Oliver Muellerklein

Parasite biodiversity faces extinction and redistribution in a changing climate

Parasites face range loss and shifts under climate change, with likely parasite extinction rates of up to one in three species. Climate change is a well-documented driver of both wildlife extinction and disease emergence, but the negative impacts of climate change on parasite diversity are undocumented. We compiled the most comprehensive spatially explicit data set available for parasites, projected range shifts in a changing climate, and estimated extinction rates for eight major parasite clades. On the basis of 53,133 occurrences capturing the geographic ranges of 457 parasite species, conservative model projections suggest that 5 to 10% of these species are committed to extinction by 2070 from climate-driven habitat loss alone. We find no evidence that parasites with zoonotic potential have a significantly higher potential to gain range in a changing climate, but we do find that ectoparasites (especially ticks) fare disproportionately worse than endoparasites. Accounting for host-driven coextinctions, models predict that up to 30% of parasitic worms are committed to extinction, driven by a combination of direct and indirect pressures. Despite high local extinction rates, parasite richness could still increase by an order of magnitude in some places, because species successfully tracking climate change invade temperate ecosystems and replace native species with unpredictable ecological consequences.

Carnivore carcasses are avoided by carnivores

Ecologists have traditionally focused on herbivore carcasses as study models in scavenging research. However, some observations of scavengers avoiding feeding on carnivore carrion suggest that different types of carrion may lead to differential pressures. Untested assumptions about carrion produced at different trophic levels could therefore lead ecologists to overlook important evolutionary processes and their ecological consequences. Our general goal was to investigate the use of mammalian carnivore carrion by vertebrate scavengers. In particular, we aimed to test the hypothesis that carnivore carcasses are avoided by other carnivores, especially at the intraspecific level, most likely to reduce exposure to parasitism. We take a three-pronged approach to study this principle by: (i) providing data from field experiments, (ii) carrying out evolutionary simulations of carnivore scavenging strategies under risks of parasitic infection, and (iii) conducting a literature-review to test two predictions regarding parasite life-history strategies. First, our field experiments showed that the mean number of species observed feeding at carcasses and the percentage of consumed carrion biomass were substantially higher at herbivore carcasses than at carnivore carcasses. This occurred even though the number of scavenger species visiting carcasses and the time needed by scavengers to detect carcasses were similar between both types of carcasses. In addition, we did not observe cannibalism. Second, our evolutionary simulations demonstrated that a risk of parasite transmission leads to the evolution of scavengers with generally low cannibalistic tendencies, and that the emergence of cannibalism-avoidance behaviour depends strongly on assumptions about parasite-based mortality rates. Third, our literature review indicated that parasite species potentially able to follow a carnivore-carnivore indirect cycle, as well as those transmitted via meat consumption, are rare in our study system. Our findings support the existence of a novel coevolutionary relation between carnivores and their parasites, and suggest that carnivore and herbivore carcasses play very different roles in food webs and ecosystems.

A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges

Abstract To solve current environmental challenges such as biodiversity loss, climate change, and rapid conversion of natural areas due to urbanization and agricultural expansion, researchers are increasingly leveraging large, multi-scale, multi-temporal, and multi-dimensional geospatial data. In response, a rapidly expanding array of collaborative geospatial tools is being developed to help collaborators share data, code, and results. Successful navigation of these tools requires users to understand their strengths, synergies, and weaknesses. In this paper, we identify the key components of a collaborative Spatial Data Science workflow to develop a framework for evaluating the various functional aspects of collaborative geospatial tools. Using this framework, we then score thirty-one existing collaborative geospatial tools and apply a cluster analysis to create a typology of these tools. We present this typology as a map of the emergent ecosystem and functional niches of collaborative geospatial tools. We identify three primary clusters of tools composed of eight secondary clusters across which divergence is driven by required infrastructure and user involvement. Overall, our results highlight how environmental collaborations have benefitted from the use of these tools and propose key areas of future tool development for continued support of collaborative geospatial efforts.

Fear‐based niche shifts in neotropical birds

Predation is a strong ecological force that shapes animal communities through natural selection. Recent studies have shown the cascading effects of predation risk on ecosystems through changes in prey behavior. Minimizing predation risk may explain why multiple prey species associate together in space and time. For example, mixed-species flocks that have been widely documented from forest systems, often include birds that eavesdrop on sentinel species (alarm calling heterospecifics). Sentinel species may be pivotal in (1) allowing flocking species to forage in open areas within forests that otherwise incur high predation risk, and (2) influencing flock occurrence (the amount of time species spend with a flock). To test this, we conducted a short-term removal experiment in an Amazonian lowland rainforest to test whether flock habitat use and flock occurrence was influenced by sentinel presence. Antshrikes (genus Thamnomanes) act as sentinels in Amazonian mixed-species flocks by providing alarm calls widely used by other flock members. The alarm calls provide threat information about ambush predators such as hawks and falcons which attack in flight. We quantified home range behavior, the forest vegetation profile used by flocks, and the proportion occurrence of other flocking species, both before and after removal of antshrikes from flocks. We found that when sentinel species were removed, (1) flock members shifted habitat use to lower risk habitats with greater vegetation cover, and (2) species flock occurrence decreased. We conclude that eavesdropping on sentinel species may allow other species to expand their realized niche by allowing them to safely forage in high-risk habitats within the forest. In allowing species to use extended parts of the forest, sentinel species may influence overall biodiversity across a diverse landscape.

A comparison of visual features used by humans and machines to classify wildlife

In our quest to develop more intelligent machines, knowledge of the visual features used by machines to classify objects shall be helpful. The current state of the art in training machines to classify wildlife species from camera-trap data is to employ convolutional neural networks (CNN) encoded within deep learning algorithms. Here we report on results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We then used a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that are in most, but not all, cases similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species, and we then interpret the features in the image where the strongest responses occur. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated how images that were not part of the training set fell within our dendrogram when these images were one of the 20 species “known” to our CNN in contrast to where they fell when these images were “unknown” to our CNN.

Modeling epidemics: A primer and Numerus Model Builder implementation

Epidemiological models are dominated by compartmental models, of which SIR formulations are the most commonly used. These formulations can be continuous or discrete (in either the state-variable values or time), deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SIR dynamical systems models, and we outline how they can be easily and rapidly constructed using Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using NMB network and mapping tools.

The Parasite Extinction Assessment & Red List: an open-source, online biodiversity database for neglected symbionts

Parasite conservation is a rapidly growing field at the intersection of ecology, epidemiology, parasitology, and public health. The overwhelming diversity of parasitic life on earth, and recent work showing that parasites and other symbionts face severe extinction risk, necessitates infrastructure for parasite conservation assessments. Here, we describe the release of the Parasite Extinction Assessment & Red List (PEARL) version 1.0, an open-access database of conservation assessments and distributional data for almost 500 macroparasitic invertebrates. The current approach to vulnerability assessment is based on range shifts and loss from climate change, and will be expanded as additional data (e.g., host-parasite associations and coextinction risk) is consolidated in PEARL. The web architecture is also open-source, scalable, and extensible, making PEARL a template for more eZcient red listing for other high-diversity, data-de1cient groups. Future iterations will also include new functionality, including a user-friendly open data pository and automated assessment and re-listing.

Modeling Epidemics: A Primer and Numerus Software Implementation

Epidemiological models are dominated by SEIR (Susceptible, Exposed, Infected and Removed) dynamical systems formulations and their elaborations. These formulations can be continuous or discrete, deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SEIR dynamical systems models, and we outline how they can be easily and rapidly constructed using the Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using both the Numerus Model Builder network and geographical mapping tools.