Chelsea L. Wood

Macroparasites represent a major fraction of biodiversity and biomass in marine ecosystems, yet remain substantially underrepresented in ecological surveys and food-web models, particularly in the Global South. This study addresses this geographic bias by providing the first comprehensive ecological survey of macroparasite communities of fishes in a West African Marine Protected Area (MPA) and adjacent Open-Access Area (OAA) characterised by mangrove forests. We collected 5,020 parasite specimens from 10 fish and one crab species in the Saloum Delta, Senegal, identifying 27 parasite species across all major taxa. Our findings include 15 new records for Senegal of which 11 are also new for West Africa. Additionally, 64% of host-parasite combinations constitute new host records. We provide 208 DNA barcodes, offering the first molecular data for 31% of these species globally and establishing a reference library for future DNA metabarcoding efforts in the region. Using sample size–corrected biodiversity indices and mixed-model analyses, we detected significant variations in parasite infracommunities and component communities between the MPA and OAA, demonstrating the feasibility of using comparative study designs to monitor fisheries impacts on parasite diversity in the Saloum Delta. Furthermore, the identification of trophically transmitted parasites provided evidence of unstudied food-web connections, e.g. predator-prey linkages between fishes and higher-trophic-level definitive hosts including birds and elasmobranchs. By integrating morphological identification, molecular data, and archiving of voucher specimens into publicly accessible, curated collections, this study establishes a new baseline for marine parasite biodiversity in West Africa and provides a methodological blueprint for surveying understudied regions.

Biophysical processes associated with large‐scale nearshore oceanographic upwelling can influence coral reef communities by increasing the delivery of nutrient subsidies and food resources to reefs. However, these processes occur alongside anthropogenic activities that may alter ecological patterns across coral reef ecosystems. We examined relationships among nearshore oceanic primary production, human habitation, and the abundance, biomass, body condition and life‐history traits of eight common coral reef fish species across six islands in the Northern Line Islands, central Pacific Ocean. Variation in oceanic primary production and habitation status was associated with differences in abundance, biomass, body condition and life‐history traits (i.e., growth rates, maximum size and longevity) of key coral reef fishes. These relationships varied among species and islands, with some taxa exhibiting more pronounced relationships with productivity and human habitation than others. Among uninhabited islands, several grazer‐detritivore species exhibited life‐history patterns that were consistent with variation in oceanic primary production, whereas these relationships between productivity and life‐history traits were less apparent among inhabited islands. In contrast, piscivore‐invertivore species generally exhibited stronger differences associated with human habitation status. These findings suggest that local human impacts may influence life‐history traits of reef fishes even when strong gradients in oceanic primary production are present. Although the mechanisms underlying these patterns were not evaluated directly, our results highlight the role of biophysical processes in shaping coral reef fish assemblages and suggest that human activities may alter ecological relationships linking environmental conditions and life‐history traits across tropical reef ecosystems.

Malaria remains a major public health challenge, causing an estimated 600,000 deaths and 250 million infections annually. Most malaria control efforts focus on freshwater mosquito vectors, leaving saltwater‐tolerant vector mosquitoes inhabiting coastal ecosystems like mangrove forests understudied. Historically, the role of mangrove forests as breeding grounds for malaria vectors often motivated their destruction. However, mangroves provide crucial ecosystem services, and their impact on malaria transmission remains poorly understood. This study presents the first multi‐country analysis linking African mangrove forests to prevalence of malaria ( Plasmodium falciparum ). We employed piecewise structural equation models to examine the relationships among mangrove land cover and mangrove vegetation greenness across coastal settlements in 27 African countries. We combined satellite‐derived land cover, vegetation, and weather data with malaria prevalence records from 11 years, ranging from 1996 to 2020. We found two key associations. First, increasing mangrove land cover is associated with decreasing malaria prevalence at coarse spatial resolution, refuting the traditional view of mangroves as disease‐promoting environments. We hypothesise that this relationship may reflect ecological factors such as reduced larval development due to shading, or the presence of predators. Second, at fine and coarse spatial resolutions, increasing mangrove health (i.e., greener vegetation) is associated with increasing malaria prevalence. This trend may be driven by higher mosquito abundance and biodiversity in vegetation‐rich mangrove areas, consistent with studies showing vegetation as a positive predictor of mosquito population density. Our findings constitute continent‐wide patterns derived from observational data, whereas the precise impact of mangrove vegetation likely depends on the geographic location, configuration of the forests, and the identity and competence of host and vector species. We conclude that mangrove conservation needs to be integrated with locally adapted, context‐specific vector management strategies.

Species distribution models (SDMs) are widely used to guide conservation, invasive species management, and disease surveillance, yet biotic predictors are often excluded even when focal species depend on other taxa. Limited co‑occurrence data make joint estimation difficult, and when biotic dependencies are incorporated through multistage approaches, predictor-species’ distributions are typically treated as fixed, error‑free inputs despite substantial uncertainty. Yet, this practice obscures a key source of uncertainty that can influence spatial inference and downstream applications. We present a two-stage SDM framework for dependent species that propagates uncertainty from interacting-species models into predictions for a focal species. Using a simulation-based implementation of the law of total variance, we propagate uncertainty from interacting-species predictors and decompose total predictive uncertainty into model-based (conditional) and predictor-propagated components. We then translate these components into a spatial Survey Priority Index (SPI) to identify locations where uncertainty in interacting species most constrains inference. We apply the framework to two schistosomiasis parasite–host systems in Madagascar that differ in the number and specificity of hosts, showing how uncertainty propagation varies with the structure of biotic dependence. Incorporating host information altered spatial predictions and, in some cases, improved predictive performance, with larger effects in the more tightly coupled single-host system. Variance decomposition revealed that predictor-propagated uncertainty dominated in the single-host system, whereas model-based uncertainty contributed more substantially in the multi-host system. These differences produced distinct SPI patterns, yielding spatially clustered priorities in the single-host system and more diffuse priorities in the multi-host system. Thus, incorporating biotic predictors into SDMs and treating uncertainty as an explicit object of inference improves ecological realism and provides a transparent framework for directing limited surveillance effort. Because the approach applies to any uncertain predictor, it offers a broadly applicable strategy for identifying where missing ecological information constrains inference in interacting-species systems.

As the oceans change, the abundance of parasites and the risk of infection to marine mammals may also be changing. Nematodes in the family Anisakidae can harm marine mammals, and recent studies have revealed a global increase in these parasites, but the cause is unknown. We sought to determine how anisakid risk in Puget Sound had changed over 98 years by conducting a parasitological analysis of museum specimens of the prey species of marine mammals. We dissected Pacific Herring, Walleye Pollock, Surf Smelt, Pacific Hake, and Copper Rockfish collected between 1920 and 2018. We found that the larval anisakid Contracaecum spp. was the most abundant marine mammal parasite in these prey fish. We used a state‐space model to assess the relationship between Contracaecum spp. abundance and time, with harbor seal abundance and sea surface temperature as potential correlates. We detected an overall decline in Contracaecum spp. abundance with a recent uptick starting in 1989, which was correlated with increasing harbor seal abundance. While these data reveal a regional trend, increases in marine mammal parasites in response to marine mammal protection have occurred elsewhere and suggest that the phenomenon might be more widespread than is currently appreciated. Marine mammals in Puget Sound are probably less burdened by anisakids than they were historically, but the recent recovery of anisakids could impact the health of these hosts, which today face very different stressors than they did in the past.

Rapid and comprehensive data sharing is vital to the transparency and actionability of wildlife infectious disease research and surveillance. Unfortunately, most best practices for publicly sharing these data are focused on pathogen determination and genetic sequence data. Other facets of wildlife disease data – particularly negative results – are often withheld or, at best, summarized in a descriptive table with limited metadata. Here, we propose a minimum data and metadata reporting standard for wildlife disease studies. Our data standard identifies a set of 40 data fields (9 required) and 24 metadata fields (7 required) sufficient to standardize and document a dataset consisting of records disaggregated to the finest possible spatial, temporal, and taxonomic scale. We illustrate how this standard is applied to an example study, which documented a novel alphacoronavirus found in bats in Belize. Finally, we outline best practices for how data should be formatted for optimal re-use, and how researchers can navigate potential safety concerns around data sharing.

The complex relationship between temperature and schistosomiasis, an environmentally mediated neglected tropical disease affecting 250 million people globally, with hyperendemicity mostly in Africa, is poorly characterized. Here, we explored how seasonal temperature fluctuation affects the persistence, dynamics, and geographic distribution of schistosomiasis in Africa. We used a temperature-sensitive, mechanistic model of schistosomiasis dynamics that accounts for the adaptive behaviors of intermediate snail hosts and derived the disease’s thermal response curve for different patterns of seasonal temperature fluctuations. Changing the amplitude of seasonal temperature fluctuations can influence both the thermal optimum and critical thermal thresholds which imply accurately drawing the thermal response curves requires accounting for seasonality in addition to mean annual temperature. Moreover, our simulations can reproduce the documented persistence of schistosomiasis at locations with strong seasonal temperature fluctuations and mean annual temperatures near or above the critical thermal maxima for snail hosts only when snail adaptive behavior (e.g., aestivation, movement into cooler depths or shade) is included in the model. These results suggest that future climate change impacting the amplitude and timing of these fluctuations will likely alter the future geographic distribution of schistosomiasis in African regions. Our work demonstrates that a comprehensive understanding of schistosomiasis and, potentially, other environmentally mediated diseases in Africa, necessitates the inclusion of seasonal temperature fluctuations and host behavioral adaptations in process-based mechanistic models.

There are plenty of reasons to believe that parasite populations will respond to biodiversity loss, warming, pollution, and other forms of global change. But will global change enhance transmission, increasing the incidence of troublesome parasites that put people, livestock, and wildlife at risk? Or will parasite species decline in abundance—or even become extinct—suggesting trouble on the horizon for parasite biodiversity? Here, I explain why answers have thus far eluded us and suggest new lines of research that would advance the field. Data collected to date suggest that parasites can respond to global change with increases or decreases in abundance, depending on the driver and the parasite. The future will certainly bring outbreaks of some parasites, and these should be addressed to protect human and ecosystem health. But troublesome parasites should not consume all of our research effort, because this changing world contains many parasite species that are in trouble.

How has parasitism changed for Alaskan salmon over the past several decades? Parasitological assessments of salmon are inconsistent across time, and though parasite data are sometimes noted when processing fillets for the market, those data are not retained for more than a few years. The landscape of parasite risk is changing for salmon, and long‐term data are needed to quantify this change. Parasitic nematodes of the family Anisakidae (anisakids) use salmonid fishes as intermediate or paratenic hosts in life cycles that terminate in marine mammal definitive hosts. Alaskan marine mammals have been protected since the 1970s, and as populations recover, the density of definitive hosts in this region has increased. To assess whether the anisakid burden has changed in salmonids over time, we used a novel data source: salmon that were caught, canned, and thermally processed for human consumption in Alaska, USA. We examined canned fillets of chum (Oncorhynchus keta, n = 42), coho (Oncorhynchus kisutch, n = 22), pink (Oncorhynchus gorbuscha, n = 62), and sockeye salmon (Oncorhynchus nerka, n = 52) processed between 1979 and 2019. We dissected each fillet and quantified the number of worms per gram of salmon tissue. Anisakid burden increased over time in chum and pink salmon, but there was no change in sockeye or coho salmon. This difference may be due to differences in the prey preferences of each species, or to differences in the parasite species detected across hosts. Canned fish serve as a window into the past, providing information that would otherwise be lost, including information on changes over time in the parasite burden of commercially, culturally, and ecologically important fish species.