Biology subjects

Joseph, M. B.

Publications and source records attributed to Joseph, M. B..

11 recordsLinked to original sources

bioRxiv2023

Evolutionary rescue and reintroduction of resistant frogs allows recovery in the presence of a lethal fungal disease

Vast alteration of the biosphere by humans is causing a sixth mass extinction, driven in part by an increase in emerging infectious diseases. The emergence of the lethal fungal pathogen (Batrachochytrium dendrobatidis; "Bd") has devastated global amphibian biodiversity, with hundreds of species experiencing declines or extinctions. With no broadly applicable methods available to reverse these impacts in the wild, the future of many amphibians appears grim. The once-common mountain yellow-legged (MYL) frog is emblematic of amphibians threatened by Bd. Although most MYL frog populations are extirpated following disease outbreaks, some persist and eventually recover. Frogs in these recovering populations have increased resistance against Bd infection, consistent with evolution of resistant genotypes and/or acquired immunity. We conducted a 15-year landscape-scale reintroduction study and show that frogs collected from recovering populations and reintroduced to vacant habitats can reestablish populations despite the presence of Bd. In addition, results from viability modeling suggest that many reintroduced populations have a low probability of extinction over 50 years. To better understand the role of evolution in frog resistance, we compared the genomes of MYL frogs from Bd-naive and recovering populations. We found substantial differences between these categories, including changes in immune function loci that may confer increased resistance, consistent with evolutionary changes in response to Bd exposure. These results provide a rare example of how reintroduction of resistant individuals can allow the landscape-scale recovery of disease-impacted species. This example has broad implications for the many taxa worldwide that are threatened with extinction by novel pathogens. Significance StatementUnderstanding how species persist despite accelerating global change is critical for the conservation of biodiversity. Emerging infectious diseases can have particularly devastating impacts, and few options exist to reverse these effects. We used large-scale reintroductions of disease-resistant individuals in an effort to recover a once-common frog species driven to near-extinction by a disease that has decimated amphibian biodiversity. Introduction of resistant frogs allowed reestablishment of viable populations in the presence of disease. In addition, resistance may be at least partially the result of natural selection at specific immune function genes, which show evidence for selection in recovering populations. The evolution of resistance and reintroduction of resistant individuals could play an important role in biodiversity conservation in our rapidly changing world.

ecology↗

bioRxiv2021

Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data

Delineating and classifying individual trees in remote sensing data is challenging. Many tree crown delineation methods have difficulty in closed-canopy forests and do not leverage multiple datasets. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for delineation of individual crowns and classification to determine species identity. This competition included data from multiple sites to assess the methods ability to generalize learning across multiple sites simultaneously, and transfer learning to novel sites where the methods were not trained. Six teams, representing 4 countries and 9 individual participants, submitted predictions. Methods from a previous competition were also applied and used as the baseline to understand whether the methods are changing and improving over time. The best delineation method was based on an instance segmentation pipeline, closely followed by a Faster R-CNN pipeline, both of which outperformed the baseline method. However, the baseline (based on a growing region algorithm) still performed well as did the Faster R-CNN. All delineation methods generalized well and transferred to novel forests effectively. The best species classification method was based on a two-stage fully connected neural network, which significantly outperformed the baseline (a random forest and Gradient boosting ensemble). The classification methods generalized well, with all teams training their models using multiple sites simultaneously, but the predictions from these trained models generally failed to transfer effectively to a novel site. Classification performance was strongly influenced by the number of field-based species IDs available for training the models, with most methods predicting common species well at the training sites. Classification errors (i.e., species misidentification) were most common between similar species in the same genus and different species that occur in the same habitat. The best methods handled class imbalance well and learned unique spectral features even with limited data. Most methods performed better than baseline in detecting new (untrained) species, especially in the site with no training data. Our experience further shows that data science competitions are useful for comparing different methods through the use of a standardized dataset and set of evaluation criteria, which highlights promising approaches and common challenges, and therefore advances the ecological and remote sensing field as a whole.

ecology↗

bioRxiv2021

Effectiveness of antifungal treatments during chytridiomycosis epizootics in populations of an endangered frog

The recently-emerged amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd) has had an unprecedented impact on global amphibian populations, and highlights the urgent need to develop effective mitigation strategies against this pathogen. We conducted field antifungal treatment experiments in populations of the endangered mountain yellow-legged frog during or immediately after Bd-caused mass die-off events. The objective of the treatments was to reduce Bd infection intensity ("load") and in doing so alter frog-Bd dynamics and increase the probability of frog population persistence despite ongoing Bd infection. Experiments included treatment of early life stages (tadpoles and subadults) with the antifungal drug itraconazole, treatment of adults with itraconazole, and augmentation of the skin microbiome of subadults with Janthinobacterium lividum, a commensal bacterium with antifungal properties. All itraconazole treatments caused immediate reductions in Bd load, and produced longer-term effects that differed between life stages. In experiments focused on early life stages, Bd load was reduced in the two months immediately following treatment and was associated with increased survival of subadults. However, Bd load and frog survival returned to pre-treatment levels in less than one year, and treatment had no effect on population persistence. In adults, treatment reduced Bd load and increased frog survival over the three-year post-treatment period, consistent with frogs having developed an effective adaptive immune response against Bd. Despite this protracted period of reduced impacts of Bd on adults, recruitment of new individuals into the adult population was limited and the population eventually declined to near-extirpation. In the microbiome augmentation experiment, bathing frogs in a J. lividum solution after Bd load reduction with itraconazole increased concentrations of this bacterium on frogs, but concentrations declined to baseline levels within one month and did not have a protective effect against Bd infection. Collectively, these results suggest that Bd mitigation efforts focused on frog populations that have recently declined due to Bd emergence are ineffective in causing long-term changes in frog-Bd dynamics and increasing population persistence, due largely to the inability of early life stages to mount an effective immune response against Bd and resulting high susceptibility. This results in repeated recruitment failure and a low probability of population persistence in the face of ongoing Bd infection.

ecology↗

bioRxiv2021

Data fusion for abundance estimation: community science augments systematically collected removal-in-time distance sampling data

Ecologists use a variety of systematically and opportunistically sampled count data to estimate bird abundance, and integrating or fusing different datasets has emerged as a critical challenge in recent years. While previous work provides data integration methodology for occupancy (presence/absence) estimation, methods for abundance estimation that account for imperfect detection and disparate survey protocols remains an active area of research. Here we show how to integrate systematically collected removal-in-time distance sampling data from the Integrated Monitoring in Bird Conservation Regions (IMBCR) program with North American Breeding Bird Survey (BBS) point counts and eBird community science observations. Using the Grasshopper Sparrow (Ammodramus savannarum) in the Great Plains of the United States as a focal species, we demonstrate that BBS and eBird data improve predictive performance for IMBCR count data, providing more spatially refined and precise estimates of abundance at regional scales. Data fusion increased predictive performance even despite relatively weak spatial correlations among data sets. The methodology developed here provides a principled way to fuse data when estimating abundance with distance sampling, that accounts for imperfect detection and variable effort.

ecology↗

bioRxiv2021

Estimating occupancy dynamics and encounter rates with species misclassification: a semi-supervised individual-level approach

O_LILarge-scale, long-term biodiversity monitoring is essential to meeting conservation and land management goals and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is encountered but its species identity is not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including "unknown", morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic rates, occurrence, and species richness. C_LIO_LIHere we develop an occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain morphospecies designations in the imperfect species classifications, and makes use of individual specimen with known species identities in a semi-supervised setting. We compare the performance of our joint classification-occupancy model to a reduced classification model that discards information about occupancy and encounter rate on a withheld test set. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, west of Boulder, CO, USA, and quantify taxonomist identification error by accounting for classification probabilities. C_LIO_LISpecies occupancy varied through time and across sites and species. The model yielded high probabilities (30 to 92% medians) of classification where the imperfect classifier matched the true species. The classification model informed by occupancy and encounter rates outperformed the classification that was not, and these differences were most pronounced for abundant species. C_LIO_LIOur probabilistic framework can be applied to datasets with imperfect species detection and classification. This model can identify commonly misclassified species, helping biodiversity monitoring organizations systematically prioritize which samples need validation by an expert. Our Bayesian approach propagates classification uncertainty to offer an alternative to making conservation decisions based on point estimates C_LI

ecology↗

bioRxiv2020

Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem

Vessel-based sonar systems that focus on the water column provide valuable information on the distribution of underwater marine organisms, but such data are expensive to collect and limited in their spatiotemporal coverage. Satellite data, however, are widely available across large regions and provide information on surface ocean conditions. If satellite data can be linked to subsurface sonar measurements, it may be possible to predict marine life over broader spatial regions with higher frequency using satellite observations. Here, we use random forest models to evaluate the potential for predicting a sonar-derived proxy for subsurface biomass as a function of satellite imagery in the California Current Ecosystem. We find that satellite data may be useful for prediction under some circumstances, but across a range of sonar frequencies and depths, overall predictive performance was low. Performance in spatial interpolation tasks exceeded performance in spatial and temporal extrapolation, suggesting that this approach is not yet reliable for forecasting or spatial extrapolation. We conclude with some potential limitations and extensions of this work.

ecology↗

bioRxiv2020

Using encounter data to improve capture-recapture abundance estimates

Capture-recapture studies are widely used in ecology to estimate population sizes and demographic rates. In some capture-recapture studies, individuals may be visually encountered but not identified. For example, if individual identification is only possible upon capture and individuals escape capture, visual encounters can result in failed captures where individual identities are unknown. In such cases, the data consist of capture histories with known individual identities, and counts of failed captures for individuals with unknown identities. These failed captures are ignored in traditional capture-recapture analyses that require known individual identities. Here we show that if animals can be encountered at most once per sampling occasion, failed captures provide lower bounds on population size that can increase the precision of abundance estimates. Analytical results and simulations indicate that visual encounter data improve abundance estimates when capture probabilities are low, and when there are few repeat surveys. We present a hierarchical Bayesian approach for integrating failed captures and auxiliary encounter data in statistical capture-recapture models. This approach can be integrated with existing capture-recapture models, and may prove particularly useful for hard to capture species in data-limited settings.

ecology↗

bioRxiv2019

Neural hierarchical models of ecological populations

Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This paper describes a class of hierarchical models parameterized by neural networks: neural hierarchical models. The derivation of such models analogizes the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non-detection time series for hundreds of species, providing insights into colonization and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modeling that combines the function representation power of neural networks with the inferential capacity of hierarchical models.

ecology↗

bioRxiv2018

Untangling the dynamics of persistence and colonization in microbial communities

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here we introduce a dynamic statistical model that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We apply our modeling approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

ecology↗

bioRxiv2018

Understanding and predicting extreme wildfires in the contiguous United States

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30 year wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.

ecology↗

bioRxiv2018

Disease and climate effects on individuals jointly drive post-reintroduction population dynamics of an endangered amphibian

The emergence of novel pathogens often has dramatic negative effects on previously unexposed host populations. Subsequent disease can drive populations and even species to extinction. After establishment in populations, pathogens can continue to affect host dynamics, influencing the success or failure of species recovery efforts. However, quantifying the effect of pathogens on host populations in the wild is challenging because individual hosts and their pathogens are difficult to observe. Here we use long-term mark-recapture data to describe the dynamics of reintroduced populations of an endangered amphibian (Rana sierrae) and evaluate the success of these recovery efforts in the presence of a recently-emerged pathogen, the amphibian chytrid fungus Batrachochytrium dendrobatidis. We find that high B. dendrobatidis infection intensities are associated with increases in detectability, reductions in survival, and more infected adults. We also find evidence for intensity-dependent survival, with heavily infected individuals suffering higher mortality. These results highlight the need in disease ecology for probabilistic approaches that account for uncertainty in infection intensity using imperfect observational data. Such approaches can advance the understanding of disease impacts on host population dynamics, and in the current study will improve the effectiveness of species conservation actions.

ecology↗