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.

Ecological Niche Modeling

Ecological niche models (ENMs) attempt to predict a species’s 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 like 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 that 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 samples 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 (Snead & Earley 2022).

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 the abiotic (Fundamental niche) and biotic variables, including dispersal and 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. I use over ten years of data to evaluate species-specific population responses to environmental variables, interspecific interactions, and abundance fluctuations.

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. interpretations. 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 environment 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 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 estimated connectivity with genetic data to evaluate ocean currents’ role in driving gene flow patterns between populations.

Environmental DNA

     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 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.

eDNA Accumulation and Degradation Rates

The rate of DNA shedding and degradation is dependent on abiotic and biotic variables such as temperature, salinity, ultraviolet radiation, salinity biomass, metabolic rate, and microbial community. At a particular abundance, eDNA concentration could reach an asymptote representing a balance between eDNA accumulation (shedding) and degradation. Therefore, asymptotic values could be used to estimate abundance as long as appropriate cofounding variables are considered. To accurately characterize the impact of confounding variables, I perform lab-based eDNA experiments in which I manipulate abiotic conditions and fish density to quantify the effects of abiotic variables on eDNA accumulation and degradation rates.

eDNA- A Proxy for Abundance

Estimating abundance in natural systems is a fundamental component of ecological research and conservation. However, the accuracy of abundance estimates is impacted by sampling method, human biases, and local environmental conditions. Replicate surveys are often required to disentangle the true abundance from variability in species detection. To avoid these limitations in aquatic systems, invasive sampling such as trawling or electrofishing is often employed, which is highly invasive and can potentially impact target species. Using replicate surveys, I am developing hierarchal Bayesian models to 1) estimate true abundance, 2) evaluate the impact of abiotic variables on eDNA concentration, and 3) estimate relative abundance from eDNA concentration and relevant abiotic variables in wild populations.