Geology topics

Ana V. Longo

Publications and source records attributed to Ana V. Longo.

4 recordsLinked to original sources

USGS2025

Similar population dynamics before and after a chytridiomycosis outbreak in a tropical riparian amphibian species

Emerging infectious diseases can cause rapid, widespread host mortality, and the lack of demographic data before and after pathogen emergence complicates understanding mechanisms of host persistence. This challenge is further compounded by environmental conditions that influence host behavior, while driving pathogen growth and virulence. These interactions create complex disease outcomes that hinder predictions of when and how hosts endure pathogen outbreaks. Here, we analyzed 10 years of capture-mark-recapture data (2000–2014) spanning wet and dry seasons for male Espadarana prosoblepon in El Copé, Panama, encompassing a period before (2000–2004) and after (2010–2014) a Batrachochytrium dendrobatidis ( Bd ) outbreak using Jolly-Seber models. We found that post- Bd male E. prosoblepon population size (range in mean population size among primary periods = 136–225 individuals) was similar to pre- Bd population size (range in mean population size among primary periods = 201–242 individuals). Pre- Bd , average monthly survival probability in the wet season was 0.93 (95% credible interval [CI] = 0.90–0.96). Post- Bd , uninfected individuals had survival probability higher in the wet season (mean = 0.97; [95% CI = 0.95–0.98]) than the dry season (mean = 0.90 [95% CI = 0.84–0.94]), while survival probability for infected individuals decreased as a function of Bd infection intensity. Pre- Bd , mean monthly per-capita entry probability was 0.07 (95% CI = 0.05–0.10), and post- Bd , mean monthly per-capita entry probability was 0.06 (95% CI = 0.00–0.10). Lastly, infection probability during the wet season was lower (mean = 0.04 [95% CI = 0.03–0.05]) than the dry season (mean = 0.10 [95% CI = 0.05–0.15]), and recovery probability during the wet season was lower (mean = 0.19 [95% CI = 0.11–0.28]) than the dry season (mean = 0.54 [95% CI = 0.20–0.88]). Our findings suggest that survival probabilities of uninfected individuals, as well as per-capita entry probabilities, are similar pre- and post- Bd , leading to a stable and similar sized pre- Bd population. These results contribute to understanding disease dynamics and tropical amphibian ecology.

Ecosphere↗

USGS2025

Vertebrates in trade that pose high invasion risk to the United States

The United States imports thousands of live vertebrate species annually as part of legal trade. Escapes and releases from captivity are major pathways of invasion, however, the risk posed by the thousands of imported vertebrate species has not been systematically assessed. We conducted a horizon scan that used a data-driven climate match to filter a list of nearly 15,000 taxa drawn from across the globe of imported fish, amphibians, reptiles, birds, and mammals for rapid assessment by taxonomic experts. Experts evaluated 840 species and identified 32 (22 reptiles and 10 fishes) as having the highest risk for establishment, spread, and negative impacts. Of those high-risk species, the majority have the capacity to disrupt ecosystem processes via their role as top predators or the unique ecological niches that they occupy, while several of the snake species pose a threat to human health. High-risk species were often scored with high confidence while in contrast, low scores were attributed to a combination of ecological redundancy, low propagule pressure, or low climate match while low confidence arose from a lack of information in the literature (i.e., data deficiency). Our study therefore highlights legally imported species likely to cause the greatest harm with the recognition that many other species could also become invasive in the United States. The ranked list of vertebrate threats can be used to prioritize watchlists and inform the development of targeted regulations for importation can be applied to regions to provide a rapid, preliminary screening for large pools of potential invaders.

Biological Conservation↗

USGS2018

Eco‐evolutionary rescue promotes host–pathogen coexistence

Emerging infectious pathogens are responsible for some of the most severe host mass mortality events in wild populations. Yet, effective pathogen control strategies are notoriously difficult to identify, in part because quantifying and forecasting pathogen spread and disease dynamics is challenging. Following an outbreak, hosts must cope with the presence of the pathogen, leading to host–pathogen coexistence or extirpation. Despite decades of research, little is known about host–pathogen coexistence post‐outbreak when low host abundances and cryptic species make these interactions difficult to study. Using a novel disease‐structured N‐mixture model, we evaluate empirical support for three host–pathogen coexistence hypotheses (source–sink, eco‐evolutionary rescue, and spatial variation in pathogen transmission) in a Neotropical amphibian community decimated by Batrachochytrium dendrobatidis ( Bd ) in 2004. During 2010–2014, we surveyed amphibians in Parque Nacional G. D. Omar Torríjos Herrera, Coclé Province, El Copé, Panama. We found that the primary driver of host–pathogen coexistence was eco‐evolutionary rescue, as evidenced by similar amphibian survival and recruitment rates between infected and uninfected hosts. Average apparent monthly survival rates of uninfected and infected hosts were both close to 96%, and the expected number of uninfected and infected hosts recruited (via immigration/reproduction) was less than one host per disease state per 20‐m site. The secondary driver of host–pathogen coexistence was spatial variation in pathogen transmission as we found that transmission was highest in areas of low abundance but there was no support for the source–sink hypothesis. Our results indicate that changes in the host community (i.e., through genetic or species composition) can reduce the impacts of emerging infectious disease post‐outbreak. Our disease‐structured N‐mixture model represents a valuable advancement for conservation managers trying to understand underlying host–pathogen interactions and provides new opportunities to study disease dynamics in remnant host populations decimated by virulent pathogens.

Ecological Applications↗

USGS2017

Imperfect pathogen detection from non-invasive skin swabs biases disease inference

1. Conservation managers rely on accurate estimates of disease parameters, such as pathogen prevalence and infection intensity, to assess disease status of a host population. However, these disease metrics may be biased if low-level infection intensities are missed by sampling methods or laboratory diagnostic tests. These false negatives underestimate pathogen prevalence and overestimate mean infection intensity of infected individuals. 2. Our objectives were two-fold. First, we quantified false negative error rates of Batrachochytrium dendrobatidis on non-invasive skin swabs collected from an amphibian community in El Copé, Panama. We swabbed amphibians twice in sequence, and we used a recently developed hierarchical Bayesian estimator to assess disease status of the population. Second, we developed a novel hierarchical Bayesian model to simultaneously account for imperfect pathogen detection from field sampling and laboratory diagnostic testing. We evaluated the performance of the model using simulations and varying sampling design to quantify the magnitude of bias in estimates of pathogen prevalence and infection intensity. 3. We show that Bd detection probability from skin swabs was related to host infection intensity, where Bd infections < 10 zoospores have < 95% probability of being detected. If imperfect Bd detection was not considered, then Bd prevalence was underestimated by as much as 16%. In the Bd-amphibian system, this indicates a need to correct for imperfect pathogen detection caused by skin swabs in persisting host communities with low-level infections. More generally, our results have implications for study designs in other disease systems, particularly those with similar objectives, biology, and sampling decisions. 4. Uncertainty in pathogen detection is an inherent property of most sampling protocols and diagnostic tests, where the magnitude of bias depends on the study system, type of infection, and false negative error rates. Given that it may be difficult to know this information in advance, we advocate that the most cautious approach is to assume all errors are possible and to accommodate them by adjusting sampling designs. The modeling framework presented here improves the accuracy in estimating pathogen prevalence and infection intensity.

Methods in Ecology and Evolution↗