Ignoring species availability biases occupancy estimates in single-scale occupancy models

Most applications of single-scale occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. Species availability can be estimated using multi-scale occupancy models; however, for the practical application of multi-scale occupancy models, it can be unclear what a robust sampling design looks like and what the statistical properties of the multi-scale and single-scale occupancy models are when availability is less than one. Using simulations, we explore the following common questions asked by ecologists during the design phase of a field study: (Q1) what is a robust sampling design for the multi-scale occupancy model when there are a priori expectations of parameter estimates? (Q2) what is a robust sampling design when we have no expectations of parameter estimates? and (Q3) can a single-scale occupancy model with a random effects term adequately absorb the extra heterogeneity produced when availability is less than one and provide reliable estimates of occupancy probability? Our results show that there is a tradeoff between the number of sites and surveys needed to achieve a specified level of acceptable error for occupancy estimates using the multi-scale occupancy model. We also document that when species availability is low (\u3c0.40 on the probability scale), then single-scale occupancy models underestimate occupancy by as much as 0.40 on the probability scale, produce overly precise estimates, and provide poor parameter coverage. This pattern was observed when a random effects term was and was not included in the single-scale occupancy model, suggesting that adding a random-effects term does not adequately absorb the extra heterogeneity produced by the availability process. In contrast, when species availability was high (\u3e0.60), single-scale occupancy models performed similarly to the multi-scale occupancy model. Users can further explore our results and sampling designs across a number of different scenarios using the RShiny app https://gdire nzo.shiny apps.io/multiscale -occ/. Our results suggest that unaccounted for availability can lead to underestimating species distributions when using single-scale occupancy models

Dermal denticle assemblages in coral reef sediments correlate with conventional shark surveys

Abstract It is challenging to assess long‐term trends in mobile, long‐lived and relatively rare species such as sharks. Despite ongoing declines in many coastal shark populations, conventional surveys might be too fleeting and too recent to describe population trends over decades to millennia. Placing recent shark declines into historical context should improve management efforts as well as our understanding of past ecosystem dynamics. A new palaeoecological approach for surveying shark abundance on coral reefs is to quantify dermal denticle assemblages preserved in sediments. This approach assumes that denticle accumulation rates correlate with shark abundances. Here, we test this assumption by comparing the denticle record in surface sediments to three conventional shark survey methods at Palmyra Atoll, Line Islands, central Pacific Ocean, where shark density is high and spatially heterogeneous. We generally found a significant positive correlation between denticle accumulation rates and shark abundances derived from underwater visual census, baited remote underwater video and hook and line surveys. Denticle accumulation rates reflected shark abundances, suggesting that denticle assemblages can preserve a signal of time‐averaged shark abundance in low‐energy coral reef environments. We offer suggestions for applying this tool to measure shark abundance over long time‐scales in other contexts

A practical guide to understanding and validating complex models using data simulations

Abstract Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood‐based approaches (e.g. packages lme4) and machine learning methods. These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution, and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods. Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement. Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices

Fungal infection intensity and zoospore output of Atelopus zeteki, a potential acute chytrid supershedder

Amphibians vary in their response to infection by the amphibian-killing chytrid fungus, Batrachochytrium dendrobatidis (Bd). Highly susceptible species are the first to decline and/or disappear once Bd arrives at a site. These competent hosts likely facilitate Bd proliferation because of ineffective innate and/or acquired immune defenses. We show that Atelopus zeteki, a highly susceptible species that has undergone substantial population declines throughout its range, rapidly and exponentially increases skin Bd infection intensity, achieving intensities that are several orders of magnitude greater than most other species reported. We experimentally infected individuals that were never exposed to Bd (n = 5) or previously exposed to an attenuated Bd strain (JEL427-P39; n = 3). Within seven days post-inoculation, the average Bd infection intensity was 18,213 zoospores (SE: 9,010; range: 0 to 66,928). Both average Bd infection intensity and zoospore output (i.e., the number of zoospores released per minute by an infected individual) increased exponentially until time of death (t50 = 7.018, p,0.001, t46 = 3.164, p = 0.001, respectively). Mean Bd infection intensity and zoospore output at death were 4,334,422 zoospores (SE: 1,236,431) and 23.55 zoospores per minute (SE: 22.78), respectively, with as many as 9,584,158 zoospores on a single individual. The daily percent increases in Bd infection intensity and zoospore output were 35.4 % (SE: 0.05) and 13.1 % (SE: 0.04), respectively. We also found that Bd infection intensity and zoospore output were positively correlated (t43 = 3.926, p,0.001). All animals died between 22 and 33 days post-inoculation (mean: 28.88; SE: 1.58). Prior B

Inferring pathogen presence when sample misclassification and partial observation occur

Abstract Surveillance programmes are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e. false positives and false negatives) or partial detection errors (i.e. detections with ‘ambiguous’, ‘uncertain’ or ‘equivocal’ results). Then, when data are to be analysed, these partial observations are either discarded or censored; this, however, disregards information that could be used to make inference about the true state of the system. There is a critical need for more direction and guidance related to how many samples are enough to declare a unit of interest ‘pathogen free’. Here, we develop a Bayesian hierarchal framework that accommodates false negative, false positive and uncertain detections to improve inference related to the occupancy of a pathogen. We apply our modelling framework to a case study of the fungal pathogen Pseudogymnoascus destructans (Pd) identified in Texas bats at the invasion front of white‐nose syndrome. To improve future surveillance programmes, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site. We found that the presence of uncertain detections increased the variability of resulting posterior probability distributions of pathogen occurrence, and that our estimates of required sample size were very sensitive to prior information about pathogen occupancy, pathogen prevalence and diagnostic test specificity. In the Pd case study, we found that the posterior probability of occupancy was very low in 2018, but occupancy probability approached 1 in 2020, reflecting increasing prior probabilities of occupancy and prevalence elicited from the site manager. Our modelling framework provides the user a posterior probability distribution of pathogen occurrence, which allows for subjective interpretation by the decision‐maker. To help readers apply and use the methods we developed, we provide an interactive RShiny app that generates target species occupancy estimation and sample size estimates to make these methods more accessible to the scientific community (https://rmummah.shinyapps.io/ambigDetect_sampleSize). This modelling framework and sample size guide may be useful for improving inferences from molecular surveillance data about emerging pathogens, non‐native invasive species and endangered species where misclassifications and ambiguous detections occur

First in Vivo Batrachochytrium dendrobatidis Transcriptomes Reveal Mechanisms of Host Exploitation, Host-Specific Gene Expression, and Expressed Genotype Shifts

For generalist pathogens, host species represent distinct selective environments, providing unique challenges for resource acquisition and defense from host immunity, potentially resulting in host-dependent differences in pathogen fitness. Gene expression modulation should be advantageous, responding optimally to a given host and mitigating the costs of generalism. Batrachochytrium dendrobatidis (Bd), a fungal pathogen of amphibians, shows variability in pathogenicity among isolates, and within-strain virulence changes rapidly during serial passages through artificial culture. For the first time, we characterize the transcriptomic profile of Bd in vivo, using laser-capture microdissection. Comparison of Bd transcriptomes (strain JEL423) in culture and in two hosts (Atelopus zeteki and Hylomantis lemur), reveals >2000 differentially expressed genes that likely include key Bd defense and host exploitation mechanisms. Variation in Bd transcriptomes from different amphibian hosts demonstrates shifts in pathogen resource allocation. Furthermore, expressed genotype variant frequencies of Bd populations differ between culture and amphibian skin, and among host species, revealing potential mechanisms underlying rapid changes in virulence and the possibility that amphibian community composition shapes Bd evolutionary trajectories. Our results provide new insights into how changes in gene expression and infecting population genotypes can be key to the success of a generalist fungal pathogen

Batrachochytrium dendrobatidis infection intensity data of amphibians in El Cope, Panama

This file contains Batrachochytrium dendrobatidis (i.e., chytrid) infection intensity data of amphibians in El Cope, Panama across > 30 species from 2012 to 2014 (including wet and dry seasons; and stream and trail habitats). A subset of individuals were swabbed twice in sequence. These double swabs were used to estimate the sensitivity of swabs to detect pathogen infection. Number listed under the column "First_swab" represent the chytrid infection load detected on the first swab, and the column "Second_swab" is the chytrid infection load detected on the second swab

Data from: 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

Relationship between <i>Bd</i> infection intensity and zoospore output.

<p>The solid black line corresponds to the linear regression fitted to all points (<i>t₄₃</i> = 3.926, <i>p</i><0.001). <i>Bd</i> infection intensity and zoospore output were positively correlated and not influenced by prior <i>Bd</i> exposure of the amphibian.</p

Summary of <i>Atelopus zeteki</i> infection intensity (number of zoospores on skin swabs) and zoospore output (number of zoospores released per minute) at death.

<p>Summary of <i>Atelopus zeteki</i> infection intensity (number of zoospores on skin swabs) and zoospore output (number of zoospores released per minute) at death.</p