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Habitat Suitability

Identifying and understanding the abiotic or biotic factors drive species-specific habitat quality or suitability is fundamental to ecology. However, habitat suitability can refer to two very different properties, occupancy versus abundance. When evaluating which factors drive occupancy, we are interested in how environmental variables predict the probability a species is found at a given location by developing niche models or occupancy models. However, when interested in abundance, we focus on how environmental variables drive the number of individuals at a site. These two questions are related but require both different methods and different interpretations.

The graph visualizes Hutchinson’s multidimensional niche concept where by the organism’s niche is dependent upon the combination of multiple enviromental variables.

Niche Modeling

Ecological niche models (ENMs) attempt to predict a species’ fundamental niche using occurrence and environmental data. With the development of remote sensing, researchers have access to numerous publicly available environmental databases. Furthermore, public occurrence data is available through multiple databases such as the Global Biodiversity Information Facility and FishBase. However, these databases should be used with caution. Occurrence data could lack high spatial resolution, proper identification, or improper taxonomic nomenclature for species who underwent recent revisions. Coastal habitats present unique difficulties for niche modeling as they are influenced by both terrestrial and marine environmental conditions. My research leverages over a decade of sample throughout the mangrove swamps of Florida to evaluate the niche of Kryptolebias marmoratus while incorporating the influence of adjacent environmental variables. After model development, I estimate how different representative concentration pathways (RCPs), climate change scenarios, may impact the fundamental niche of rivulus.

Multiple abiotic (temperature, precipitation, etc..) and biotic factors (competition, predation, etc..) likely drive abundance.

Abundance Modeling

ENMs attempt to predict the fundamental niche; however, they are inherently confounded with the realized niche given that occurrence points are a product of both abiotic and biotic variables, including dispersal, species-interactions, and evolutionary history. To disentangle the impacts of biotic and abiotic variables, I apply N-mixture models within a Bayesian framework to evaluate the impact of community structure and local environmental variables on the abundance of Kryptolebias marmoratus. Using over ten years of data, I evaluate species-specific population responses to enviromental variables, interspecific interactions, and abundance fluctuations. Using these models, we can compare the influence of environmental variables on individual species abundance across community members. This community-wide species specific approach is imperative given climate change will introduce both new abiotic and biotic challenges through rapid enviromental change and species-specific range shifts, respectively.

Environmental DNA

eDNA deposited by rivulus is likely impacted by ultraviolet (UV) radiation, time, and temperature. Salinity may also impact rates of eDNA degradation.

Environmental DNA (eDNA) refers to DNA fragments deposited by organisms into the environment through slime, skin/scales, gametes, tissues, and excrement. The ability to capture, quantify, and identify species from these small fragments has opened up new veins of research with direct implications for both ecology and conservation. My current work focuses on expanding eDNA from a presence/absence tool to a quick, accurate, and unbiased estimate of local abundance for coastal and marine species. I develop models to estimate local abundance as a function of eDNA concentration and a host of environmental variables, including temperature, salinity, and ultraviolet light, that may impact the degradation rate of eDNA. I do this with a combination of field experiments and controlled laboratory experiments to isolate the impact of abiotic variables on eDNA degradation rates.

Seascape Genetics

Landscape and seascape genetics use population genetic concepts to explore how abiotic factors (ocean currents, temperature) influence spatial genetic patterns across the landscape. Understanding how environmental factors affect gene flow or genetic diversity can, in turn, inform the design of protected areas and corridors through which individuals can move. Identifying these factors is especially urgent given that low genetic diversity can make species less resilient to climate change, and connectivity losses can shift metapopulation structure resulting in local species declines.

Genetic Diversity

Identifying the factors driving patterns in genetic diversity is essential given the imminent threat of climate change. Evolution acts on phenotypic variation, a product of physiological responses to the enviroment such as plasticity or flexibility and genetic variation. Therefore, protecting genetic diversity is critical to a species’ ability to respond to environmental change. I am currently evaluating the relative impact of landscape configuration, local site-specific variables, and the intervening matrix, unsuitable habitat between populations, on genetic diversity in Kryptolebias marmoratus.

Gene Flow

Traditional landscape genetic studies investigate how population connectivity is affected by minimum, maximum, or average values for environmental variables such as terrain, vegetation cover, or elevation, which do not rapidly change. However, abiotic factors that affect gene flow in marine environments, such as salinity or ocean current, are in constant flux because of variation in temperature, sea level, and wind. Understanding how these dynamic factors affect gene flow is challenging because traditional landscape genetic techniques cannot account for this variation. My research uses methods adapted from physical oceanography to estimate the connectivity between populations via ocean currents while including the inherent variability of the marine environment. I use this estimate connectivity with genetic data to evaluate the role ocean currents play in driving patterns of gene flow between populations.

Adapted from the Ocean Circulation Group