Abstract
Effective conservation of rare species requires reasonable knowledge of population locations. However, surveys for rare species can be time-intensive and therefore expensive. We test a methodology using stacked species distribution models (S-SDMs) to efficiently discover the greatest number of new rare species’ occurrences possible. We used S-SDMs for 22 rare plant species in southern Ontario, Canada to predict the best survey locations among individual 1-ha cells. For each cell, we weighted distribution model outputs by accuracy and species rarity to create an efficiency value. We used these efficiency values as an index to determine the locations of our field surveys. We conducted field surveys in multi-species cells, “MSC” (areas with high predicted efficiency for multiple species) and single species cells, “SSC” (areas with high probability for only one species) to determine the relative efficiency of a multi-species survey approach. MSC were more than twice as likely as SSC to have at least one rare plant species discovered. Efficiency ranks were also useful in directing surveyors toward incidental discoveries of other rare species that were not modeled. Our technique of using S-SDMs can help direct surveys to more efficiently find rare species occurrences.
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References
Albuquerque F, Beier P (2016) Predicted rarity-weighted richness, a new tool to prioritize sites for species representation. Ecol Evol 6:8107–8114
Amaral AG, Munhoz CB, Walter BM, Aguirre-Gutiérrez J, Raes N (2017) Richness pattern and phytogeography of the Cerrado's herb-shrub flora and implications for conservation. J Veg Sci 28:848–858
Bennett JR (2014) Comparison of native and exotic distribution and richness models across scales reveals essential conservation lessons. Ecography 37:120–129
Bucklin DN, Basille M, Benscoter AM, Brandt LA, Mazzotti FJ, Romanach SS, Speroterra C, Watling JI (2015) Comparing species distribution models constructed with different subsets of environmental predictors. Divers Distrib 21:23–35
Bueno de Mesquita CP, King AJ, Schmidt SK, Farrer EC, Suding KN (2016) Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important? Ecography 39(10):970–980
Burnham KP, Anderson DR (2002) Model selection and multi-model inference: a practical information-theoretic approach, 2nd edn. Springer-Verlag, New York, USA
Calabrese JM, Certain G, Kraan C, Dormann CF (2014) Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob Ecol Biogeogr 23:99–112
Cord AF, Klein D, Mora F, Dech S (2014) Comparing the suitability of classified land cover data and remote sensing variables for modeling distribution patterns of plants. Ecol Model 272:129–140
Crins WJ, Gray PA, Uhlig PWC, Wester MC (2009) The ecosystems of Ontario, part I: ecozones and ecoregions. Ontario Ministry of Natural Resources, Peterborough, ON
D’Amen M, Dubuis A, Fernandes RF, Pottier J, Pellissier L, Guisan A (2015) Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models. J Biogeogr 42:1255–1266
Del Toro I, Ribbons RR, Hayward J, Andersen AN (2019) Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient? Austral Ecol 44(1):105–113
Dunn JC, Buchanan GM, Stein RW, Whittingham MJ, McGowan PJ (2016) Optimising different types of biodiversity coverage of protected areas with a case study using Himalayan Galliformes. Biol Conserv 196:22–30
Dubuis A, Pottier J, Rion V, Pellissier L, Theurillat JP, Guisan A (2011) Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches. Divers Distrib 17:1122–1131
Elith J, Burgman MA (2002) Predictions and their validation: rare plants in the Central Highlands, Victoria, Australia. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, pp 303–313
Elith J, Graham CH (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32:66–77
Elith J et al (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129–151
Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342
Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697
Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41:263–274
ESA (Endangered Species Act) (1973) Endangered Species Act of 1973, enacted through the 93rd United States Congress. U.S. Department of the Interior
Eskildsen A, le Roux PC, Heikkinen RK, Høye TT, Kissling WD, Pöyry J, Wisz MS, Luoto M (2013) Testing species distribution models across space and time: high latitude butterflies and recent warming. Glob Ecol Biogeogr 22:1293–1303
Faber-Langendoen D, Nichols J, Master L, Snow K, Tomaino A, Bittman R, Hammerson G, Heidel B, Ramsay L, Teucher A, Young B (2012) NatureServe Conservation Status Assessments: methodology for assigning ranks. NatureServe, Arlington, VA
Fernandes RF, Scherrer D, Guisan A (2018) How much should one sample to accurately predict the distribution of species assemblages? A virtual community approach. Ecol Inform 48:125–134
Ferrier S, Guisan A (2006) Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49
Giannini TC, Chapman DS, Saraiva AM, Alves-dos-Santos I, Biesmeijer JC (2013) Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants. Ecography 36(6):649–656
Gogol-Prokurat M (2011) Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol Appl 21:33–47
Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503
Grenouillet G, Buisson L, Casajus N, Lek S (2011) Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34:9–17
Guisan A, Rahbek C (2011) SESAM—a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J Biogeogr 38:1433–1444
Guisan A et al (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435
Guisan A, Theurillat JP (2000) Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia 30:353–384
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186
Guisan A, Zimmermann N, Elith J, Graham C, Phillips S, Peterson A (2007) What matters for predicting spatial distributions of tree occurrences: techniques, data, or species' characteristics. Ecol Monogr 77:615–630
Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modelling methods. Ecography 29:773–785
Hijmans RJ, van Etten J (2017). raster: geographic analysis and modeling with raster data. https://CRAN.R-project.org/package=raster
Krause CM, Cobb NS, Pennington DD (2015) Range shifts under future scenarios of climate change: dispersal ability matters for Colorado Plateau endemic plants. Nat Areas J 35(3):428–438
Koch R, Almeida-Cortez JS, Kleinschmit B (2017) Revealing areas of high nature conservation importance in a seasonally dry tropical forest in Brazil: combination of modelled plant diversity hot spots and threat patterns. J Nat Conserv 35:24–39
Le Lay G, Engler R, Franc E, Guisan A (2010) Prospective sampling based on model ensembles improves the detection of rare species. Ecography 33:1015–1027
Lindenmayer DB, Piggott MP, Wintle BA (2013) Counting the books while the library burns: why conservation monitoring programs need a plan for action. Front Ecol Environ 11:549–555
Liu C, Newell G, White M (2016) On the selection of thresholds for predicting species occurrence with presence-only data. Ecol Evol 6:337–348
Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600
Luoto M, Pöyry J, Heikkinen RK, Saarinen K (2005) Uncertainty of bioclimate envelope models based on the geographical distribution of species. Glob Ecol Biogeogr 14:575–584
Luoto M, Virkkala R, Heikkinen RK (2007) The role of land cover in bioclimatic models depends on spatial resolution. Glob Ecol Biogeogr 16:34–42
MacDougall A, Loo J (2002) Land use history, plant rarity, and protected area adequacy in an intensively managed forest landscape. J Nat Conserv 10:171–183
McCune JL (2016) Species distribution models predict rare species occurrences despite significant effects of landscape context. J Appl Ecol 53:1871–1879
McCune JL, Van Natto A, MacDougall AS (2017) The efficacy of protected areas and private land for plant conservation in a fragmented landscape. Landsc Ecol 32:871–882
McKenney DW, Pedlar JH, Lawrence K, Papadopol P, Campbell K (2015) Hardiness zones and bioclimatic modelling of plant species distributions in North America. Acta Hortic 1085:139–148
Meier ES, Kienast F, Pearman PB, Svenning JC, Thuiller W, Araújo MB, Guisan A, Zimmermann NE (2010) Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecography 33(6):1038–1048
Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069
Miličić M, Vujić A, Jurca T, Cardoso P (2017) Designating conservation priorities for Southeast European hoverflies (Diptera: Syrphidae) based on species distribution models and species vulnerability. Insect Conserv Divers 10:354–366
Moudrý V, Šímová P (2012) Influence of positional accuracy, sample size and scale on modelling species distributions: a review. Int J Geogr Inf Sci 26:2083–2095
Newbold T, Gilbert F, Zalat S, El-Gabbas A, Reader T (2009) Climate-based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna. J Biogeogr 36:2085–2095
Oldham MJ, Brinker SR (2009) Rare vascular plants of Ontario, 4th edn. Natural Heritage Information Centre, Ontario Ministry of Natural Resources, Peterborough, ON
Oldham MJ (2017) List of the Vascular Plants of Ontario’s Carolinian Zone (Ecoregion 7E). Carolinian Canada and Ontario Ministry of Natural Resources and Forestry, Peterborough, ON
Parviainen M, Marmion M, Luoto M, Thuiller W, Heikkinen RK (2009) Using summed individual species models and state-of-the-art modelling techniques to identify threatened plant species hotspots. Biol Conserv 142:2501–2509
Pearce J, Ferrier S (2000) Evaluating the predictive performanceof habitat models developed using logistic regression. Ecol Model 133:225–245
Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102–117
Pellissier L, Anne Bråthen K, Pottier J, Randin CF, Vittoz P, Dubuis A, Yoccoz NG, Alm T, Zimmermann NE, Guisan A (2010) Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants. Ecography 33(6):1004–1014
Peterson AT, Soberon J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araujo MB (2011) Ecological niches and geographic distributions. Princeton University Press, Princeton, NJ
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259
Pouteau R, Bayle É, Blanchard É, Birnbaum P, Cassan JJ, Hequet V, Ibanez T, Vandrot H (2015) Accounting for the indirect area effect in stacked species distribution models to map species richness in a montane biodiversity hotspot. Divers Distrib 21:1329–1338
Primack RB, Miao SL (1992) Dispersal can limit local plant distribution. Conserv Biol 6(4):513–519
R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Raes N, ter Steege H (2007) A null-model for significance testing of presence-only species distribution models. Ecography 30:727–736
Rebelo H, Jones G (2010) Ground validation of presence-only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae). J Appl Ecol 47:410–420
Rhoden CM, Peterman WE, Taylor CA (2017) Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ 5:e3632. https://doi.org/10.7717/peerj.3632
SARA (Species at Risk Act) (2002) Bill C-5, an Act Respecting the Protection of Wildlife Species at Risk in Canada. Government of Canada, Ottawa, Ontario
Segurado P, Araujo MB (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31:1555–1568
Soultan A, Safi K (2017) The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialisation. PLoS ONE 12:e0187906. https://doi.org/10.1371/journal.pone.0187906
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
Syphard AD, Franklin J (2010) Species traits affect the performance of species distribution models for plants in southern California. J Veg Sci 21:177–189
Thuiller W (2004) Patterns and uncertainties of species' range shifts under climate change. Glob Change Biol 10:2020–2027
Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005) Climate change threats to plant diversity in Europe. Proc Natl Acad Sci USA 102:8245–8250
Tukiainen H, Bailey JJ, Field R, Kangas K, Hjort J (2017) Combining geodiversity with climate and topography to account for threatened species richness. Conserv Biol 31:364–375
van Proosdij AS, Sosef MS, Wieringa JJ, Raes N (2016) Minimum required number of specimen records to develop accurate species distribution models. Ecography 39:542–552
Vaughan IP, Ormerod SJ (2005) The continuing challenges of testing species distribution models. J Appl Ecol 42:720–730
Williams JN, Seo C, Thorne J, Nelson JK, Erwin S, O’Brien JM, Schwartz MW (2009) Using species distribution models to predict new occurrences for rare plants. Divers Distrib 15:565–576
Wilson JW, Sexton JO, Jobe RT, Haddad NM (2013) The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models. Biol Conserv 164:170–176
Yu F, Skidmore AK, Wang T, Huang J, Ma K, Groen TA (2017) Rhododendron diversity patterns and priority conservation areas in China. Divers Distrib 23:1143–1156
Acknowledgements
We thank J. Lloren, J. Pon, and C. Raymond for field work assistance. M. Oldham, T. Smith, and E. Snyder provided plant identification advice. The Natural Heritage Information Centre of Ontario provided the occurrence records for all species. We also thank the private landowners who allowed us access to their woodlots, as well as to The Nature Conservancy of Canada, Ontario Nature, the Province of Ontario, and the University of Waterloo, for granting permits to access protected areas. This research was funded by the Ontario Ministry of Natural Resources and Forestry’s Species at Risk Stewardship Fund, the Natural Science and Engineering Research Council of Canada (NSERC) through a Postdoctoral Fellowship to JLM and a Discovery Grant to JRB, and a Liber Ero fellowship to JLM.
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Rosner-Katz, H., McCune, J.L. & Bennett, J.R. Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species. Biodivers Conserv 29, 3209–3225 (2020). https://doi.org/10.1007/s10531-020-02018-1
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DOI: https://doi.org/10.1007/s10531-020-02018-1