Anna Thonis

Postdoctoral Researcher

Curriculum vitae

Department of Biology

New York University


Broadly, I am interested in how anthropogenic change affects the ecology of herpetofauna (specifically, demography, distributions, and space use). My current research 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. For papers relevant to this work, see my Publications page.
AUC(test) performance metrics for each species with and without the inclusion of tropical cyclone (TC) variables.
In March 2024, I was a Finalist in Stony Brook's Three Minute Thesis competition where I presented on this work. You can watch my talk below. 

Modeling species richness 

While various methods exist for modeling species richness for a given area, it appears that species richness models have undergone far less scrutiny than species distribution models. To assess the reliability of two of the more popular methods for modeling richness, I built macroecological models (MEMs) and stacked SDMs (SSDMs) for all ten of Puerto Rico's anole species and compared their predictions to survey data from the Puerto Rican conservation non-profit Para la NaturalezaThis work is ongoing and currently being prepared for publication.
Type II regression comparing surveyed species richness and species richness extracted from range maps at the 70 Para la Naturaleza study sites in Puerto Rico.

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 published (see Publications page). 
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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