Anna Thonis

PhD Candidate

Curriculum vitae

Ecology and Evolution

Stony Brook University


My work uses a combination of quantitative modeling methods (e.g., SDMs, species richness modeling) and field work to enhance our understanding of Puerto Rican anole ecology and distributions.

Incorporating extreme weather events in species distribution models

Species distribution models (SDMs, also called ecological niche models or ENMs) use a set of environmental predictor variables and occurrence records for a species to predict a species’ probability of presence (sometimes referred to as a species’ habitat suitability). Frequently, the climatic data used in SDMs are climatic averages (e.g., the annual mean precipitation) at some spatial resolution. Temporal variability in climatic data is more complicated to include and often overlooked. However, several studies have found that weather variability and/or extreme weather events can shape species distributions more strongly than climatic averages. To address this idea, I developed spatially- and temporally-explicit SDMs for all ten species of Puerto Rican Anolis lizard that directly incorporate Puerto Rico’s history of tropical cyclones (i.e., hurricanes, tropical storms, tropical depressions). I found that the inclusion of tropical cyclone variables improved model performance for the majority of Puerto Rico’s ten anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. These findings suggest that incorporating data on tropical cyclones into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events. This work is about to be submitted for publication.
AUC(test) performance metrics for each species with and without the inclusion of tropical cyclone (TC) variables.

Modeling species richness 

There are various methods for modeling species richness in an area, including macroecological models (MEMs), stacked species distribution models (S-SDMs), and joint species distribution models (JSDMs). A common goal of modeling species richness is to implicitly incorporate species interactions. By stacking single-species SDMs and then constraining these predictiions with a MEM, or by modeling all target species in an area jointly (species co-occurrences inform predictions) using JSDMs, we can indirectly infer species interactions. I am generating species richness models using these two methods and assessing how their predictions of which species are present in a given grid cell deviate from single-species models for all ten species of Puerto Rican anole. This work is ongoing.

Species interactions

We (myself and a team of undergraduate students) conducted manual removal and addition experiments in Utuado, Puerto Rico to quantify changes in growth rates and gravidity in A. gundlachi driven by changes in the density of either A. gundlachi, A. evermanni, or A. cristatellus. I found that intraspecific competition within A. gundlachi to be strongest (i.e., largest effects on growth rates and gravidity), followed by interspecific competition between the two species of the same ecomorph (i.e., A. gundlachi and A. cristatellus), and weakest - albeit still present - between the two species of different ecomorph (i.e., A. gundlachi and A. evermanni). These findings aligned with our expectations given the varying degrees of ecological similarity between these species. This work is currently In Press. 
A depiction of our experimental design for the manual removal and addition of these three anole species.

Quantifying species demography

Although Anolis lizards are well-studied with respect to their evolutionary biology, behavior, phenotypic variation, and more, we know comparatively little about their demography. Using a multi-year robust-design mark-recpature study on three species of anole (A. gundlachi, A. evermanni, and A. cristatellus), I am quantifiying a number of demographic paramaters for these three species. Additionally, and with the help of University of Puerto Rico Mayaguez Professor Alberto Puente-Rolon, we are going to continue surveying these sites and build a long-term data set for these species. In doing so, we will be able to examine natural variability in demography through time, as well as any changes in demography driven by extreme weather events such as tropical cyclones. This work is ongoing.
Tagged yellow-chinned anole (Anolis gundlachi) with Tag ID Z69 green.
Tagged yellow-chinned anole (Anolis gundlachi) with Tag ID Z69 green.
Tagged yellow-chinned anole (Anolis gundlachi) with Tag ID B92 orange.
Tagged yellow-chinned anole (Anolis gundlachi) with Tag ID B92 orange.

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