This data release includes the data and computer code that we produced to fit two open-robust design removal models developed to simultaneously model population dynamics, temporary emigration, and imperfect detection: a random walk linear trend model (estimable without ancillary information), and a 2-age class integrated population model (IPM) that used prior information for age-structured vital rates and relative juvenile availability. To evaluate the effectiveness of management programs, we applied both models to a multi-year, removal trapping time-series data set of a large invasive lizard (Argentine black and white tegu, Salvator merianae) in three management areas of South Florida collected from 2016-1018. The data from these 3 sites as well as the code to run the models in program R are included.

Mid-sized mammals (i.e., mesomammals) fulfill important ecological roles, serving as essential scavengers, predators, pollinators, and seed dispersers in the ecosystems they inhabit. Consequently, declines in mesomammal populations have the potential to disrupt ecological processes and degrade ecosystems. However, ecosystems characterized by high functional redundancy, where multiple species can fulfill similar ecological roles, may be less impacted by the loss of mesomammals and other vertebrates. The Greater Everglades Ecosystem in southern Florida is a historically biodiverse region that has recently been impacted by multiple anthropogenic threats, most notably the introduction of the Burmese python (Python bivittatus). Since pythons became established, mesomammal populations have become greatly reduced. To assess whether these declines in mesomammals have affected two critical ecosystem functions—scavenging and frugivory—we conducted experiments in areas where mesomammals were present and absent. After passive sampling had concluded at each site, we conducted scavenging and frugivory experiments to quantify how mesomammal presence affected frugivory and scavenging rates. To assess these processes, we monitored the persistence of carrion and fruit using motion triggered cameras. We reviewed photos to identify species and determine if they consumed carrion/fruit. To quantify scavenging rates, we secured carrion to a 40 x 40 cm board at each station and recorded detection time (the elapsed time between deployment and the first scavenger’s arrival) and consumption time (the elapsed time between deployment and the complete consumption of the carcass).

Mid-sized mammals (i.e., mesomammals) fulfill important ecological roles, serving as essential scavengers, predators, pollinators, and seed dispersers in the ecosystems they inhabit. Consequently, declines in mesomammal populations have the potential to disrupt ecological processes and degrade ecosystems. However, ecosystems characterized by high functional redundancy, where multiple species can fulfill similar ecological roles, may be less impacted by the loss of mesomammals and other vertebrates. The Greater Everglades Ecosystem in southern Florida is a historically biodiverse region that has recently been impacted by multiple anthropogenic threats, most notably the introduction of the Burmese python (Python bivittatus). Since pythons became established, mesomammal populations have become greatly reduced. To assess whether these declines in mesomammals have affected two critical ecosystem functions—scavenging and frugivory—we conducted experiments in areas where mesomammals were present and absent. After passive sampling had concluded at each site, we conducted scavenging and frugivory experiments to quantify how mesomammal presence affected frugivory and scavenging rates. To assess these processes, we monitored the persistence of carrion and fruit using motion triggered cameras. We reviewed photos to identify species and determine if they consumed carrion/fruit. To quantify scavenging rates, we secured carrion to a 40 x 40 cm board at each station and recorded detection time (the elapsed time between deployment and the first scavenger’s arrival) and consumption time (the elapsed time between deployment and the complete consumption of the carcass).

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0-1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0-1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

The dataset contains 3 components: (1) acceleration data logger (ADL) data, (2) GPS location data, and (3) body temperature data. We have ADL data from pythons in captivity (N = 2) and in free-ranging snakes (N=4). We have GPS data for 3 out of 4 free-ranging snakes. We have body temperature data for all 4 free-ranging snakes.

The dataset contains 3 components: (1) acceleration data logger (ADL) data, (2) GPS location data, and (3) body temperature data. We have ADL data from pythons in captivity (N = 2) and in free-ranging snakes (N=4). We have GPS data for 3 out of 4 free-ranging snakes. We have body temperature data for all 4 free-ranging snakes.

Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. The models of particular interest to the South Florida Water Management District for planning for the Everglades Agricultural Area (EAA) Reservoir were: (1) Cape Sable Seaside Sparrow Marl Prairie Indicator, (2) Florida apple snail (native) population model (EverSnail), (3) Wader Distribution Evaluation Modeling (WADEM), (4) Small-sized freshwater fish density, and (5) American alligator production probability (i.e., habitat suitability index (HSI)). We ran these models using hydrologic conditions (provided by the South Florida Water Management District, see Process Steps section below) for baseline and future conditions for the EAR.

Ecological models facilitate evaluation and assessment of alternative approaches to restore the Greater Everglades ecosystem. The models of particular interest to the South Florida Water Management District for planning for the Everglades Agricultural Area (EAA) Reservoir were: (1) Cape Sable Seaside Sparrow Marl Prairie Indicator, (2) Florida apple snail (native) population model (EverSnail), (3) Wader Distribution Evaluation Modeling (WADEM), (4) Small-sized freshwater fish density, and (5) American alligator production probability (i.e., habitat suitability index (HSI)). We ran these models using hydrologic conditions (provided by the South Florida Water Management District, see Process Steps section below) for baseline and future conditions for the EAR.

KiteNest is a spatially explicit model of Everglades snail kite (Rostrhamus sociabilis plumbeus) relative nest site selection that quantifies the relationships between a range of environmental factors and nest site selection specific to the southern portion of the species' range. Using hydrologic conditions such as mean 2-week water depth and water depth change rate, days since the last fire, distance to the nearest fire within the last year, and percent canopy cover, KiteNest provides biweekly probabilities of relative snail kite nest site selection on a 400 x 400 m grid. Here we provide the scripts to extract and calculate the model inputs, generate the model, validate the model, and produce predictions of snail kite relative nest site selection across the landscape from 1996 through 2017. A NetCDF of these predicted probabilities is also provided for users. For full details of the model development process, see Larger Work citation.