz, L.; Schickhoff, U. Modelling the Potential Distribution of Betula Utilis in the Himalaya. Glob. Ecol. Conserv. 2017, 11, 69–83. [Google Scholar] [CrossRef]
  64. Bhandari, M.S.; Meena, R.K.; Shankhwar, R.; Shekhar, C.; Saxena, J.; Kant, R.; Pandey, V.V.; Barthwal, S.; Pandey, S.; Chandra, G.; et al. Prediction Mapping Through Maxent Modeling Paves the Way for the Conservation of Rhododendron Arboreum in Uttarakhand Himalayas. J. Indian Soc. Remote Sens. 2020, 48, 411–422. [Google Scholar] [CrossRef]
  65. Chatterjee, P.; Tripathy, B.; Chandra, K.; Saha, G.K.; Mondal, K. Climate Change Alarms the Survival of Near Threatened Species Malayan Giant Squirrel (Ratufa Bicolor Sparrman, 1778) in India. JMAM 2020, 45, 289–302. [Google Scholar] [CrossRef]
  66. Kanagaraj, R.; Araujo, M.B.; Barman, R.; Davidar, P.; De, R.; Digal, D.K.; Gopi, G.V.; Johnsingh, A.J.T.; Kakati, K.; Kramer-Schadt, S.; et al. Predicting Range Shifts of Asian Elephants under Global Change. Divers. Distrib. 2019, 25, 822–838. [Google Scholar] [CrossRef]
  67. Singh, P.B.; Mainali, K.; Jiang, Z.; Thapa, A.; Subedi, N.; Awan, M.N.; Ilyas, O.; Luitel, H.; Zhou, Z.; Hu, H. Projected Distribution and Climate Refugia of Endangered Kashmir Musk Deer Moschus Cupreus in Greater Himalaya, South Asia. Sci. Rep. 2020, 10, 1511. [Google Scholar] [CrossRef]
  68. Jennings, A.P.; Veron, G. Predicted Distributions and Ecological Niches of 8 Civet and Mongoose Species in Southeast Asia. J. Mammal. 2011, 92, 316–327. [Google Scholar] [CrossRef]
  69. Kumara, H.N.; Irfan-Ullah, M.; Kumar, S. Mapping Potential Distribution of Slender Loris Subspecies in Peninsular India. Endanger. Species Res. 2009, 7, 29–38. [Google Scholar] [CrossRef]
  70. Thorn, J.S.; Nijman, V.; Smith, D.; Nekaris, K.A.I. Ecological Niche Modelling as a Technique for Assessing Threats and Setting Conservation Priorities for Asian Slow Lorises (Primates: Nycticebus). Divers. Distrib. 2009, 15, 289–298. [Google Scholar] [CrossRef]
  71. Anand, V.; Oinam, B.; Singh, I.H. Predicting the Current and Future Potential Spatial Distribution of Endangered Rucervus Eldii Eldii (Sangai) Using MaxEnt Model. Environ. Monit. Assess. 2021, 193, 147. [Google Scholar] [CrossRef]
  72. Mungi, N.A.; Qureshi, Q.; Jhala, Y.V. Expanding Niche and Degrading Forests: Key to the Successful Global Invasion of Lantana Camara (Sensu Lato). Glob. Ecol. Conserv. 2020, 23, e01080. [Google Scholar] [CrossRef]
  73. Bushi, D.; Mahato, R.; Nimasow, O.D.; Nimasow, G. MaxEnt-Based Prediction of the Potential Invasion of Lantana Camara L. under Climate Change Scenarios in Arunachal Pradesh, India. Acta Ecol. Sin. 2023, 43, 674–683. [Google Scholar] [CrossRef]
  74. Maruthadurai, R.; Das, B.; Ramesh, R. Predicting the Invasion Risk of Rugose Spiraling Whitefly, Aleurodicus Rugioperculatus, in India Based on CMIP6 Projections by MaxEnt. Pest Manag. Sci. 2023, 79, 295–305. [Google Scholar] [CrossRef] [PubMed]
  75. Choudhary, J.S.; Mali, S.S.; Fand, B.B.; Das, B. Predicting the Invasion Potential of Indigenous Restricted Mango Fruit Borer, Citripestis Eutraphera (Lepidoptera: Pyralidae) in India Based on MaxEnt Modelling. Curr. Sci. 2019, 116, 636–642. [Google Scholar] [CrossRef]
  76. Kumar, S.; Graham, J.; West, A.M.; Evangelista, P.H. Using District-Level Occurrences in MaxEnt for Predicting the Invasion Potential of an Exotic Insect Pest in India. Comput. Electron. Agric. 2014, 103, 55–62. [Google Scholar] [CrossRef]
  77. Padalia, H.; Srivastava, V.; Kushwaha, S.P.S. Modeling Potential Invasion Range of Alien Invasive Species, Hyptis Suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP. Ecol. Inform. 2014, 22, 36–43. [Google Scholar] [CrossRef]
  78. Deomurari, A.; Sharma, A.; Ghose, D.; Singh, R. Projected Shifts in Bird Distribution in India under Climate Change. Diversity 2023, 15, 404. [Google Scholar] [CrossRef]
  79. GBIF Occurrence Download. Available online: http://www.gbif.org/ (accessed on 25 September 2021).
  80. Jayadevan, P.; Jayapal, R.; Pittie, A. A Checklist of the Birds of India. Indian BIRDS 2016, 11, 113–172. [Google Scholar]
  81. Sullivan, B.L.; Wood, C.L.; Iliff, M.J.; Bonney, R.E.; Fink, D. eBird: A Citizen-Based Bird Observation Network in the Biological Sciences. Biol. Conserv. 2009, 142, 2282–2292. [Google Scholar] [CrossRef]
  82. BirdLife International and Handbook of the Birds of the World Bird Species Distribution Maps of the World. Version 2020.1. 2020. Available online: http://datazone.birdlife.org/species/requestdis (accessed on 25 December 2021).
  83. Beninde, J.; Delaney, T.W.; Gonzalez, G.; Shaffer, H.B. Harnessing iNaturalist to Quantify Hotspots of Urban Biodiversity: The Los Angeles Case Study. Front. Ecol. Evol. 2023, 11, 983371. [Google Scholar] [CrossRef]
  84. Mesaglio, T.; Callaghan, C.T.; Mesaglio, T.; Callaghan, C.T. An Overview of the History, Current Contributions and Future Outlook of iNaturalist in Australia. Wildl. Res. 2021, 48, 289–303. [Google Scholar] [CrossRef]
  85. Isaac, N.J.B.; van Strien, A.J.; August, T.A.; de Zeeuw, M.P.; Roy, D.B. Statistics for Citizen Science: Extracting Signals of Change from Noisy Ecological Data. Methods Ecol. Evol. 2014, 5, 1052–1060. [Google Scholar] [CrossRef]
  86. Lagoze, C. eBird: Curating Citizen Science Data for Use by Diverse Communities. Int. J. Digit. Curation 2014, 9, 71–82. [Google Scholar] [CrossRef]
  87. Aiello-Lammens, M.E.; Boria, R.A.; Radosavljevic, A.; Vilela, B.; Anderson, R.P. spThin: An R Package for Spatial Thinning of Species Occurrence Records for Use in Ecological Niche Models. Ecography 2015, 38, 541–545. [Google Scholar] [CrossRef]
  88. Radosavljevic, A.; Anderson, R.P. Making Better Maxent Models of Species Distributions: Complexity, Overfitting and Evaluation. J. Biogeogr. 2014, 41, 629–643. [Google Scholar] [CrossRef]
  89. R Core Team, R. A Language and Environment for Statistical Computing; R Core Team R: Vienna, Austria, 2022. [Google Scholar]
  90. Van Eupen, C.; Maes, D.; Herremans, M.; Swinnen, K.R.R.; Somers, B.; Luca, S. The Impact of Data Quality Filtering of Opportunistic Citizen Science Data on Species Distribution Model Performance. Ecol. Model. 2021, 444, 109453. [Google Scholar] [CrossRef]
  91. Wisz, M.S.; Hijmans, R.J.; Li, J.; Peterson, A.T.; Graham, C.H.; Guisan, A.; Elith, J.; Dudík, M.; Ferrier, S.; Huettmann, F.; et al. Effects of Sample Size on the Performance of Species Distribution Models. Divers. Distrib. 2008, 14, 763–773. [Google Scholar] [CrossRef]
  92. Cayuela, L.; Golicher, D.J.; Newton, A.C.; Kolb, M.; de Alburquerque, F.S.; Arets, E.J.M.M.; Alkemade, J.R.M.; Pérez, A.M. Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation. Trop. Conserv. Sci. 2009, 2, 319–352. [Google Scholar] [CrossRef]
  93. Kramer-Schadt, S.; Niedballa, J.; Pilgrim, J.D.; Schröder, B.; Lindenborn, J.; Reinfelder, V.; Stillfried, M.; Heckmann, I.; Scharf, A.K.; Augeri, D.M.; et al. The Importance of Correcting for Sampling Bias in MaxEnt Species Distribution Models. Divers. Distrib. 2013, 19, 1366–1379. [Google Scholar] [CrossRef]
  94. Rocchini, D.; Hortal, J.; Lengyel, S.; Lobo, J.M.; Jiménez-Valverde, A.; Ricotta, C.; Bacaro, G.; Chiarucci, A. Accounting for Uncertainty When Mapping Species Distributions: The Need for Maps of Ignorance. Prog. Phys. Geogr. Earth Environ. 2011, 35, 211–226. [Google Scholar] [CrossRef]
  95. Zizka, A.; Antonelli, A.; Silvestro, D. Sampbias, a Method for Quantifying Geographic Sampling Biases in Species Distribution Data. Ecography 2021, 44, 25–32. [Google Scholar] [CrossRef]
  96. Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very High Resolution Interpolated Climate Surfaces for Global Land Areas. Int. J. Climatol. 2005, 25, 1965–1978. [Google Scholar] [CrossRef]
  97. Title, P.O.; Bemmels, J.B. ENVIREM: An Expanded Set of Bioclimatic Variables Improves Ecological Niche Modeling Performance. Prep. Submiss. Methods Ecol. Evol. 2016, 1–48. [Google Scholar] [CrossRef]
  98. Feeley, K.J.; Bravo-Avila, C.; Fadrique, B.; Perez, T.M.; Zuleta, D. Climate-Driven Changes in the Composition of New World Plant Communities. Nat. Clim. Chang. 2020, 10, 965–970. [Google Scholar] [CrossRef]
  99. Kougioumoutzis, K.; Kokkoris, I.P.; Panitsa, M.; Kallimanis, A.; Strid, A.; Dimopoulos, P. Plant Endemism Centres and Biodiversity Hotspots in Greece. Biology 2021, 10, 72. [Google Scholar] [CrossRef] [PubMed]
  100. Lembrechts, J.J.; Nijs, I.; Lenoir, J. Incorporating Microclimate into Species Distribution Models. Ecography 2019, 42, 1267–1279. [Google Scholar] [CrossRef]
  101. Pecchi, M.; Marchi, M.; Burton, V.; Giannetti, F.; Moriondo, M.; Bernetti, I.; Bindi, M.; Chirici, G. Species Distribution Modelling to Support Forest Management. A Literature Review. Ecol. Model. 2019, 411, 108817. [Google Scholar] [CrossRef]
  102. Urbina-Cardona, N.; Blair, M.E.; Londoño, M.C.; Loyola, R.; Velásquez-Tibatá, J.; Morales-Devia, H. Species Distribution Modeling in Latin America: A 25-Year Retrospective Review. Trop. Conserv. Sci. 2019, 12, 194008291985405. [Google Scholar] [CrossRef]
  103. Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A Statistical Explanation of MaxEnt for Ecologists. Divers. Distrib. 2011, 17, 43–57. [Google Scholar] [CrossRef]
  104. Feng, X.; Park, D.S.; Liang, Y.; Pandey, R.; Papeş, M. Collinearity in Ecological Niche Modeling: Confusions and Challenges. Ecol. Evol. 2019, 9, 10365–10376. [Google Scholar] [CrossRef]
  105. Phillips, S.J.; Anderson, R.P.; Dudík, M.; Schapire, R.E.; Blair, M.E. Opening the Black Box: An Open-Source Release of Maxent. Ecography 2017, 40, 887–893. [Google Scholar] [CrossRef]
  106. Barve, N.; Barve, V.; Jiménez-Valverde, A.; Lira-Noriega, A.; Maher, S.P.; Peterson, A.T.; Soberón, J.; Villalobos, F. The Crucial Role of the Accessible Area in Ecological Niche Modeling and Species Distribution Modeling. Ecol. Model. 2011, 222, 1810–1819. [Google Scholar] [CrossRef]
  107. Elith, J.; Kearney, M.; Phillips, S. The Art of Modelling Range-Shifting Species. Methods Ecol. Evol. 2010, 1, 330–342. [Google Scholar] [CrossRef]
  108. Anderson, R.P.; Raza, A. The Effect of the Extent of the Study Region on GIS Models of Species Geographic Distributions and Estimates of Niche Evolution: Preliminary Tests with Montane Rodents (Genus Nephelomys) in Venezuela. J. Biogeogr. 2010, 37, 1378–1393. [Google Scholar] [CrossRef]
  109. Barbet-Massin, M.; Jiguet, F.; Albert, C.H.; Thuiller, W. Selecting Pseudo-Absences for Species Distribution Models: How, Where and How Many? Methods Ecol. Evol. 2012, 3, 327–338. [Google Scholar] [CrossRef]
  110. Inman, R.; Franklin, J.; Esque, T.; Nussear, K. Comparing Sample Bias Correction Methods for Species Distribution Modeling Using Virtual Species. Ecosphere 2021, 12, e03422. [Google Scholar] [CrossRef]
  111. Phillips, S.J.; Dudík, M.; Elith, J.; Graham, C.H.; Lehmann, A.; Leathwick, J.; Ferrier, S. Sample Selection Bias and Presence-Only Distribution Models: Implications for Background and Pseudo-Absence Data. Ecol. Appl. 2009, 19, 181–197. [Google Scholar] [CrossRef]
  112. Kass, J.M.; Muscarella, R.; Galante, P.J.; Bohl, C.L.; Pinilla-Buitrago, G.E.; Boria, R.A.; Soley-Guardia, M.; Anderson, R.P. ENMeval 2.0: Redesigned for Customizable and Reproducible Modeling of Species’ Niches and Distributions. Methods Ecol. Evol. 2021, 12, 1602–1608. [Google Scholar] [CrossRef]
  113. Muscarella, R.; Galante, P.J.; Soley-Guardia, M.; Boria, R.A.; Kass, J.M.; Uriarte, M.; Anderson, R.P. ENMeval: An R Package for Conducting Spatially Independent Evaluations and Estimating Optimal Model Complexity for Maxent Ecological Niche Models. Methods Ecol. Evol. 2014, 5, 1198–1205. [Google Scholar] [CrossRef]
  114. Liu, C.; Berry, P.M.; Dawson, T.P.; Liu, R.G.P.; Berry, C.; Dawson, P.M.; Pearson, T.P.; Liu, C.; Berry, P.M.; Dawson, T.P.; et al. Selecting Thresholds of Occurrence in the Prediction of Species Distributions. Ecography 2005, 28, 385–393. [Google Scholar] [CrossRef]
  115. Liu, C.; White, M.; Newell, G. Selecting Thresholds for the Prediction of Species Occurrence with Presence-Only Data. J. Biogeogr. 2013, 40, 778–789. [Google Scholar] [CrossRef]
  116. Anderson, R.P.; Martínez-Meyer, E.; Nakamura, M.; Araújo, M.B.; Peterson, A.T.; Soberón, J.; Pearson, R.G. Ecological Niches and Geographic Distributions (MPB-49); Princeton University Press: Princeton, NJ, USA, 2011; ISBN 978-1-4008-4067-0. [Google Scholar]
  117. Fielding, A.H.; Bell, J.F. A Review of Methods for the Assessment of Prediction Errors in Conservation Presence/Absence Models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
  118. Lobo, J.M.; Jiménez-Valverde, A.; Real, R. AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Glob. Ecol. Biogeogr. 2008, 17, 145–151. [Google Scholar] [CrossRef]
  119. Warren, D.L.; Seifert, S.N. Ecological Niche Modeling in Maxent: The Importance of Model Complexity and the Performance of Model Selection Criteria. Ecol. Appl. 2011, 21, 335–342. [Google Scholar] [CrossRef] [PubMed]
  120. Pearson, R.G.; Raxworthy, C.J.; Nakamura, M.; Townsend Peterson, A. Predicting Species Distributions from Small Numbers of Occurrence Records: A Test Case Using Cryptic Geckos in Madagascar. J. Biogeogr. 2007, 34, 102–117. [Google Scholar] [CrossRef]
  121. Cobos, M.E.; Peterson, A.T.; Barve, N.; Osorio-Olvera, L. Kuenm: An R Package for Detailed Development of Ecological Niche Models Using Maxent. PeerJ 2019, 7, e6281. [Google Scholar] [CrossRef] [PubMed]
  122. Boyce, M.S.; Vernier, P.R.; Nielsen, S.E.; Schmiegelow, F.K.A. Evaluating Resource Selection Functions. Ecol. Model. 2002, 157, 281–300. [Google Scholar] [CrossRef]
  123. Hanley, J.A.; McNeil, B.J. The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology 1982, 143, 29–36. [Google Scholar] [CrossRef] [PubMed]
  124. Santini, L.; Benítez-López, A.; Maiorano, L.; Čengić, M.; Huijbregts, M.A.J. Assessing the Reliability of Species Distribution Projections in Climate Change Research. Divers. Distrib. 2021, 27, 1035–1050. [Google Scholar] [CrossRef]
  125. Early, R.; Keith, S.A. Geographically Variable Biotic Interactions and Implications for Species Ranges. Glob. Ecol. Biogeogr. 2019, 28, 42–53. [Google Scholar] [CrossRef]
  126. Pigot, A.L.; Tobias, J.A. Species Interactions Constrain Geographic Range Expansion over Evolutionary Time. Ecol. Lett. 2013, 16, 330–338. [Google Scholar] [CrossRef]
  127. Dinerstein, E.; Olson, D.; Joshi, A.; Vynne, C.; Burgess, N.D.; Wikramanayake, E.; Hahn, N.; Palminteri, S.; Hedao, P.; Noss, R.; et al. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience 2017, 67, 534–545. [Google Scholar] [CrossRef]
Data 08 00144 g001
Figure 1. Potential distribution range map of (a) Rufous-faced warbler (Abroscopus albogularis, (Moore, 1854)), (b) Indian courser (Cursorius coromandelicus, (Gmelin, JF, 1789)), (c) Red-necked falcon (Falco chicquera, Daudin, 1800) and (d) spot-bellied eagle-owl (Bubu nipalensis, (Hodgson, 1836)).
Figure 1. Potential distribution range map of (a) Rufous-faced warbler (Abroscopus albogularis, (Moore, 1854)), (b) Indian courser (Cursorius coromandelicus, (Gmelin, JF, 1789)), (c) Red-necked falcon (Falco chicquera, Daudin, 1800) and (d) spot-bellied eagle-owl (Bubu nipalensis, (Hodgson, 1836)).
Data 08 00144 g001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Deomurari, A.; Sharma, A.; Ghose, D.; Singh, R. Potential Range Map Dataset of Indian Birds. Data 2023, 8, 144. https://doi.org/10.3390/data8090144

AMA Style

Deomurari A, Sharma A, Ghose D, Singh R. Potential Range Map Dataset of Indian Birds. Data. 2023; 8(9):144. https://doi.org/10.3390/data8090144

Chicago/Turabian Style

Deomurari, Arpit, Ajay Sharma, Dipankar Ghose, and Randeep Singh. 2023. "Potential Range Map Dataset of Indian Birds" Data 8, no. 9: 144. https://doi.org/10.3390/data8090144

APA Style

Deomurari, A., Sharma, A., Ghose, D., & Singh, R. (2023). Potential Range Map Dataset of Indian Birds. Data, 8(9), 144. https://doi.org/10.3390/data8090144

Article Metrics

Back to TopTop