Research

Why do various kinds and numbers of species live in different environments? It's a simple question that usually has a complex answer. This is because there are many processes that determine where species live, and who they live with. These processes arise from interactions between one or more axes of environmental heterogeneity, evolutionary trade-offs among organisms, and the density of different resources species may need in a given locality. It is my goal to better understand these processes to better conserve wildlife.

Overall, my research can be categorized into two broad areas.

Urban ecology

The composition of urban wildlife communities are determined by factors at varying hierarchical scales (Magle et al. 2019).

In the last decade humanity has transitioned from primarily living in rural to urban areas, and in response urbanization rates have increased worldwide. And even though it is clear that urbanization profoundly alters the distribution and abundance of wildlife, there is still much to discover to better sustain biodiversity on an urbanizing planet.

Through my research I seek to better understand how urban wildlife communities respond to urbanization. I am especially interested in determining how urban wildlife communities differ at varying hierarchical scales (e.g., both within and between cities). By doing so, we can improve our understanding of how urban wildlife populations and communities form and persist as well as how they interact with humans. This should, in turn, allow us to inform urban planning to make cities work better for humans and wildlife.

Fidino et al. (2024). Gentrification drives patterns of alpha and beta diversity in cities. PNAS.
PNAS. (PDF)

Haight et al. (2023). Urbanization, climate, and species traits shape mammal communities from local to continental scales. Nature Ecology and Evolution.
Nature Ecology & Evolution. (PDF)

Fidino et al. (2020). Landscape-scale differences among cities alter common species responses to urbanization.
Ecological Applications. (PDF)

Murray et al. (2020). City sanitation and socioeconomics predict rat zoonotic infection across diverse neighbourhoods.
Zoonoses and Public Health. (PDF)

Zellmer et al. (2020). What can we learn from wildlife sightings during the COVID‐19 global shutdown?
Ecosphere. (PDF)

Magle et al. (2019). Advancing urban wildlife research through a multi-city collaboration.
Frontiers in Ecology and the Environment. (PDF)

Gallo et al. (2019). Urbanization alters predator avoidance behaviors.
Journal of Animal Ecology. (PDF)

Gallo et al. (2018). Need for multiscale planning for conservation of urban bats.
Conservation Biology. (PDF)

Magle and Fidino (2018). Long-term declines of a highly interactive urban species.
Biodiversity and Conservation. (PDF)

Murray et al. (2018). Public Complaints Reflect Rat Relative Abundance Across Diverse Urban Neighborhoods.
Frontiers in Ecology and Evolution. (PDF)

Fidino and Magle (2017). Trends in long-term urban bird research.
Ecology and Conservation of Birds in Urban Environments. (PDF)

Gallo et al. (2017). Mammal diversity and metacommunity dynamics in urban green spaces: implications for urban wildlife conservation. Ecological Applications (PDF)

Fidino et al. (2016). Habitat dynamics of the virginia opossum in a highly urban landscape.
The American Midland Naturalist. (PDF)

Quantitative ecology

Colonization probabilities (the likelihood of showing up to a habitat patch at time t given absence at time t-1) of coyote, opossum, and raccoon conditional on the presence or absence of each other estimated from thirteen seasons of camera trapping data in Chicago, Illinois, USA. Colonization probabilities in each column are conditional on the presence or absence of the species in a given row. Lines in each subplot are posterior means while the shaded ribbons represent 95% credible intervals (Fidino et al. 2019).

The models that ecologists develop, be they graphical, mathematical, or statistical are abstractions of the truth and function as a means to extract useful information from data. Maps, for instance, are a model of the geographic features of a region, and good maps are useful. Ecologists are mapmakers of the natural world, and it is our job to simplify and approximate what we can so that we may better conserve and understand the species around us. Our ability to do this is, in part, conditional on the methodological tools at our disposal. I have a keen interest in developing techniques to get the most ecologically relevant information as possible from observational data, especially when data is collected imperfectly. By doing so, we can better answer both applied and basic questions in ecology.

Gerber et al. (2024). A model-based hypothesis framework to define and estimate the diel niche via the ’Diel.Niche’ R package.
Journal of Animal Ecology. (PDF)

Fidino et al. (2022). Integrated species distribution models reveal spatiotemporal patterns of human-wildlife conflict.
Ecological Applications. (PDF)

Rivera et al. (2022). Rethinking habitat occupancy modeling and the role of diel activity in an anthropogenic world.
American Naturalist. (PDF)

Murray et al. (2021). A multi‐state occupancy model to non‐invasively monitor visible signs of wildlife health with camera traps that accounts for image quality.
Journal of Animal Ecology. (PDF)

Fidino et al. (2020). The effect of lure on detecting mammals with camera traps.
Wildlife Society Bulletin. (PDF)

Voorhies et al. (2019). A method to project future impacts from threats and conservation on the probability of extinction for North American migratory monarch (Danaus plexxipus) populations.
Frontiers in Ecology and Evolution. (PDF)

Fidino et al. (2019). A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species.
Methods in Ecology and Evolution. (PDF)

Fidino et al. (2018). Assessing online opinions of wildlife through social media.
Human Dimensions of Wildlife. (PDF)

Fidino and Magle (2017). Using fourier series to estimate periodic patterns in dynamic occupancy models.
Ecosphere. (PDF)