Abstract
Species distribution models (SDMs) have become the most widely used method for wildlife management and have been applied in the fields of ecology, biogeography, and conservation.
Species distribution modelling commonly requires two categories of data: (1) species data and (2) environmental data. Species data can be nominal (presence/absence records), ordinal (ranked abundances), or ratio (abundance and richness). Environmental data refers to both biotic and abiotic conditions. The most common types of environmental data in species distribution modelling are climatic and topographical variables since these two sets of variables represent, respectively, the large-scale conditions relevant to species’ physiology and small-scale conditions which affect solar energy input and availability of moisture. There are various techniques for species distribution modelling. The choice of modelling technique is affected by the availability of data and in turn affects modelling outcomes. The accuracy of SDMs can be measured with respect to two characteristics: discrimination capacity and reliability; generally, discrimination capacity has been seen as a more crucial metric of model performance. Accuracy is an important challenge faced by SDMs. Several factors affect the accuracy of SDMs such as environmental data, species data, the ecology of the species, available computational resources, the model being utilized, and spatial resolution.
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References
Aguirre-Gutiérrez J, Carvalheiro LG, Polce C, van Loon EE, Raes N, Reemer M, Biesmeijer JC (2013) Fit-for-purpose: species distribution model performance depends on evaluation criteria–Dutch hoverflies as a case study. PLoS One 8(5):e63708
Alexandrino J, Teixeira J, Arntzen JW, Ferrand N (2007) Historical biogeography and conservation of the golden-striped salamander (Chioglossa lusitanica) in northwestern Iberia: integrating ecological phenotypic and phylogeographic data. In: Phylogeography of southern European refugia. Springer, Dordrecht, pp 189–205. https://doi.org/10.1007/1-4020-4904-8_6
Allouche O, Steinitz O, Rotem D, Rosenfeld A, Kadmon R (2008) Incorporating distance constraints into species distribution models. J Appl Ecol 45(2):599–609. https://doi.org/10.1111/j.1365-2664.2007.01445.x
Anselin L, Bongiovanni R, Lowenberg-DeBoer J (2004) A spatial econometric approach to the economics of site-specific nitrogen management in corn production. Am J Agric Econ 86(3):675–687. https://doi.org/10.1111/j.0002-9092.2004.00610.x
Araujo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33(10):1677–1688. https://doi.org/10.1111/j.1365-2699.2006.01584.x
Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22(1):42–47. https://doi.org/10.1016/j.tree.2006.09.010
Ash A, Shwartz M (1999) R2: a useful measure of model performance when predicting a dichotomous outcome. Stat Med 18(4):375–384. https://doi.org/10.1002/(SICI)1097-0258(19990228)18:4<375::AID-SIM20>3.0.CO;2-J
Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157(2–3):101–118. https://doi.org/10.1016/S0304-3800(02)00205-3
Beale CM, Lennon JJ, Gimona A (2008) Opening the climate envelope reveals no macroscale associations with climate in European birds. Proc Natl Acad Sci 105(39):14908–14912
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1–3
Carnaval AC, Moritz C (2008) Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic Forest. J Biogeogr 35(7):1187–1201. https://doi.org/10.1111/j.1365-2699.2007.01870.x
Caruana R, Niculescu-Mizil A (2004) Data mining in metric space: an empirical analysis of supervised learning performance criteria. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 69–78. https://doi.org/10.1145/1014052.1014063
Cerasoli F, Iannella M, D’Alessandro P, Biondi M (2017) Comparing pseudo-absences generation techniques in Boosted Regression Trees models for conservation purposes: a case study on amphibians in a protected area. PLoS One 12(11):e0187589. https://doi.org/10.1371/journal.pone.0187589
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46. https://doi.org/10.1177/001316446002000104
Daskalaki S, Kopanas I, Avouris N (2006) Evaluation of classifiers for an uneven class distribution problem. Appl Artif Intell 20(5):381–417. https://doi.org/10.1080/08839510500313653
Dormann CF, Schymanski SJ, Cabral J, Chuine I, Graham C et al (2012) Correlation and process in species distribution models: bridging a dichotomy. J Biogeogr 39(12):2119–2131. https://doi.org/10.1111/j.1365-2699.2011.02659.x
Dormann CF, Calabrese JM, Guillera-Arroita G, Matechou E, Bahn V et al (2018) Model averaging in ecology: a review of Bayesian information-theoretic and tactical approaches for predictive inference. Ecol Monogr 88(4):485–504. https://doi.org/10.1002/ecm.1309
Dorph A, Swan M, Di Stefano J, Penman TD (2021) Relating mammal species richness to landscape patterns across multiple spatial scales. Landsc Ecol 36(4):1003–1022. https://doi.org/10.1007/s10980-021-01208-8
Farashi A, Alizadeh-Noughani M (2021) Predicting the invasion risk of non-native reptiles as pets in the Middle East. Glob Ecol Conserv 31:e01818. https://doi.org/10.1016/j.gecco.2021.e01818
Farashi A, Erfani M (2018) Modeling of habitat suitability of Asiatic black bear (Ursus thibetanus gedrosianus): in Iran in future. Acta Ecol Sin 38(1):9–14. https://doi.org/10.1016/j.chnaes.2017.07.003
Farashi A, Karimian Z (2021) Assessing climate change risks to the geographical distribution of grass species. Plant Signal Behav 16(7):1913311. https://doi.org/10.1080/15592324.2021.1913311
Farashi A, Shariati M (2017) Biodiversity hotspots and conservation gaps in Iran. J Nat Conserv 39:37–57. https://doi.org/10.1016/j.jnc.2017.06.003
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49. https://doi.org/10.1017/S0376892997000088
Finley JP (1884) Tornado predictions. Am Meteorol J 1(3):85
Finn JT (1993) Use of the average mutual information index in evaluating classification error and consistency. Int J Geogr Inf Sci 7(4):349–366. https://doi.org/10.1080/02693799308901966
Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67. https://doi.org/10.1214/aos/1176347963
Gaston KJ (2009) Geographic range limits: achieving synthesis. Proc R Soc B Biol Sci 276(1661):1395–1406. https://doi.org/10.1098/rspb.2008.1480
Gavin DG, Fitzpatrick MC, Gugger PF, Heath KD, Rodríguez-Sánchez F, Dobrowski SZ et al (2014) Climate refugia: joint inference from fossil records species distribution models and phylogeography. New Phytol 204(1):37–54. https://doi.org/10.1111/nph.12929
Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM (2003) The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 56(11):1129–1135. https://doi.org/10.1016/S0895-4356(03)00177-X
Glass GV (1966) Note on rank biserial correlation. Educ Psychol Meas 26(3):623–631
Graham CH, Hijmans RJ (2006) A comparison of methods for mapping species ranges and species richness. Glob Ecol Biogeogr 15(6):578–587. https://doi.org/10.1111/j.1466-8238.2006.00257.x
Gregory AW, Smith GW, Yetman J (2001) Testing for forecast consensus. J Bus Econ Stat 19(1):34–43. https://doi.org/10.1198/07350010152472599
Grinnell J (1904) The origin and distribution of the chest-nut-backed chickadee. Auk 21(3):364–382
Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H, Lentini PE et al (2015) Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr 24(3):276–292. https://doi.org/10.1111/geb.12268
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135(2–3):147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Guisan A, Theurillat JP, Kienast F (1998) Predicting the potential distribution of plant species in an alpine environment. J Veg Sci 9(1):65–74. https://doi.org/10.2307/3237224
Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157(2–3):89–100. https://doi.org/10.1016/S0304-3800(02)00204-1
Guo C, Lek S, Ye S, Li W, Liu J, Li Z (2015) Uncertainty in ensemble modelling of large-scale species distribution: effects from species characteristics and model techniques. Ecol Model 306:67–75. https://doi.org/10.1016/j.ecolmodel.2014.08.002
Hand DJ (2001) Measuring diagnostic accuracy of statistical prediction rules. Statistica Neerlandica 55(1):3–16. https://doi.org/10.1111/1467-9574.00153
Hao T, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G (2020) Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43(4):549–558. https://doi.org/10.1111/ecog.04890
Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89(428):1255–1270. https://doi.org/10.1080/01621459.1994.10476866
He Y, Escobar M (2008) Nonparametric statistical inference method for partial areas under receiver operating characteristic curves with application to genomic studies. Stat Med 27(25):5291–5308. https://doi.org/10.1002/sim.3335
Heikkinen RK, Luoto M, Virkkala R, Pearson RG, Körber JH (2007) Biotic interactions improve prediction of boreal bird distributions at macro-scales. Glob Ecol Biogeogr 16(6):754–763. https://doi.org/10.1111/j.1466-8238.2007.00345.x
Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5):773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x
Hirzel AH, Le Lay G (2008) Habitat suitability modelling and niche theory. J Appl Ecol 45(5):1372–1381. https://doi.org/10.1111/j.1365-2664.2008.01524.x
Hosseini M, Farashi A, Khani A, Farhadinia MS (2019) Landscape connectivity for mammalian megafauna along the Iran-Turkmenistan-Afghanistan borderland. J Nat Conserv 52:125735. https://doi.org/10.1016/j.jnc.2019.125735
Huettmann F, Diamond AW (2006) Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic. Landsc Ecol 21(7):1089–1108. https://doi.org/10.1007/s10980-006-7246-8
Hutchinson GE (1957) Concluding remarks. In: Cold spring harbor symposia on quantitative biology, vol 22, pp 415–427
Jiang Y, Metz CE, Nishikawa RM (1996) A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201(3):745–750. https://doi.org/10.1148/radiology.201.3.8939225
Jiménez-Valverde A, Lobo JM, Hortal J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14(6):885–890. https://doi.org/10.1111/j.1472-4642.2008.00496.x
Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between ensemble and MaxEnt species distribution modelling approaches for conservation: a case study with Egyptian medicinal plants. Eco Inform 60:101150. https://doi.org/10.1016/j.ecoinf.2020.101150
Kamino LH, Stehmann JR, Amaral S, De Marco P Jr, Rangel TF, de Siqueira MF et al (2012) Challenges and perspectives for species distribution modelling in the neotropics. Biol Lett 8:324–326. https://doi.org/10.1098/rsbl.2011.0942
Kearney M (2006) Habitat environment and niche: what are we modelling? Oikos 115(1):186–191. https://doi.org/10.1111/j.2006.0030-1299.14908.x
Kindt R (2018) Ensemble species distribution modelling with transformed suitability values. Environ Modell Softw 100:136–145. https://doi.org/10.1016/j.envsoft.2017.11.009
Kraemer HC (2006) Correlation coefficients in medical research: from product moment correlation to the odds ratio. Stat Methods Med Res 15(6):525–545. https://doi.org/10.1177/0962280206070650
Lausch A, Blaschke T, Haase D, Herzog F, Syrbe RU, Tischendorf L, Walz U (2015) Understanding and quantifying landscape structure–a review on relevant process characteristics data models and landscape metrics. Ecol Model 295:31–41. https://doi.org/10.1016/j.ecolmodel.2014.08.018
Lek S, Guégan JF (1999) Artificial neural networks as a tool in ecological modelling an introduction. Ecol Model 120(2–3):65–73. https://doi.org/10.1016/S0304-3800(99)00092-7
Lennon JJ (2000) Red-shifts and red herrings in geographical ecology. Ecography 23(1):101–113. https://doi.org/10.1111/j.1600-0587.2000.tb00265.x
Liu C, White M, Newell G (2011) Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography 34(2):232–243. https://doi.org/10.1111/j.1600-0587.2010.06354.x
Lobo JM, Tognelli MF (2011) Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data. J Nat Conserv 19(1):1–7. https://doi.org/10.1016/j.jnc.2010.03.002
Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17(2):145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
Makori DM, Abdel-Rahman EM, Ndungu N, Odindi J, Mutanga O, Landmann T et al (2022) The use of multisource spatial data for determining the proliferation of stingless bees in Kenya. GISci Remote Sens 59(1):648–669. https://doi.org/10.1080/15481603.2022.2049536
Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (ROC): and relative operating levels (ROL): curves: statistical significance and interpretation. Quart J R Meteorol Soc 128(584):2145–2166. https://doi.org/10.1256/003590002320603584
McClish DK (1989) Analyzing a portion of the ROC curve. Med Decis Mak 9(3):190–195. https://doi.org/10.1177/0272989X8900900307
McDonald HG, Bryson RA (2010) Modeling Pleistocene local climatic parameters using macrophysical climate modeling and the paleoecology of Pleistocene megafauna. Quat Int 217(1–2):131–137. https://doi.org/10.1016/j.quaint.2009.10.010
McGuire JL, Davis EB (2013) Using the palaeontological record of Microtus to test species distribution models and reveal responses to climate change. J Biogeogr 40(8):1490–1500. https://doi.org/10.1111/jbi.12106
Meseguer AS, Lobo JM, Cornuault J, Beerling D, Ruhfel BR, Davis CC et al (2018) Reconstructing deep-time palaeoclimate legacies in the clusioid Malpighiales unveils their role in the evolution and extinction of the boreotropical flora. Glob Ecol Biogeogr 27(5):616–628. https://doi.org/10.1111/geb.12724
Miller J (2010) Species distribution modeling. Geography. Compass 4(6):490–509. https://doi.org/10.1111/j.1749-8198.2010.00351.x
Miller J, Franklin J, Aspinall R (2007) Incorporating spatial dependence in predictive vegetation models. Ecol Model 202(3–4):225–242. https://doi.org/10.1016/j.ecolmodel.2006.12.012
Mittlböck M, Schemper M (1996) Explained variation for logistic regression. Stat Med 15(19):1987–1997. https://doi.org/10.1002/(SICI)1097-0258(19961015)15:19<1987::AID-SIM318>3.0.CO;2-9
Moghadam ZR, Farashi A, Rashki A (2021) Development of a framework to predict the effects of climate change on birds. Ecol Complex 47:100952. https://doi.org/10.1016/j.ecocom.2021.100952
Murphy AH (1996) The Finley affair: a signal event in the history of forecast verification. Weather Forecast 11(1):3–20. https://doi.org/10.1175/1520-0434(1996)011<0003:TFAASE>2.0.CO;2
Parolo G, Rossi G, Ferrarini A (2008) Toward improved species niche modelling: Arnica montana in the Alps as a case study. J Appl Ecol 45(5):1410–1418. https://doi.org/10.1111/j.1365-2664.2008.01516.x
Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225–245. https://doi.org/10.1016/S0304-3800(00)00322-7
Peirce CS (1884) The numerical measure of the success of predictions. Science 93:453–454. https://doi.org/10.1126/science.ns-4.93.453.b
Peterson AT (2003) Predicting the geography of species’ invasions via ecological niche modeling. Q Rev Biol 78(4):419–433. https://doi.org/10.1086/378926
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3–4):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB et al (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol Evol 5(5):397–406. https://doi.org/10.1111/2041-210X.12180
Raney PA, Leopold DJ (2018) Fantastic wetlands and where to find them: modeling rich fen distribution in New York state with Maxent. Wetlands 38(1):81–93. https://doi.org/10.1007/s13157-017-0958-5
Rangel TFL, Diniz-Filho JAF, Bini LM (2006) Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Glob Ecol Biogeogr 15(4):321–327
Rocchini D, Hortal J, Lengyel S, Lobo JM, Jimenez-Valverde A, Ricotta C et al (2011) Accounting for uncertainty when mapping species distributions: the need for maps of ignorance. Prog Phys Geogr 35(2):211–226. https://doi.org/10.1177/0309133311399491
Rodríguez-Rey M, Jiménez-Valverde A, Acevedo P (2013) Species distribution models predict range expansion better than chance but not better than a simple dispersal model. Ecol Model 256:1–5. https://doi.org/10.1016/j.ecolmodel.2013.01.024
Saupe EE, Barve V, Myers CE, Soberón J, Barve N, Hensz CM et al (2012) Variation in niche and distribution model performance: the need for a priori assessment of key causal factors. Ecol Model 237:11–22. https://doi.org/10.1016/j.ecolmodel.2012.04.001
Schemper M (2003) Predictive accuracy and explained variation. Stat Med 22(14):2299–2308. https://doi.org/10.1002/sim.1486
Schmickl R, Jørgensen MH, Brysting AK, Koch MA (2010) The evolutionary history of the Arabidopsis lyrata complex: a hybrid in the amphi-Beringian area closes a large distribution gap and builds up a genetic barrier. BMC Evol Biol 10(1):1–18. https://doi.org/10.1186/1471-2148-10-98
Segurado P, Araujo MB, Kunin WE (2006) Consequences of spatial autocorrelation for niche-based models. J Appl Ecol 43(3):433–444. https://doi.org/10.1111/j.1365-2664.2006.01162.x
Seni G, Elder JF (2010) Ensemble methods in data mining: improving accuracy through combining predictions. In: Synthesis lectures on data mining and knowledge discovery, vol 2, no, 1, pp 1–126. https://doi.org/10.2200/S00240ED1V01Y200912DMK002
Sobek-Swant S, Kluza DA, Cuddington K, Lyons DB (2012) Potential distribution of emerald ash borer: what can we learn from ecological niche models using Maxent and GARP? For Ecol Manag 281:23–31. https://doi.org/10.1016/j.foreco.2012.06.017
Soberón J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10(12):1115–1123
Soilhi Z, Sayari N, Benalouache N, Mekki M (2022) Predicting current and future distributions of Mentha pulegium L. in Tunisia under climate change conditions using the MaxEnt model. Eco Inform 68:101533. https://doi.org/10.1016/j.ecoinf.2021.101533
Srivastava V, Lafond V, Griess VC (2019) Species distribution models (SDM): applications benefits and challenges in invasive species management. CAB Rev 14:1–13. https://doi.org/10.1079/PAVSNNR201914020
Stephenson DB (2000) Use of the “odds ratio” for diagnosing forecast skill. Weather Forecast 15(2):221–232. https://doi.org/10.1175/1520-0434(2000)015<0221:UOTORF>2.0.CO;2
Stephenson SL, Schnittler M, Novozhilov YK (2008) Myxomycete diversity and distribution from the fossil record to the present. Biodivers Conserv 17:285–301
Stockwell DRB, Peters D (1999) The GARP Modeling System: problems and solutions to automated spatial prediction. Int J Geograph Inform Sci 13:143–158. https://doi.org/10.1080/136588199241391
Stockwell DR, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148(1):1–13. https://doi.org/10.1016/S0304-3800(01)00388-X
Svenning JC, Fløjgaard C, Marske KA, Nógues-Bravo D, Normand S (2011) Applications of species distribution modeling to paleobiology. Quat Sci Rev 30(21–22):2930–2947
Tate RF (1954) Correlation between a discrete and a continuous variable. Point-biserial correlation. Ann Math Stat 25(3):603–607
Townsend Peterson A, Papeş M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30(4):550–560. https://doi.org/10.1111/j.0906-7590.2007.05102.x
Varela S, Lobo JM, Hortal J (2011) Using species distribution models in paleobiogeography: a matter of data predictors and concepts. Palaeogeogr Palaeoclimatol Palaeoecol 310(3–4):451–463. https://doi.org/10.1016/j.palaeo.2011.07.021
Vaughan IP, Ormerod SJ (2003) Improving the quality of distribution models for conservation by addressing shortcomings in the field collection of training data. Conserv Biol 17(6):1601–1611. https://doi.org/10.1111/j.1523-1739.2003.00359.x
Vayssières MP, Plant RE, Allen-Diaz BH (2000) Classification trees: an alternative non-parametric approach for predicting species distributions. J Veg Sci 11(5):679–694. https://doi.org/10.2307/3236575
Wei B, Wang R, Hou K, Wang X, Wu W (2018) Predicting the current and future cultivation regions of Carthamus tinctorius L. using MaxEnt model under climate change in China. Glob Ecol Conserv 16:e00477. https://doi.org/10.1016/j.gecco.2018.e00477
Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF et al (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88(1):15–30. https://doi.org/10.1111/j.1469-185X.2012.00235.x
Zurell D, Franklin J, König C, Bouchet PJ, Dormann CF, Elith J et al (2020) A standard protocol for reporting species distribution models. Dent Echo 43(9):1261–1277. https://doi.org/10.1111/ecog.04960
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Farashi, A., Alizadeh-Noughani, M. (2023). Basic Introduction to Species Distribution Modelling. In: Dhyani, S., Adhikari, D., Dasgupta, R., Kadaverugu, R. (eds) Ecosystem and Species Habitat Modeling for Conservation and Restoration. Springer, Singapore. https://doi.org/10.1007/978-981-99-0131-9_2
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