Skip to main content

Advertisement

SpringerLink
Log in

Genetically-informed population models improve climate change vulnerability assessments

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

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.

Objectives

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).

Methods

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.

Results

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.

Conclusions

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Akçakaya HR, Root W (2013) RAMAS GIS: linking spatial data with population viability analysis (version 6). Applied Biomathematics, Setauket

    Google Scholar 

  • 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

    Google Scholar 

  • Bélisle M (2005) Measuring landscape connectivity: the challenge of behavioral landscape ecology. Ecology 86:1988–1995

    Google Scholar 

  • 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

    PubMed  PubMed Central  Google Scholar 

  • Brook BW (2008) Synergies between climate change, extinctions and invasive vertebrates. Wildlife Res 35:249–252

    Google Scholar 

  • 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

    PubMed  PubMed Central  Google Scholar 

  • Brook BW, Sodhi NS, Bradshaw CJ (2008) Synergies among extinction drivers under global change. Trends Ecol Evol 23:453–460

    PubMed  Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  PubMed  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • DeFries RS, Foley JA, Asner GP (2004) Land-use choices: balancing human needs and ecosystem function. Front Ecol Environ 2:249–257

    Google Scholar 

  • 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

    Google Scholar 

  • Ernst CH, Lovich JE (2009) Turtles of the United States and Canada. JHU Press, Baltimore

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Janzen FJ (1994) Climate change and temperature-dependent sex determination in reptiles. Proc Natl Acad Sci USA 91:7487–7490

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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

    Google Scholar 

  • 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

    PubMed  PubMed Central  Google Scholar 

  • 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

    Article  Google Scholar 

  • Krull CR, Stanley MC, Burns BR, Choquenot D, Etherington TR (2016) Reducing wildlife damage with cost-effective management programmes. PLoS ONE 11:e0146765

    PubMed  PubMed Central  Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  PubMed  Google Scholar 

  • Laurance WF (1998) A crisis in the making: responses of Amazonian forests to land use and climate change. Trends Ecol Evol 13:411–415

    CAS  PubMed  Google Scholar 

  • 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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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

    PubMed  Google Scholar 

  • 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

    PubMed  Google Scholar 

  • Lowe WH, Allendorf FW (2010) What can genetics tell us about population connectivity? Mol Ecol 19:3038–3051

    PubMed  Google Scholar 

  • Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621

    PubMed  Google Scholar 

  • Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197

    Google Scholar 

  • McCarthy MA, Thompson C (2001) Expected minimum population size as a measure of threat. An Constr 4:351–355

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    PubMed  Google Scholar 

  • 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

    Google Scholar 

  • 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

    PubMed  Google Scholar 

  • Reid BN, Thiel RP, Peery MZ (2016b) Population dynamics of endangered Blanding's turtles in a restored area. J Wildlife Manag 80:553–562

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • South A (1999) Dispersal in spatially explicit population models. Conserv Biol 13:1039–1046

    Google Scholar 

  • Stanton JC, Shoemaker KT, Pearson RG, Akçakaya HR (2014) Warning times for species extinctions due to climate change. Global Change Biol 21:1066–1077

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  PubMed  Google Scholar 

  • 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

    CAS  PubMed  Google Scholar 

  • 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

    PubMed  PubMed Central  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Xu J, Wild G (2018) Dispersal altering local states has a limited effect on persistence of a metapopulation. J Biol Dynam 12:455–470

    Google Scholar 

  • 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

    Google Scholar 

Download references

Acknowledgements

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

Funding was provided by U.S. Department of Agriculture (Grant Number WIS01865).

Author information

Authors and Affiliations

  1. Department of Forestry and Wildlife Ecology, University of WI–Madison, Madison, WI, 53706, USA

    Nathan W. Byer & M. Z. Peery

  2. W.K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI, 49060, USA

    Brendan N. Reid

  3. Russell Laboratories Room A230A, 1630 Linden Drive, Madison, WI, 53706-1598, USA

    Nathan W. Byer

Authors
  1. Nathan W. Byer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brendan N. Reid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Z. Peery
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nathan W. Byer.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (PDF 2041 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-020-01011-x

Keywords

Search

Navigation