Climate change will cause species extinctions that will be exacerbated by human-caused landscape changes, preventing species from tracking shifting climatic niches. Although incorporating functional connectivity into prospective population models has proven challenging, the field of landscape genetics provides underutilized tools for characterizing functional connectivity.
The aim of this study was to explore how genetically-derived representations of dispersal affect assessments of environmental change impacts using a spatially-explicit population modelling approach. We illustrated the utility of this approach to test hypotheses related to the effects of dispersal representation and environmental change for the IUCN-threatened Blanding’s Turtle (Emydoidea blandingii).
We integrated existing demographic and genetic datasets into a spatially-explicit metapopulation modelling framework. We ran several sets of simulations with varying dispersal representations (distance-based, landscape resistance-based with either static or changing land cover) to explore how landscape genetic estimates of connectivity impact estimates of extinction risk.
Models incorporating land cover-based dispersal resulted in lower patch occupancy than simulations where dispersal was only a function of interpatch distance. Furthermore, both climate change-induced declines in habitat suitability and land use change-induced declines in connectivity reduced abundance and patch occupancy.
Incorporating landscape genetics into population models revealed that choices involved in dispersal representation alter both extinction risk and path occupancy, often altering the distribution of extant patches by the end of simulations. As technological advances continue to increase access to landscape genetic datasets, we suggest that researchers carefully consider how genetic resources can be used to improve climate vulnerability assessments.
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Akçakaya HR, Root W (2013) RAMAS GIS: linking spatial data with population viability analysis (version 6). Applied Biomathematics, Setauket
Ash JD, Givnish TJ, Waller DM (2017) Tracking lags in historical plant species’ shifts in relation to regional climate change. Global Change Biol 23:1305–1315
Bélisle M (2005) Measuring landscape connectivity: the challenge of behavioral landscape ecology. Ecology 86:1988–1995
Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377
Brook BW (2008) Synergies between climate change, extinctions and invasive vertebrates. Wildlife Res 35:249–252
Brook BW, Akçakaya HR, Keith DA, Mace GM, Pearson RG, Araujo MB (2009) Integrating bioclimate with population models to improve forecasts of species extinctions under climate change. Biol Lett 5:723–725
Brook BW, Sodhi NS, Bradshaw CJ (2008) Synergies among extinction drivers under global change. Trends Ecol Evol 23:453–460
Burke VJ, Rathbun SL, Bodie JR, Gibbons JW (1998) Effect of density on predation rate for turtle nests in a complex landscape. Oikos 83:3–11
Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026
Congdon JD, Dunham AE, Sels RCVL (1993) Delayed sexual maturity and demographics of Blanding's turtles (Emydoidea blandingii): implications for conservation and management of long-lived organisms. Conserv Biol 7:826–833
Congdon JD, Dunham AE, Sels RCVL (1994) Demographics of common snapping turtles (Chelydra serpentina): implications for conservation and management of long-lived organisms. Am Zool 34:397–408
Conlisk E, Syphard AD, Franklin J, Flint L, Flint A, Regan H (2013) Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models. Global Change Biol 19:858–869
DeFries RS, Foley JA, Asner GP (2004) Land-use choices: balancing human needs and ecosystem function. Front Ecol Environ 2:249–257
Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57
Ernst CH, Lovich JE (2009) Turtles of the United States and Canada. JHU Press, Baltimore
Fordham DA, Akçakaya HR, Araujo MB, Keith DA, Brook BW (2013) Tools for integrating range change, extinction risk and climate change information into conservation management. Ecography 36:956–964
Freeman BG, Class Freeman AM (2014) Rapid upslope shifts in New Guinean birds illustrate strong distributional responses of tropical montane species to global warming. Proc Natl Acad Sci USA 111:4490–4494
Gibbons JW, Scott DE, Ryan TJ, Buhlmann KA, Tuberville TD, Metts BS, Greene JL, Mills T, Leiden Y, Poppy S, Winne CT (2000) The global decline of reptiles, déjà vu amphibians. Bioscience 50:653–666
Hamilton CM, Bateman BL, Gorzo JM, Reid BN, Thogmartin WE, Peery MZ, Heglund PJ, Radeloff VC, Pigeon AM (2018) Slow and steady wins the race? Future climate and land use change leaves the imperiled Blanding's turtle (Emydoidea blandingii) behind. Biol Conserv 222:75–85
Hansen GJ, Read JS, Hansen JF, Winslow LA (2017) Projected shifts in fish species dominance in Wisconsin lakes under climate change. Global Change Biol 23:1463–1476
Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W, Sykes MT (2006) Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog Phys Geog 30:751–777
Hodgson JA, Thomas CD, Wintle BA, Moilanen A (2009) Climate change, connectivity and conservation decision making: back to basics. J Appl Ecol 46:964–969
Janzen FJ (1994) Climate change and temperature-dependent sex determination in reptiles. Proc Natl Acad Sci USA 91:7487–7490
Kahilainen A, Nouhuys SV, Schulz T, Saastamoinen M (2018) Metapopulation dynamics in a changing climate: increasing spatial synchrony in weather conditions drives metapopulation synchrony of a butterfly inhabiting a fragmented landscape. Global Change Biol 24:4316–4329
Keith DA, Akçakaya HR, Thuiller W, Midgwley GF, Pearson RG, Phillips SJ, Regan HM, Araújo MB, Rebelo TG (2008) Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biol Lett 4:560–563
Klausmeyer KR, Shaw MR, MacKenzie JB, Cameron DR (2011) Landscape-scale indicators of biodiversity's vulnerability to climate change. Ecosphere. https://doi.org/10.1890/es11-00044.1
Krull CR, Stanley MC, Burns BR, Choquenot D, Etherington TR (2016) Reducing wildlife damage with cost-effective management programmes. PLoS ONE 11:e0146765
Landguth EL, Bearlin A, Day CC, Dunham J (2017) CDMetaPOP: an individual-based, eco-evolutionary model for spatially explicit simulation of landscape demogenetics. Methods Ecol Evol 8:4–11
Landguth EL, Muhlfeld CC, Waples RS, Jones L, Lowe WH, Whited D, Lucotch J, Neville H, Luikart G (2014) Combining demographic and genetic factors to assess population vulnerability in stream species. Ecol Appl 24:1505–1524
Laurance WF (1998) A crisis in the making: responses of Amazonian forests to land use and climate change. Trends Ecol Evol 13:411–415
Lawler JJ, Lewis DJ, Nelson E, Plantinga AJ, Polasky S, Withey JC, Helmers DP, Martinuzzi S, Pennington D, Radeloff VC (2014) Projected land-use change impacts on ecosystem services in the United States. Proc Natl Acad Sci USA 111:7492–7497
Lemoine N, Bauer HG, Peintinger M, Bohning-Gaese K (2007) Effects of climate and land-use change on species abundance in a central European bird community. Conserv Biol 21:495–503
Leroux SJ, Larrivee M, Boucher-Lalonde V, Hurford A, Zuloaga J, Kerr JT, Lutscher F (2013) Mechanistic models for the spatial spread of species under climate change. Ecol Appl 23:815–828
Lowe WH, Allendorf FW (2010) What can genetics tell us about population connectivity? Mol Ecol 19:3038–3051
Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621
Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197
McCarthy MA, Thompson C (2001) Expected minimum population size as a measure of threat. An Constr 4:351–355
Mims MC, Day CC, Burkhart JJ, Fuller MR, Hinkle J, Bearlin A, Dunham JB, DeHaan PW, Holden ZA, Landguth EE (2019) Simulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened char. Ecosphere 10:e02589
Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol Biogeogr 12:361–371
Pearson RG, Stanton JC, Shoemaker KT, Aiello-Lammens ME, Erst PJ, Horning N, Fordham DA, Raxworthy CJ, Ryu HY, McNees J, Akçakaya HR (2014) Life history and spatial traits predict extinction risk due to climate change. Nat Clim Change 4:217–221
Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175
Radinger J, Essl F, Holker F, Horky P, Slavik O, Wolter C (2017) The future distribution of river fish: the complex interplay of climate and land use changes, species dispersal and movement barriers. Global Change Biol 23:4970–4986
Reid BN, Mladenoff DJ, Peery MZ (2017) Genetic effects of landscape, habitat preference and demography on three co-occurring turtle species. Mol Ecol 26:781–798
Reid BN, Peery MZ (2014) Land use patterns skew sex ratios, decrease genetic diversity and trump the effects of recent climate change in an endangered turtle. Divers Distrib 20:1425–1437
Reid BN, Thiel RP, Palsbøll PJ, Peery MZ (2016a) Linking genetic kinship and demographic analyses to characterize dispersal: methods and application to Blanding’s turtle. J Hered 107:603–614
Reid BN, Thiel RP, Peery MZ (2016b) Population dynamics of endangered Blanding's turtles in a restored area. J Wildlife Manag 80:553–562
Segelbacher G, Cushman SA, Epperson BK, Fortin MJ, Francois O, Hardy OJ, Holderegger R, Taberlet P, Waits LP, Manel S (2010) Applications of landscape genetics in conservation biology: concepts and challenges. Conserv Genet 11:375–385
Sohl TL, Sleeter BM, Sayler KL, Bouchard MA, Reker RR, Bennett SL, Sleeter RR, Kanengieter RL, Zhu Z (2012) Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States. Agr Ecosyst Environ 153:1–15
Sohl TL, Wimberly MC, Radeloff VC, Theobald DM, Sleeter BM (2016) Divergent projections of future land use in the United States arising from different models and scenarios. Ecol Model 337:281–297
South A (1999) Dispersal in spatially explicit population models. Conserv Biol 13:1039–1046
Stanton JC, Shoemaker KT, Pearson RG, Akçakaya HR (2014) Warning times for species extinctions due to climate change. Global Change Biol 21:1066–1077
Sultaire SM, Pauli JN, Martin KJ, Meyer MW, Notaro M, Zuckerberg B (2016) Climate change surpasses land-use change in the contracting range boundary of a winter-adapted mammal. Proc R Soc B-Biol Sci 283:20153104
Tayleur CM, Devictor V, Gaüzère P, Jonzén N, Smith HG, Lindström A (2016) Regional variation in climate change winners and losers highlights the rapid loss of cold-dwelling species. Divers Distrib 22:468–480
Thatte P, Joshi A, Vaidyanathan S, Landguth E, Ramakrishnan U (2018) Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biol Conserv 218:181–191
Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, De Siqueira MF, Grainger A, Hannah L, Hughes L Huntley B, Van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:145–148
Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin JM, Hoegh-Guldberg O, Bairlen F (2002) Ecological responses to recent climate change. Nature 416:389–395
Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11:e0156720
Warren R, Price J, VanDerWal J, Cornelius S, Sohl H (2018) The implications of the United Nations Paris Agreement on climate change for globally significant biodiversity areas. Clim Change 147:395–409
Watts MJ, Fordham DA, Akçakaya HR, Aiello-Lammens ME, Brook BW (2013) Tracking shifting range margins using geographical centroids of metapopulations weighted by population density. Ecol Model 269:61–69
Webb JK, Brook BW, Shine R (2002) What makes a species vulnerable to extinction? Comparative life-history traits of two sympatric snakes. Ecol Res 17:59–67
Xu J, Wild G (2018) Dispersal altering local states has a limited effect on persistence of a metapopulation. J Biol Dynam 12:455–470
Ye X, Skidmore AK, Wang T (2013) Within-patch habitat quality determines the resilience of specialist species in fragmented landscapes. Land Ecol 28:135–147
We would like to thank the Wisconsin National Heritage Inventory Program and the Wisconsin Department of Natural Resources for providing occurrence records for this study. We would also like to thank John D. J. Clare and Monica G. Turner for providing advice on several aspects of study design. Furthermore, we would like to thank Benjamin Zuckerberg for providing computational assistance and advice crucial for this study. This work was supported by a United States Department of Agriculture Hatch Act formula grant to MZP (WIS01865). We have no conflicts of interest to disclose. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding was provided by U.S. Department of Agriculture (Grant Number WIS01865).
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Byer, N.W., Reid, B.N. & Peery, M.Z. Genetically-informed population models improve climate change vulnerability assessments. Landscape Ecol 35, 1215–1228 (2020). https://doi.org/10.1007/s10980-020-01011-x