Biodiversity and Conservation

, Volume 26, Issue 2, pp 251–271 | Cite as

Integrating species distribution modelling into decision-making to inform conservation actions

  • Dani Villero
  • Magda Pla
  • David Camps
  • Jordi Ruiz-Olmo
  • Lluís Brotons
Review Paper
Part of the following topical collections:
  1. Biodiversity protection and reserves

Abstract

Species distribution models (SDMs) have been widely tagged as valuable tools in a variety of conservation assessments to address pressing conservation problems. However, these solutions could be hampered by difficulties to overcome the knowledge-action boundary between conservation and modelling practice. These difficulties have been well typified in the ecological modelling sphere, but a specific conceptual framework on how to bridge this gap is still lacking. This work reports successful examples on how to use SDMs to identify the most favourable habitats for implementing conservation management actions. We use these examples to discuss about the three main topics that deserve special attention to help enhance information flow between practitioners and modellers: the decision context, the modelling framework and the spatial products. Finally, we suggest some practical solutions to improve applications of effective conservation action on the ground. We emphasize the importance of matching modelling goals and decision targets by a close collaboration of modellers with decision makers and species experts. Moreover, we highlight the key role of clear and useful spatial products to provide relevant and timely feedback to increase understanding and promote utilisation by conservation practitioners, and to inform and involve targeted audiences.

Keywords

Conservation management Environmental payments Habitat corridor Implementation gap Knowledge-action boundary Risk assessments Species distribution models 

References

  1. Addison PFE, Rumpff L, Bau SS et al (2013) Practical solutions for making models indispensable in conservation decision-making. Divers Distrib 19:490–502. doi:10.1111/ddi.12054 CrossRefGoogle Scholar
  2. Anderson RP, Gonzalez I Jr (2011) Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol Model 222:2796–2811CrossRefGoogle Scholar
  3. Anderson RP, Martinez-Meyer E (2004) Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biol Conserv 116:167–179CrossRefGoogle Scholar
  4. Angelieri CCS, Adams-Hosking C, de Barroz KMPM et al (2016) Using species distribution models to predict potential landscape restoration effects on puma conservation. PLoS ONE 11:1–18. doi:10.1371/journal.pone.0145232 CrossRefGoogle Scholar
  5. Arcos JM, Bécares J, Rodríguez B, Ruiz A (2009) Áreas Importantes para la conservación de las aves marinas en España. LIFE04NAT/ES/000049-Sociedad Española de Ornitologia (SEO/BirdLife), MadridGoogle Scholar
  6. Arcos JM, Bécares J, Villero D et al (2012) Assessing the location and stability of foraging hotspots for pelagic seabirds: an approach to identify marine Important Bird Areas (IBAs) in Spain. Biol Conserv. doi:10.1016/j.biocon.2011.12.011 Google Scholar
  7. Arts K, van der Wal R, Adams WM (2015) Digital technology and the conservation of nature. Ambio 44:661–673. doi:10.1007/s13280-015-0705-1 PubMedPubMedCentralCrossRefGoogle Scholar
  8. Augustin NH, Mugglestone MA, Buckland ST (1996) An autologistic model for the spatial distribution of wildlife. J Appl Ecol 33:339. doi:10.2307/2404755 CrossRefGoogle Scholar
  9. Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423. doi:10.1111/j.1365-2664.2006.01136.x CrossRefGoogle Scholar
  10. Beale CM, Lennon JJ (2012) Incorporating uncertainty in predictive species distribution modelling. Philos Trans R Soc B Biol Sci 367:247–258. doi:10.1098/rstb.2011.0178 CrossRefGoogle Scholar
  11. Bertolero A (2008) Cens i distribució de la tortuga mediterrània a la Serra de l’Albera. Avaluació de la situació durant el 2007. BarcelonaGoogle Scholar
  12. Bota G, Brotons L, Giralt D, Pla M (2008) Informe científico sobre la identificación de zonas de hàbitat adecuado para la carraca, la terrera común, la calandria común y el sisón en el ámbito de las IBAs 142 (Secans de Lleida) y 144 (Cogul-Alfés). Informe inèdit, Centre Tecnológic Forestal de CatalunyaGoogle Scholar
  13. Boyce M, Vernier P, Nielsen S, Schmiegelow F (2002) Evaluating resource selection functions. Ecol Modell 157:281–300. doi:10.1016/S0304-3800(02)00200-4 CrossRefGoogle Scholar
  14. Brotons L (2014) Species distribution models and impact factor growth in environmental journals: methodological fashion or the attraction of global change science. PLoS ONE 9:e111996. doi:10.1371/journal.pone.0111996 PubMedPubMedCentralCrossRefGoogle Scholar
  15. Brotons L, Herrando S, Pla M (2007) Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long-term monitoring programs. Divers Distrib 13:276–288. doi:10.1111/j.1472-4642.2007.00339.x CrossRefGoogle Scholar
  16. Campbell CA, Lefroy EC, Caddy-Retalic S et al (2015) Designing environmental research for impact. Sci Total Environ 534:4–13. doi:10.1016/j.scitotenv.2014.11.089 PubMedCrossRefGoogle Scholar
  17. Carwardine J, Wilson KA, Watts M et al (2008) Avoiding costly conservation mistakes: the importance of defining actions and costs in spatial priority setting. PLoS ONE 3:e2586PubMedPubMedCentralCrossRefGoogle Scholar
  18. Cash DW, Clark WC, Alcock F et al (2003) Knowledge systems for sustainable development. Proc Natl Acad Sci 100:8086–8091. doi:10.1073/pnas.1231332100 PubMedPubMedCentralCrossRefGoogle Scholar
  19. Chapman AD (2005) Principles and methods of data cleaning—primary species and species-occurrence data, version 1.0. Rep. Glob. Biodivers. Inf. Facil. 77Google Scholar
  20. Clavero M, Hermoso V (2015) Historical data to plan the recovery of the European eel. J Appl Ecol 52:960–968. doi:10.1111/1365-2664.12446 CrossRefGoogle Scholar
  21. Cook CN, Hockings M, Carter RWB (2010) Conservation in the dark ? The information used to support management decisions. Front Ecol Environ 8:181–186. doi:10.1890/090020 CrossRefGoogle Scholar
  22. Cook CN, Mascia MB, Schwartz MW et al (2013) Achieving conservation science that bridges the knowledge-action boundary. Conserv Biol 27:669–678. doi:10.1111/cobi.12050 PubMedPubMedCentralCrossRefGoogle Scholar
  23. de Siqueira MF, Durigan G, de Júnior Marco P, Peterson AT (2009) Something from nothing: using landscape similarity and ecological niche modeling to find rare plant species. J Nat Conserv 17:25–32. doi:10.1016/j.jnc.2008.11.001 CrossRefGoogle Scholar
  24. DMAH (2007) Ordre MAH/279/2007, de 24 de juliol, per la qual s’aproven les bases reguladores de les subvencions destinades a compatibilitzar les activitats apícoles amb la conservació de l’abellerol al territori de Catalunya i s’obre la convocatòria per a l’any 2007. DOGC 4938:26160–26165Google Scholar
  25. Driver A, Cowling RM, Maze K (2003) Planning for living landscapes—perspectives and lessons from South Africa. Center for Applied Biodiversity Science at Conservation International, Washington, DCGoogle Scholar
  26. Edrén SMC, Wisz MS, Teilmann J et al (2010) Modelling spatial patterns in harbour porpoise satellite telemetry data using maximum entropy. Ecography (Cop) 33:698–708. doi:10.1111/j.1600-0587.2009.05901.x CrossRefGoogle Scholar
  27. Elith J, Leathwick J (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Divers Distrib 13:265–275. doi:10.1111/j.1472-4642.2007.00340.x= CrossRefGoogle Scholar
  28. Elith J, Leathwick J (2009) Spatial conservation prioritization: quantitative methods and computational tools. In: Moilanen A, Wilson KA, Possingham H (eds) Spatial conservation prioritization: quantitative methods and computational tools. Oxford University, Oxford, pp 70–93Google Scholar
  29. Elith JH, Graham CP, Anderson R et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop) 29:129–151. doi:10.1111/j.2006.0906-7590.04596.x CrossRefGoogle Scholar
  30. Elith J, Phillips SJ, Hastie T et al (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57. doi:10.1111/j.1472-4642.2010.00725.x CrossRefGoogle Scholar
  31. Estrada J, Pedrocchi V, Brotons L, Herrando S (2004) Catalan Breeding Bird Atlas (1999–2002). Lynx ed. & Institut Català d’Ornitologia, BellaterraGoogle Scholar
  32. Fajardo J, Lessmann J, Bonaccorso E et al (2014) Combined use of systematic conservation planning, species distribution modelling, and connectivity analysis reveals severe conservation gaps in a megadiverse country (Peru). PLoS ONE 9:1–23. doi:10.1371/journal.pone.0114367 CrossRefGoogle Scholar
  33. Fei S, Liang L, Paillet FL et al (2012) Modelling chestnut biogeography for American chestnut restoration. Divers Distrib 18:754–768. doi:10.1111/j.1472-4642.2012.00886.x CrossRefGoogle Scholar
  34. Ferrier S, Watson G, Pearce J et al (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales I. Species-level modelling. Biodivers Conserv 11:2275–2307CrossRefGoogle Scholar
  35. Ficetola GF, Thuiller W, Miaud C (2007) Prediction and validation of the potential global distribution of a problematic alien invasive species—the American bullfrog. Divers Distrib 13:476–485. doi:10.1111/j.1472-4642.2007.00377.x CrossRefGoogle Scholar
  36. Ficetola GF, Maiorano L, Falcucci A et al (2010) Knowing the past to predict the future: land-use change and the distribution of invasive bullfrogs. Glob Chang Biol 16:528–537. doi:10.1111/j.1365-2486.2009.01957.x CrossRefGoogle Scholar
  37. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence: absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  38. Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9:e97122PubMedPubMedCentralCrossRefGoogle Scholar
  39. Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, New YorkGoogle Scholar
  40. Gastón A, García-Viñas JI (2013) Evaluating the predictive performance of stacked species distribution models applied to plant species selection in ecological restoration. Ecol Modell 263:103–108. doi:10.1016/j.ecolmodel.2013.04.020 CrossRefGoogle Scholar
  41. Grantham HS, Wilson KA, Moilanen A et al (2009) Delaying conservation actions for improved knowledge: how long should we wait? Ecol Lett 12:293–301. doi:10.1111/j.1461-0248.2009.01287.x PubMedCrossRefGoogle Scholar
  42. Gregory R, Long G (2009) Using structured decision making to help implement a precautionary approach to endangered species management. Risk Anal 29:518–532PubMedCrossRefGoogle Scholar
  43. Guillera-Arroita G, Lahoz-Monfort JJ, Elith J et al (2015) Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr 24:276–292. doi:10.1111/geb.12268 CrossRefGoogle Scholar
  44. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Modell 135:147–186. doi:10.1016/S0304-3800(00)00354-9 CrossRefGoogle Scholar
  45. Guisan A, Broennimann O, Engler R et al (2006) Using Niche-Based models to improve the sampling of rare species. Conserv Biol 20:501–511. doi:10.1111/j.1523-1739.2006.00354.x PubMedCrossRefGoogle Scholar
  46. Guisan A, Tingley R, Baumgartner JB et al (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435. doi:10.1111/ele.12189 PubMedPubMedCentralCrossRefGoogle Scholar
  47. Hermoso V, Kennard MJ, Linke S (2013) Data acquisition for conservation assessments: is the effort worth It? PLoS ONE 8:e59662PubMedPubMedCentralCrossRefGoogle Scholar
  48. Hermoso V, Kennard MJ, Linke S (2015) Assessing the risks and opportunities of presence-only data for conservation planning. J Biogeogr 42:218–228. doi:10.1111/jbi.12393 CrossRefGoogle Scholar
  49. Hesterberg T, Chambers JM, Hastie TJ (1993) Statistical Models in S. Chapman and Hall, LondonGoogle Scholar
  50. Hirzel A, Guisan A (2002) Which is the optimal sampling strategy for habitat suitability modelling. Ecol Modell 157:331–341. doi:10.1016/S0304-3800(02)00203-X CrossRefGoogle Scholar
  51. Jetz W, Wilcove DS, Dobson AP (2007) Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol 5:e157. doi:10.1371/journal.pbio.0050157 PubMedPubMedCentralCrossRefGoogle Scholar
  52. Jetz W, McPherson JM, Guralnick RP (2012) Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol Evol 27:151–159. doi:10.1016/j.tree.2011.09.007 PubMedCrossRefGoogle Scholar
  53. Jiménez-Valverde A, Peterson AT, Soberón J et al (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13:2785–2797. doi:10.1007/s10530-011-9963-4 CrossRefGoogle Scholar
  54. Knight AT, Cowling RM, Campbell BM (2006) An operational model for implementing conservation action. Conserv Biol 20:408–419PubMedCrossRefGoogle Scholar
  55. Knight AT, Cowling RM, Rouget M et al (2008) Knowing but not doing: selecting priority conservation areas and the research-implementation gap. Conserv Biol 22:610–617. doi:10.1111/j.1523-1739.2008.00914.x PubMedCrossRefGoogle Scholar
  56. Kramer-Schadt S, Niedballa J, Pilgrim JD et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. doi:10.1111/ddi.12096 CrossRefGoogle Scholar
  57. Laurance WF, Koster H, Grooten M et al (2012) Making conservation research more relevant for conservation practitioners. Biol Conserv 153:164–168. doi:10.1016/j.biocon.2012.05.012 CrossRefGoogle Scholar
  58. Legendre P, Legendre L (1988) Numerical Ecology, 2nd edn. Elsevier Science B.V, AmsterdamGoogle Scholar
  59. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop) 28:385–393. doi:10.1111/j.0906-7590.2005.03957.x CrossRefGoogle Scholar
  60. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151. doi:10.1111/j.1466-8238.2007.00358.x CrossRefGoogle Scholar
  61. Longepierre S, Hailey A, Grenot C (2001) Home range area in the tortoise Testudo hermanni in relation to habitat complexity: implications for conservation of biodiversity. Biodivers Conserv 10:1131–1140CrossRefGoogle Scholar
  62. Ludwig D, Mangel M, Haddad B (2001) Ecology, conservation and public policy. Annu Rev Ecol Syst 1:481–517CrossRefGoogle Scholar
  63. MacDonald DW, Collins NM, Wrangham R (2007) Principles, practice and priorities: the quest of “alignment”. In: MacDonald DW, Service K (eds) Key topics in conservation biology. Blackwell Publishing Ltd, Malden, pp 271–290Google Scholar
  64. Marcer A, Sáez L, Molowny-horas R et al (2012) Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol Conserv 166:221–230. doi:10.1016/j.biocon.2013.07.001 CrossRefGoogle Scholar
  65. Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253. doi:10.1038/35012251 PubMedCrossRefGoogle Scholar
  66. Martin Y, Van Dyck H, Dendoncker N, Titeux N (2013) Testing instead of assuming the importance of land use change scenarios to model species distributions under climate change. Glob Ecol Biogeogr 22:1204–1216. doi:10.1111/geb.12087 CrossRefGoogle Scholar
  67. Mcdonald-Madden E, Bode M, Game ET et al (2008) The need for speed: informed land acquisitions for conservation in a dynamic property market. Ecol Lett 11:1169–1177. doi:10.1111/j.1461-0248.2008.01226.x PubMedGoogle Scholar
  68. Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and setting matter. Ecography (Cop) 36:1058–1069. doi:10.1111/j.1600-0587.2013.07872.x CrossRefGoogle Scholar
  69. Norton BG (1998) Improving ecological communication: the role of ecologists in environmental policy formation. Ecol Appl 8:350–364. doi:10.1890/1051-0761 CrossRefGoogle Scholar
  70. Pawar S, Koo M, Kelley C et al (2007) Conservation assessment and prioritization of areas in Northeast India: priorities for amphibians and reptiles. Biol Conserv 136:346–361. doi:10.1016/j.biocon.2006.12.012 CrossRefGoogle Scholar
  71. Pearce JL, Boyce MS (2006) Modelling distribution and abundance with presence-only data. J Appl Ecol 43:405–412. doi:10.1111/j.1365-2664.2005.01112.x CrossRefGoogle Scholar
  72. Pearce J, Ferrier S, Scotts D (2001a) An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales. J Environ Manage 62:171–184. doi:10.1006/jema.2001.0425 PubMedCrossRefGoogle Scholar
  73. Pearce JL, Cherry KMD et al (2001b) Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution. J Appl Ecol 38:412–424. doi:10.1046/j.1365-2664.2001.00608.x CrossRefGoogle Scholar
  74. Pearson RG, Raxworthy CJ, Nakamura M et al (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117. doi:10.1111/j.1365-2699.2006.01594.x CrossRefGoogle Scholar
  75. Pfeffer J, Sutton RI (1999) Knowing “what” to do is not enough: turning knowledge into action. Calif Manage Rev 42:83–108. doi:10.2307/41166020 CrossRefGoogle Scholar
  76. Phillips SJ, Dudik M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography (Cop) 31:161–175. doi:10.1111/j.2007.0906-7590.05203.x CrossRefGoogle Scholar
  77. Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. Twenty-first Int Conf Mach Learn–ICML’04 83. doi: 10.1145/1015330.1015412
  78. Phillips S, Anderson R, Schapire R (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259. doi:10.1016/j.ecolmodel.2005.03.026 CrossRefGoogle Scholar
  79. Phillips SJ, Dudík M, Elith J et al (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197PubMedCrossRefGoogle Scholar
  80. Prendergast JR, Quinn RM, Lawton JH (1999) The gaps between theory and practice in selecting nature reserves. Conserv Biol 13:484–492. doi:10.1046/j.1523-1739.1999.97428.x CrossRefGoogle Scholar
  81. Pullin AS (2002) Conservation Biology. Cambridge University Press, New YorkCrossRefGoogle Scholar
  82. Pullin AS, Knight TM, Stone DA, Charman K (2004) Do conservation managers use scientific evidence to support their decision-making? Biol Conserv 119:245–252. doi:10.1016/j.biocon.2003.11.007 CrossRefGoogle Scholar
  83. Raxworthy CJ, Martinez-Meyer E, Horning N et al (2003) Predicting distributions of known and unknown reptile species in Madagascar. Nature 426:837–841. doi:10.1038/nature02205 PubMedCrossRefGoogle Scholar
  84. Reddy S, Dávalos LM (2003) Geographical sampling bias and its implications for conservation priorities in Africa. J Biogeogr 30:1719–1727. doi:10.1046/j.1365-2699.2003.00946.x CrossRefGoogle Scholar
  85. Regos A, D’Amen M, Titeux N et al (2016) Predicting the future effectiveness of protected areas for bird conservation in Mediterranean ecosystems under climate change and novel fire regime scenarios. Divers Distrib 22:83–96. doi:10.1111/ddi.12375 CrossRefGoogle Scholar
  86. Reyers B, Rouget M, Jonas Z et al (2007) Developing products for conservation decision-making: lessons from a spatial biodiversity assessment for South Africa. Divers Distrib 13:608–619. doi:10.1111/j.1472-4642.2007.00379.x CrossRefGoogle Scholar
  87. Robertson A, Jarvis AM (1995) Can bird atlas data be used to estimate population size? A case study using namibian endemics. Biol Conserv 71:87–95CrossRefGoogle Scholar
  88. Rodriguez JP, Brotons L, Bustamante J et al (2007) The application of predictive modelling of species distribution to biodiversity conservation. Divers Distrib 13:243–251. doi:10.1111/j.1472-4642.2007.00356.x CrossRefGoogle Scholar
  89. Rondinini C, Wilson Ka, Boitani L et al (2006) Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett 9:1136–1145. doi:10.1111/j.1461-0248.2006.00970.x PubMedCrossRefGoogle Scholar
  90. Roura-Pascual N, Brotons L, Peterson AT, Thuiller W (2007) Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula. Biol Invasions 11:1017–1031. doi:10.1007/s10530-008-9313-3 CrossRefGoogle Scholar
  91. Rousselet J, Imbert C-E, Dekri A et al (2013) Assessing species distribution using google street view: a pilot study with the pine processionary moth. PLoS ONE 8:e74918PubMedPubMedCentralCrossRefGoogle Scholar
  92. Runge CA, Tulloch AIT, Possingham HP et al (2016) Incorporating dynamic distributions into spatial prioritization. Divers Distrib 22:332–343. doi:10.1111/ddi.12395 CrossRefGoogle Scholar
  93. Rykiel EJ (1996) Testing ecological models: the meaning of validation. Ecol Modell 90:229–244. doi:10.1016/0304-3800(95)00152-2 CrossRefGoogle Scholar
  94. Salafsky N, Margoluis R, Redford K, Robinson JG (2002) Improving the practice of conservation: a conceptual framework and research agenda for conservation science. Conserv Biol 16:1469–1479CrossRefGoogle Scholar
  95. Schmolke A, Thorbek P, DeAngelis DL, Grimm V (2010) Ecological models supporting environmental decision making: a strategy for the future. Trends Ecol Evol 25:479–486. doi:10.1016/j.tree.2010.05.001 PubMedCrossRefGoogle Scholar
  96. Scholes RJ, Mace GM, Turner W et al (2008) Toward a global biodiversity observing system. Source Sci New Ser 321:1044–1045. doi:10.1126/science.1162055 Google Scholar
  97. Shanmughavel P (2007) An overview on biodiversity information in databases. Bioinformation 1:367–369. doi:10.6026/97320630001367 PubMedPubMedCentralCrossRefGoogle Scholar
  98. Soberón JM (2004) Translating life’s diversity: can scientists and policymakers learn to communicate better? Environ Sci Policy Sustain Dev 46:10–20. doi:10.1080/00139150409604394 CrossRefGoogle Scholar
  99. Starfield AM (1997) A pragmatic approach to modeling for wildlife management. J Wildl Manage 61:261–270. doi:10.2307/3802581 CrossRefGoogle Scholar
  100. Sutherland WJ, Pullin AS, Dolman PM, Knight TM (2004) The need for evidence-based conservation. Trends Ecol Evol 19:305–308. doi:10.1016/j.tree.2004.03.018 PubMedCrossRefGoogle Scholar
  101. Syfert MM, Smith MJ, Coomes DA (2013) The effects of sampling bias and model complexity on the predictive performance of Maxent species distribution models. PLoS ONE 8:e55158PubMedPubMedCentralCrossRefGoogle Scholar
  102. Theobald DM, Spies T, Kline J et al (2005) Ecological support for rural land-use planning. Ecol Appl 15:1906–1914CrossRefGoogle Scholar
  103. Thomas CD, Cameron A, Green RE et al (2004) Extinction risk from climate change. Nature 427:145–148PubMedCrossRefGoogle Scholar
  104. Vallecillo S, Brotons L, Thuiller W (2009) Dangers of predicting bird species distributions in response to land-cover changes. Ecol Appl 19:538–549PubMedCrossRefGoogle Scholar
  105. 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 17:1601–1611. doi:10.1111/j.1523-1739.2003.00359.x CrossRefGoogle Scholar
  106. Warren DL, Seifert SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342PubMedCrossRefGoogle Scholar
  107. Wetzel FT, Saarenmaa H, Regan E et al (2015) The roles and contributions of Biodiversity Observation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study. Biodiversity 16(2–3):137–149. doi:10.1080/14888386.2015.1075902 CrossRefGoogle Scholar
  108. Wieczorek J, Bloom D, Guralnick R et al (2012) Darwin core: an evolving community-developed biodiversity data standard. PLoS ONE. doi:10.1371/journal.pone.0029715 Google Scholar
  109. Wintle BA, Elith J, Potts JM (2005) Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecol 30:719–738. doi:10.1111/j.1442-9993.2005.01514.x CrossRefGoogle Scholar
  110. Wisz MS, Hijmans RJ, Li J et al (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773. doi:10.1111/j.1472-4642.2008.00482.x CrossRefGoogle Scholar
  111. Yackulic CB, Chandler R, Zipkin EF et al (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods Ecol Evol 4:236–243. doi:10.1111/2041-210x.12004 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Forest Sciences Centre of Catalonia (CTFC-InFOREST)SolsonaSpain
  2. 2.Directorate-General for Environmental PolicyMinistry of Territory and Sustainability, Government of CataloniaBarcelonaSpain
  3. 3.Directorate-General of the Natural Environment and BiodiversityMinistry of Agriculture, Livestock, Fisheries, Food and Natural Environment, Government of CataloniaBarcelonaSpain
  4. 4.Centre for Ecological Research and Forestry Applications (CREAF)Cerdanyola del VallèsSpain
  5. 5.Spanish National Research Council (CSIC)Cerdanyola del VallèsSpain

Personalised recommendations