Nathan Schumaker

Research Ecologist

EPA - Health & Environmental Effects Research Laboratory

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I specialize in the development of predictive wildlife models. My research spans the fields of conservation biology, landscape ecology, genetics, and epidemiology. The overarching goal of my work is the development of scientifically defensible theory, technology, and methods that improve wildlife conservation and inform policy. To this end, I collaborate with scientists and conservation practitioners throughout the world. Below, I describe some focal areas that characterize my contributions.

Simulation modeling

  • Designed and developed the HexSim life history simulator.

Conservation practice

  • Used HexSim, and its predecessor PATCH, in the development of spatial simulation models for >55 different wildlife species.

Conservation theory

  • Conceived of and constructed novel research methods that bring biological detail, individual behavior, species interactions, and meaningful disturbance regimes to assessments of population trends, source-sink dynamics, and measures of landscape connectivity. Developed new approaches to predict spatio-temporal patterns of gene flow and disease spread in real-world systems.


Dunk JR, Woodbridge B, Schumaker NH, Glenn EM, White B, LaPlante DW, et al. 2019. Conservation planning for species recovery under the Endangered Species Act: A case study with the Northern Spotted Owl. PLoS ONE 14(1): e0210643. pone.0210643.

Heinrichs JA, Lawler JJ, Schumaker NH, Walker L, Cimprich D, Bleisch A. Assessing source-sink stability in the context of management and land-use change. Landscape Ecology (in press).

Dickson BG, Albano CM, Anantharaman R, Beier P, Fargione J, Graves TA, Gray ME, Hall KR, Lawler JJ, Leonard PB, Littlefield CE, McClure ML, Novembre J, Schloss CA, Schumaker NH, Shah VB, Theobald DM. Circuit-theory applications to connectivity science and conservation. Conservation Biology (in press).

Schumaker NH, Brookes A. 2018. HexSim: a modeling environment for ecology and conservation. Landscape Ecology 33:197-211.

Heinrichs JA, Lawler JJ, Schumaker NH, Wilsey CB, Monroe KC, Aldridge CL. 2018. A multispecies test of source.sink indicators to prioritize habitat for declining populations. Conservation Biology 32:648-659.

Heinrichs JA, Aldridge CL, Gummer DL, Monroe AP, Schumaker NH. 2018. Prioritizing actions for the recovery of endangered species: Emergent insights from Greater Sage-grouse simulation modeling. Biological Conservation 218:134-143.

Pacioni C, Kennedy MS, Berry O, Stephens D, Schumaker NH. 2018. Spatially-explicit model for assessing wild dog control strategies in Western Australia. Ecological Modelling:368:246-256.

Heinrichs JA, Aldridge CL, O’Donnell M, Schumaker NH. 2017. Using dynamic population simulations to extend resource selection analysis and prioritize habitat for conservation. Ecological Modelling 359:449-459.

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. Journal of Raptor Research 51:234-257.

Pierson JC, Coates DJ, Oostermeijer GB, Beissinger SR, Bragg JG, Sunnucks P, Schumaker NH, Young AG. 2016. An assessment of the consideration of genetic factors in threatened species recovery plans on three continents. Frontiers in Ecology and the Environment 14:433-440.