Skip to main content
Log in

HexSim: a modeling environment for ecology and conservation

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

Abstract

Context

Simulation models are increasingly used in both theoretical and applied studies to explore system responses to natural and anthropogenic forcing functions, develop defensible predictions of future conditions, challenge simplifying assumptions that facilitated past research, and to train students in scientific concepts and technology. Researcher’s increased use of simulation models has created a demand for new platforms that balance performance, utility, and flexibility.

Objectives

We describe HexSim, a powerful new spatially-explicit, individual-based modeling framework that will have applications spanning diverse landscape settings, species, stressors, and disciplines (e.g. ecology, conservation, genetics, epidemiology). We begin with a model overview and follow-up with a discussion of key formative studies that influenced HexSim’s development. We then describe specific model applications of relevance to readers of Landscape Ecology. Our goal is to introduce readers to this new modeling platform, and to provide examples characterizing its novelty and utility.

Conclusions

With this publication, we conclude a > 10 year development effort, and assert that our HexSim model is mature, robust, extremely well tested, and ready for adoption by the research community. The HexSim model, documentation, worked examples, and other materials can be freely obtained from the website www.hexsim.net.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Akcakaya HR, Root WT (2005) RAMAS GIS: linking landscape data with population viability analysis (version 5.0). Applied Biomathematics, Setaukey, NY

    Google Scholar 

  • Bancroft BA, Lawler JJ, Schumaker NH (2016) Weighing the relative potential impacts of climate change and land-use change on an endangered bird. Ecology and Evolution 6:4468–4477

    Article  PubMed  PubMed Central  Google Scholar 

  • Bocedi G, Palmer S, Pe’er G, Heikkinen RK, Matsinos YG, Watts K, Travis J (2014) RangeShifter: a platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes. Methods Ecol Evol 5:388–396

    Article  Google Scholar 

  • Bond ML, Bradley CM, Kiffner C, Morrison TA, Lee DE (2017) A multi-method approach to delineate and validate migratory corridors. Landscape Ecol 32:1705–1721

    Article  Google Scholar 

  • Briggs CJ, Vredenburg VT, Knapp RA, Rachowicz LJ (2005) Investigating the population-level effects of Chytridiomycosis: an emerging infectious disease of amphibians. Ecology 86:3149–3159

    Article  Google Scholar 

  • Calabrese JM, Fagan WF (2004) A comparison-shopper’s guide to connectivity metrics. Front Ecol Environ 2:529–536

    Article  Google Scholar 

  • Carvalho CF (2014) Atropelamento de vertebrados, hotspots de atropelamentos e parâmetros associados, BR-050, trecho Uberlândia-Uberaba. Master thesis in Ecology and Conservation of Natural Resources—Universidade Federal de Uberlândia, Uberlândia, Minas Gerais

  • Fordham DA, Shoemaker KT, Schumaker NH, Akçakaya HR, Clisby N, Brook BW (2014) How interactions between animal movement and landscape processes modify range dynamics and extinction risk. Biol Let 10:20140198

    Article  Google Scholar 

  • Fulford RS, Peterson MS, Grammer PO (2011) An ecological model of the habitat mosaic in estuarine nursery areas: part I—interaction of dispersal theory and habitat variability in describing juvenile fish distributions. Ecol Model 222:3202–3215

    Article  Google Scholar 

  • Heinrichs JA, Aldridge CL, O’Donnell M, Schumaker NH (2017) Using dynamic population simulations to extend resource selection analysis and prioritize habitat for conservation. Ecol Model 359:449–459

    Article  Google Scholar 

  • Heinrichs JA, Bender DJ, Gummer DL, Schumaker NH (2010) Assessing critical habitat: evaluating the relative contribution of habitats to population persistence. Biol Cons 143:2229–2237

    Article  Google Scholar 

  • Heinrichs JA, Bender DJ, Gummer DL, Schumaker NH (2015a) Effects of landscape and patch-level attributes on regional population persistence. J Nat Conserv 26:56–64

    Article  Google Scholar 

  • Heinrichs JA, Bender DJ, Schumaker NH (2016a) Habitat degradation and loss as key drivers of regional population extinction. Ecol Model 335:64–73

    Article  Google Scholar 

  • Heinrichs JA, Lawler JJ, Schumaker NH (2016b) Intrinsic and extrinsic drivers of source-sink dynamics. Ecol Evol 6:892–904

    Article  PubMed  PubMed Central  Google Scholar 

  • Heinrichs JA, Lawler JJ, Schumaker NH, Wilsey C, Bender DJ (2015b) Divergence in sink contributions to population persistence. Conserv Biol 29:1674–1683

    Article  PubMed  Google Scholar 

  • Huber PR, Greco SE, Schumaker NH, Hobbs J (2014) A priori assessment of reintroduction strategies for a native ungulate: using HexSim to guide release site selection. Landscape Ecol 29:689–701

    Article  Google Scholar 

  • Landguth EL, Cushman SA (2009) CDPOP: a spatially explicit cost distance population genetics program. Mol Ecol Resour 10:156–161

    Article  PubMed  Google Scholar 

  • Leslie PH (1945) On the use of matrices in certain population mathematics. Biometrika 33:183–212

    Article  CAS  PubMed  Google Scholar 

  • Lurgi M, Brook BW, Saltré F, Fordham DA (2015) Modelling range dynamics under global change: which framework and why? Methods Ecol Evol 6:247–256

    Article  Google Scholar 

  • Marcot BG, Raphael MG, Schumaker NH, Galleher B (2013) How big and how close? Habitat patch size and spacing to conserve a threatened species. Nat Resour Model 26:194–214

    Article  Google Scholar 

  • Marcot BG, Singleton PH, Schumaker NH (2015) Analysis of sensitivity and uncertainty in an individual-based model of a threatened wildlife species. Nat Resour Model 28:37–58

    Article  Google Scholar 

  • Marrotte RR, Bowman J, Brown MG, Cordes C, Morris KY, Prentice MB, Wilson PJ (2017) Multi-species genetic connectivity in a terrestrial habitat network. Mov Ecol 5:21

    Article  PubMed  PubMed Central  Google Scholar 

  • McClure ML, Hansen AJ, Inman RM (2016) Connecting models to movements: testing connectivity model predictions against empirical migration and dispersal data. Landscape Ecol 31:1–14

    Article  Google Scholar 

  • McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561

    Article  PubMed  Google Scholar 

  • McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. PNAS 104:19885–19890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Minor ES, Urban DL (2007) Graph theory as a proxy for spatially explicit population models in conservation planning. Ecol Appl 17:1771–1782

    Article  PubMed  Google Scholar 

  • Nogeire TM, Lawler JJ, Schumaker NH, Cypher BL, Phillips SE (2015) Land use as a driver of patterns of rodenticide exposure in modeled kit fox populations. PLoS ONE 10(8):e0133351.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pacioni C, Kennedy MS, Berry O, Stephens D, Schumaker NH (2018) Spatially-explicit model for assessing wild dog control strategies in Western Australia. Ecol Model 368:246–256

    Article  Google Scholar 

  • Pomara LY, Ledee OE, Martin KJ, Zuckerberg B (2014) Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species. Glob Change Biol 20:2087–2099

    Article  Google Scholar 

  • Pulliam RH (1988) Sources, sinks, and population regulation. Am Nat 132(132):652–661

    Article  Google Scholar 

  • Schiffers KH, Travis J (2014) ALADYN—a spatially explicit, allelic model for simulating adaptive dynamics. Ecography 37:1288–1291

    Article  PubMed  PubMed Central  Google Scholar 

  • Schumaker NH, Brookes A, Dunk JR, Woodbridge B, Heinrichs J, Lawler JJ, Carroll C, LaPlante D (2014) Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls. Landscape Ecol 29:579–592

    Article  Google Scholar 

  • Stronen AV, Schumaker NH, Forbes GJ, Paquet PC, Brook RK (2012) Landscape resistance to dispersal: simulating long-term effects of human disturbance on a small and isolated wolf population in southwestern Manitoba, Canada. Environ Monit Assess 184:6923–6934

    Article  PubMed  Google Scholar 

  • Tuma MW, Millington C, Schumaker NH, Burnett P (2016) Modeling Agassiz’s desert tortoise population response to anthropogenic stressors. J Wildl Manag 80:414–429

    Article  Google Scholar 

  • White LA, Forester JD, Craft ME (2017) Dynamic, spatial models of parasite transmission in wildlife: their structure, applications and remaining challenges. J Anim Ecol. https://doi.org/10.1111/1365-2656.12761

    Google Scholar 

  • Wiens JD, Schumaker NH, Inman RD, Esque TC, Longshore KM, Nussear KE (2017) Spatial demographic models to inform conservation planning of Golden Eagles in renewable energy landscapes. J Raptor Res 51:234–257

    Article  Google Scholar 

  • Wilsey CB, Lawler JJ, Cimprich D, Schumaker NH (2014) Dependence of the endangered black-capped vireo on sustained cowbird management. Conserv Biol 28:561–571

    Article  PubMed  Google Scholar 

  • Zang H, Gorelick SM (2014) Coupled impacts of sea-level rise and tidal marsh restoration on endangered California clapper rail. Biol Cons 172:89–100

    Article  Google Scholar 

Download references

Acknowledgements

We dedicate this paper to the memory of Dr. Brad McRae (1966–2017), our colleague and treasured friend. While insignificant in comparison to his multiple contributions to ecology and conservation science, Brad is due recognition here as a visionary force behind HexSim’s genetics toolkit. We are indebted to Jianguo Wu and two anonymous reviewers who made significant and insightful contributions that improved the paper’s structure, style, and impact. The information in this document has been funded in part by the U.S. Environmental Protection Agency. It has been subjected to review by the National Health and Environmental Effects Research Laboratory’s Western Ecology Division and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nathan H. Schumaker.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schumaker, N.H., Brookes, A. HexSim: a modeling environment for ecology and conservation. Landscape Ecol 33, 197–211 (2018). https://doi.org/10.1007/s10980-017-0605-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-017-0605-9

Keywords

Navigation