Matthew A. Mumma, PhD - Curriculum Vitae, Google Scholar, ResearchGate

Fulbright Postdoctoral Scholar in Northern Issues
Ecosystem Science & Management Program
University of Northern British Columbia
Prince George, BC

PhD (2014) University of Idaho, advisor - Lisette Waits
BS (2003) Kutztown University of Pennsylvania

Research Interests
I am an applied ecologist interested in the effects of disturbance on wildlife populations and communities.
My approach is to frame applied questions in the context
of existing ecological theory to identify solutions that generalize across systems. My research focuses on the underlying mechanisms of ecological processes, such as predation, competition, and apparent competition.

Current Projects

Moose Survival and Landscape Change in British Columbia
I am working with Mike Gillingham at the University of Northern British Columbia and Provincial biologists to evaluate the effects of mountain pine beetle outbreaks and salvage logging on moose (Alces alces) survival under a competing risks framework in British Columbia. We are interested in understanding how landscape changes alter risk from gray wolves (Canis lupus) and human hunters, while also considering the influence of landscape change on moose behavior as it pertains to thermal refugia.

Boreal Woodland Caribou Population Modeling
I am working with Chris Johnson and Martin-Hughes St-Laurent to develop a population model for boreal woodland caribou (Rangifer tarandus caribou). Boreal caribou are federally listed as threatened across their range largely as a result of resource development. Recovery options are logistically difficult and expensive. We are developing a modeling tool for managers to evaluate the viability and cost of recovery options for their populations.

Predation Risk for Boreal Woodland Caribou in British Columbia
I am working with Mike Gillingham, Kathy Parker, and Chris Johnson at the University of Northern British Columbia and Megan Watters of the Ministry of Environment, British Columbia to evaluate mechanisms leading to increased predation risk for boreal woodland caribou (Rangifer tarandus caribou) from gray wolves (Canis lupus) in northeast British Columbia. The exacerbation of apparent competition between boreal caribou and moose (Alces alces) via anthropogenic disturbance is the most-cited mechanism leading to boreal caribou population declines, but research also demonstrates that wolves select for roads and seismic lines, which might increase risk to caribou by increasing spatial overlap with wolves. We are evaluating support for changes in apparent competition, both as a result of numeric changes in moose density and spatial changes in moose distributions, while also considering the direct effect of roads and seismic lines on wolf distributions.

Mesocarnivore Release in Alaska
I am working with Kelly Sivy, Casey Pozzanghera, and Laura Prugh at the University of Washington to explore the consequences of interspecific interactions on carnivore diet and density. We are evaluating the cascading effects of gray wolf (Canis lupus) removal on diet partitioning between coyotes (Canis latrans) and red foxes (Vulpes vulpes) and are using spatial capture-recapture models to estimate changes in coyote and red fox densities.

Woodland Caribou-Predator Interactions in Newfoundland
I am working with researchers in Newfoundland and academic researchers across the US and Canada to evaluate caribou-predator interactions in Newfoundland. Woodland caribou (Rangifer tarandus caribou) populations in Newfoundland have declined >66% since 1998. High calf mortality from black bears (Ursus amercanus) and coyotes (Canis latrans) are the proximate cause of decline. We are exploring differences in the patterns of predation between black bears and coyotes toward a better understanding of the compensatory and additive effects of predation on calf survival and population growth.

Abundance Estimation of Clustered Populations
I am working with Craig Miller, Rob Lonsinger, and Erkan Buzbas at the University of Idaho to develop an abundance estimation method for clustered populations. Our approach is to estimate population parameters (abundance, sex ratio, etc.) using Approximate Bayesian Computation (ABC) to compare sampling distributions from simulated test datasets (with known population parameters) to sampling distributions of real datasets.