The spread of non-native species continues to increase around the globe, making it important we understand the dynamics of the resulting communities in which non-natives comprise a high percentage of the total fauna. As non-native species continue to invade, the resulting community may become saturated, at which point limited resources would prevent colonization by new non-native species and native or already established species might decline. As the global hotspot for non-native reptiles and amphibians, South Florida’s herpetofaunal community has a higher probability of having reached the saturation point than any other comparable system. Surveys conducted in Miami-Dade County in 2017 demonstrated that non-native species already dominated both native and non-native habitat types and provided a baseline to examine dynamic changes such as signatures of community saturation or negative impacts on native species. In 2022, we replicated the surveys from 2017 at the same 30 sites. We found that non-native richness and abundance have increased significantly (19% and 33% increase in overall alpha diversity and abundance, respectively), showing no signs of community saturation. We also found no correlation between these non-native increases and decreases in either native species richness or abundance. Non-native species richness increased more rapidly at sites dominated by non-native habitats, with two rock-loving species, Agama picticauda and Leiocephalus carinatus, standing out as the most rapidly spreading non-native herpetofauna. Our findings demonstrate that open niche space allows the continued expansion of non-native herpetofaunal populations even in the highly invaded community of Miami-Dade County, and that protection of native habitat may help slow the spread of non-native species.

Juvenile growth rate is a critical demographic parameter, as it shortens time to maturity and often dictates how long individuals remain vulnerable to predation. However, developing a mechanistic understanding of the factors determining growth rates can be difficult for wild populations. The gopher tortoise (Gopherus polyphemus) is an ecosystem engineer threatened by habitat loss and deficient management of pinelands in the southeastern United States. We investigated the factors governing immature gopher tortoise growth and explored use of drone-based imagery for habitat assessment by comparing predictive models based on ground-based plant surveys versus drone-derived data. From 2021-2022, we tracked and measured immature tortoises in native sandhill and human-modified, ruderal habitat in south-central Florida. Using quarterly, high-resolution drone imagery, we quantified plant cover types and vegetation indices at each occupied burrow, and measured frequency of occurrence of forage species by hand. Annual growth rates of immature tortoises in ruderal habitat were higher than those in sandhill and were the highest published for this species. Models based on drone-derived data were able to explain similar proportions of variation in growth as ground-collected measures of forage, especially during the late dry season when both types of models were most predictive. Habitat differences in forage nitrogen content were also more pronounced during this season, when dominant ground cover in ruderal habitat (bahiagrass) had much higher nitrogen content than dominant ground cover in sandhill (wiregrass). Despite concerns about potential growth-survival trade-offs, tortoises in ruderal habitat did not exhibit lower apparent survival. Our findings indicate that habitat dominated by nutritious non-native grass can provide a valuable supplement to native sandhill through the mechanism of increased growth rates due to higher forage quality. Finally, our study demonstrates that drone technology may facilitate management by providing less labor-intensive ways to assess habitat quality for this and other imperiled herbivores.

A key goal in ecology is to develop more effective ways to understand species’ distributions in order to facilitate both their study and conservation. Many species distribution modeling analyses have been performed to date, using either structured survey data or unstructured citizen science data; these two pools of data have tradeoffs in terms of data density, spatiotemporal coverage, and accuracy. Recent studies have shown that combining structured and unstructured survey data can greatly improve the accuracy of species distribution models for birds, but most of this work has focused on north temperate bird species and uses bird atlas data that is much more common in the temperate zone than elsewhere. We sought to adapt a data pooling approach from the literature on north temperate bird biology to create distribution models for a selection of secretive suboscine bird species that occur in a highly diverse region of the southwestern Amazon.