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bioRxiv2020

Ecology of domestic dogs Canis familiaris as an emerging reservoir of Guinea worm Dracunculus medinensis infection

Global eradication of human Guinea worm disease (dracunculiasis) has been set back by the emergence of infections in animals, particularly domestic dogs Canis familiaris. The ecology and epidemiology of this reservoir is unknown. We tracked dogs using GPS, inferred diets using stable isotope analysis and analysed correlates of infection in Chad, where numbers of Guinea worm infections are greatest. Dogs had small ranges that varied markedly among villages. Diets consisted largely of human staples and human faeces. A minority of ponds, mostly <200 m from dog-owning households, accounted for most dog exposure to potentially unsafe water. The risk of a dog having had Guinea worm was reduced in dogs living in households providing water for animals but increased with increasing fish consumption by dogs. Provision of safe water might reduce dog exposure to unsafe water, while prioritisation of proactive temephos (Abate) application to the small number of ponds to which dogs have most access is recommended. Fish might have an additional role as transport hosts for Guinea worm, by concentrating copepods infected with worm larvae. Author summaryGuinea worm is a parasite that causes profoundly debilitating disease in humans. An eradication program has been successful in nearly eliminating the disease from people. However, the same worm has now been found in domestic dogs and the frequency of detecting Guinea worm in dogs has been increasing. This means that to eradicate Guinea worm, the infection must be eliminated in dogs as well as in people. However, not much is known about the disease in dogs. This study is the first to investigate dog ecology in relation to Guinea worm infection. We worked in the worst-affected country, Chad. We attached GPS collars to dogs to track their ranging and use of water bodies and analysed their diets using a forensic technique, based on analysing stable isotope compostion of their whiskers and potential food items. We showed that dogs living in households that provided water to their animals had a lower risk of having had Guinea worm and that dogs that ate more fish had an increased risk. These findings suggest there is a classical route for worm transmission in dogs, via drinking contaminated water, as well as a novel route, potentially by eating fish carrying a source of infection.

ecology↗

bioRxiv2020

A quantitative framework reveals the ecological drivers of grassland soil microbial community assembly in response to warming

Unraveling the drivers controlling community assembly is a central issue in ecology. Selection, dispersal, diversification and drift are conceptually accepted as major community assembly processes. Defining their relative importance in governing biodiversity is compellingly needed, but very challenging. Here, we present a novel framework to quantitatively infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP). Our results with simulated microbial communities showed that iCAMP had high accuracy (0.93 - 0.99), precision (0.80 - 0.94), sensitivity (0.82 - 0.94), and specificity (0.95 - 0.98), which were 10-160% higher than those from the entire community-based approach. Applying it to grassland microbial communities in response to experimental warming, our analysis showed that homogeneous selection (38%) and "drift" (59%) played dominant roles in controlling grassland soil microbial community assembly. Interestingly, warming enhanced homogeneous selection, but decreased "drift" over time. Warming-enhanced selection was primarily imposed on Bacillales in Firmicutes, which were strengthened by increased drought and reduced plant productivity. This general framework should also be useful for plant and animal ecology.

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bioRxiv2020

The impact of colonization history on the composition of ecological systems

Observational studies of ecological systems have shown that different species compositions can arise from distinct species arrival orders during community assembly--also known as colonization history. However, it is still unclear under which conditions colonization history will dominate community composition. Yet, this is important in order to understand and anticipate the impact of species arrivals on the biodiversity that we observe in both nature and experiments. To address this fundamental question, here we develop a testable theory linking colonization history and community composition. First, we prove two general theorems to determine whether the composition of a community will depend on its colonization history. For communities governed by Lotka-Volterra dynamics, we further simplify the two theorems into a corollary that is easy for numerical test. Second, we show, via extensive numerical simulations, that the probability that community composition is dominated by colonization history increases monotonically with community size and species connectivity. Third, we show that this probability significantly increases with the variation of intrinsic growth rates across species. These results reveal that community composition is a probabilistic process mediated by ecological dynamics via the interspecific variation and the size of regional pools.

ecology↗

bioRxiv2020

On the problems of not accounting for measurement- and sampling uncertainty in ecological and environmental models

In many applied cases of ecological and environmental modelling, there is a sizeable variation among the measured variables due to measurement- and sampling error. Such measurement- and sampling error among the independent variables may lead to regression dilution and biased prediction intervals in traditional empirical modelling. It is possible to avoid these shortcomings by subsampling and hierarchical modelling, but this is still not a common practice. Here, it is recommended to model the measurement- and sampling errors by integrating the stochastic modelling of the errors into hierarchical ecological and environmental models.

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bioRxiv2020

Effectiveness of active learning for ecology teaching: the perspective of students vs their grades

We evaluated the effectiveness of active learning for ecology teaching by comparing the perspective of students to their grades in exams. We estimated the perspective of the students through anonymous survey; we used students exam grades to estimate their ecology learning, and their effort and performance in the active learning tasks through their grades and proportion of intermediate steps for each active learning task. Active learning involved teachers stimuli for students active involvement, extra-class group task, individual online writing assessments, redoing exam in pairs, and classroom writing group assessments. We also evaluated the impact, unto the effectiveness of active learning, of several student characteristics, such as sex, age, individual study effort, and previous basic knowledge. We found that self-evaluated learning increased linearly with teachers attempts to stimulate students active involvement (P = 0.0003), extra-class group task (P = 0.0003), and previous basic knowledge (P = 0.02), while students grades increased asymptotically with extra-class group task (P < 2-16), and increased linearly with online writing assessments (P = 9.3-8) and classroom-based writing group assessments (P = 0.03). Our results showed that students perceive most part of the effectiveness of active-learning tasks and of teachers efforts. We showed that active learning tasks are complementary, so we recommend that teachers in both college and high school should implement simultaneous active-learning tasks, that include extra-class work in group, individual and group writing assessments, and should stimulate students engagement through respectful and non-authoritarian behavior of the teacher. Our results also showed that previous basic knowledge also plays a central role in driving effective learning, evidencing the importance of students instruction outside college. The applied teaching methodology is cheap and feasible for large classes. In these times of rising intolerance, prejudice, dismiss of environmental issues and disregard of science itself, we need an effective, pluralistic, respectful, and student-centred education, that fosters critical thinking, tolerance and respect for differing points of view.

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bioRxiv2020

Ecological and anthropogenic drivers of large carnivore depredation on sheep in Europe

O_LISharing space with large carnivores on a human-dominated continent like Europe results in multiple conflictful interactions with human interests, of which depredation on livestock is the most widespread. Wildlife management agencies maintain compensation programs for the damage caused by large carnivores, but the long-term effectiveness of such programs is often contested. Therefore, understanding the mechanisms driving large carnivore impact on human activities is necessary to identify key management actions to reduce it. C_LIO_LIWe conducted an analysis of the impact by all four European large carnivores on sheep husbandry in 10 European countries, during the period 2010-2015. We ran a hierarchical Simultaneous Autoregressive model, to assess the influence of ecological and anthropogenic factors on the spatial and temporal patterns in the reported depredation levels across the continent. C_LIO_LIOn average, about 35,000 sheep were compensated in the ten countries as killed by large carnivores annually, representing about 0.5% of the total sheep stock. Of them, 45% were recognized as killed by wolves, 24% by wolverines, 19% by lynx and 12% by bears. At the continental level, we found a positive relationship between wolf distribution and the number of compensated sheep, but not for the other three species. Impact levels were lower in the areas where large carnivore presence has been continuous compared to areas where they disappeared and recently returned. The model explained 62% of the variation in the number of compensated sheep per year in each administrative unit. Only 13% of the variation was related to the ecological components of the process. C_LIO_LISynthesis and Applications: Large carnivore distribution and local abundance alone are poor predictors of large carnivore impact on livestock at the continental level. A few individuals can produce high damage, when the contribution of environmental, social and economic systems predisposes for it, whereas large populations can produce a limited impact when the same components of the system reduce the probability that depredations occur. Time seems to play in favour of a progressive reduction in the costs associated with coexistence, provided that the responsible agencies focus their attention both on compensation and co-adaptation. C_LI

ecology↗

bioRxiv2020

Socio-Ecological Network Structures from Process Graphs

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis (ENA) techniques. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ENA. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ENA. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results. Author summaryIn this study, we propose the novel application of the process graph (P-graph) methodology to the analysis of ecological networks. P-graph was originally developed for engineering design problems; in our work, we show how its five axioms and two algorithms - maximal structure generation (MSG) and solution structure generation (SSG) can be adapted to the problem of understanding complex interactions in ecosystems. The methodology allows multiple types of interactions among ecosystem components to be handled simultaneously based on representation as a bipartite graph. Complete network structures can be deduced from knowledge of local interactions of components using MSG. Finally, all structurally feasible networks of viable ecosystems can be identified with SSG. We illustrate the features of the P-graph methodology with a stylized illustrative example.

ecology↗

bioRxiv2020

Antibiotics in microbial communities: an ecological frame of resistance

There is growing awareness that our ability to tackle antibiotic resistance is limited by a lack of mechanistic understanding of the communities in which resistant microbes are embedded. The widespread coexistence of resistant and sensitive bacteria in microbial systems presents an especially frustrating paradox. Recent advances in ecological coexistence theory offer a powerful framework to probe the mechanisms regulating intra- and inter-specific coexistence, but the significance of this body of theory to the problem of antimicrobial resistance has been largely overlooked. In this Perspectives article, we draw on emerging ecological theory to illustrate how changes in both competitive ability and niche overlap are critical for understanding costs of resistance and the persistence of pathogens in microbial systems. We then show how trade-offs in resource acquisition strategies can have counter-intuitive consequences for the coexistence of resistant and susceptible genotypes in variable environments. These insights highlight numerous opportunities for innovative experimental and theoretical research into antibiotic resistance in the microbiome.

ecology↗

bioRxiv2020

Intrinsic ecological dynamics drive biodiversity turnover in model metacommunities

Turnover of species composition through time is frequently observed in ecosystems. It is often interpreted as indicating the impact of changes in the environment. Continuous turnover due solely to ecological dynamics--species interactions and dispersal--is also known to be theoretically possible, however the prevalence of such autonomous turnover in natural communities remains unclear. Here we demonstrate that observed patterns of compositional turnover and other important macroecological phenomena can be reproduced in large spatially explicit model ecosystems, without external forcing such as environmental change or the invasion of new species into the model. These results imply that the potential role of autonomous turnover as a widespread and important natural process is underappreciated, challenging assumptions implicit in many observation and management tools. Quantifying the baseline level of compositional change would greatly improve ecological status assessments. One Sentence SummaryBiodiversity change previously attributed to external drivers is explainable as resulting from intrinsic ecosystem dynamics.

ecology↗

bioRxiv2020

The rise and fall of novel ecological communities

Local and global environmental change is transforming ecological assemblages into new configurations, resulting in ecosystems with novel communities. Here we develop a robust methodology for the identification of novel communities, examine patterns in their natural chance of occurrence, and quantify the probability of local extinction, emigration, local origination and immigration in transitions to and from novel communities. Using a global dataset of Cenozoic marine plankton communities, we found the probability of local extinction, origination and emigration during transitions to a novel community increased up to four times that of background community changes, with the probability of species loss about equal to that of species gain. Although rare, once a novel community state emerged, the chance of shifting into another novel community state was five times greater than expected by chance. Thus, for marine planktonic communities at 100K year time scales, novel communities are particularly sensitive to further extinctions and community shift. One Sentence SummaryOnce developed, novel ecological communities face increased susceptibility to further shifts in species composition, with heightened extinction risk.

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bioRxiv2020

Food web structure alters ecological communities top-heaviness with little effect on the biodiversity-functioning relationship.

In a rapidly changing world, the composition, diversity and structure of ecological communities face many threats. Biodiversity-Ecosystem Functioning (BEF) and community food-chain analyses have focused on investigating the consequences of these changes on ecosystem processes and the resulting functions. These different and diverging conceptual frameworks have each produced important results and identified a set of important mechanisms, that shape ecosystem functions. But the disconnection between these frameworks, and the various simplifications of the study systems are not representative of the complexity of real-world communities. Here we use food webs as a more realistic depiction of communities, and use a bioenergetic model to simulate their biomass dynamics and quantify the resulting flows and stocks of biomass. We use tools from food web analysis to investigate how the predictions from BEF and food-chain analyses fit together, how they correlate to food-web structure and how it might help us understand the interplay between various drivers of ecosystem functioning. We show that food web structure is correlated to the communitys efficiency in storing the captured biomass, which may explain the distribution of biomass (top heaviness) across the different trophic compartments (producers, primary and secondary consumers). While we know that ecological network structure is important in shaping ecosystem dynamics, identifying structural attributes important in shaping ecosystem processes and synthesizing how it affects various underpinning mechanisms may help prioritize key conservation targets to protect not only biodiversity but also its structure and the resulting services.

ecology↗

bioRxiv2020

The impact of microclimate and soil on the ecology and evolution of an arctic plant

The arctic and alpine regions are predicted to experience one of the highest rates of climate change, and the arctic vegetation is expected to be especially sensitive to such changes. Understanding the ecological and evolutionary responses of arctic plant species to changes in climate is therefore a key objective. Geothermal areas, where temperature gradients naturally occur over small spatial scales, and without many of the confounding environmental factors present in latitudinal and other gradient studies, provide a natural experimental setting to examine the impact of temperature on the response of arctic-alpine plants to increasing temperatures. To test the ecological and evolutionary response of the circumpolar alpine bistort (Bistorta vivipara) to temperature, we collected plant material and soil from areas with low, intermediate, and high soil temperatures and grew them in all combinations at three different temperatures. At higher experimental soil temperatures, sprouting was earlier, and plants had more leaves. Sprouting was earlier in soil originating from intermediate temperature and plants had more leaves when grown in soil originating from low temperatures. We did not find evidence of local adaptation or genetic variation in reaction norms among plants originating from areas with low, intermediate, and high soil temperature. Our findings suggest that the alpine bistort has a strong plastic response to warming, but that differences in soil temperature have not resulted in genetic differentiation. The lack of an observed evolutionary response may, for example, be due to the absence of temperature-mediated selection on B. vivipara, or high levels of gene flow balancing differences in selection. When placed within the context of other studies, we conclude that arctic-alpine plant species often show strong plastic responses to spring warming, while evidence of evolutionary responses varies among species.

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bioRxiv2020

High performance machine learning models can fully automate labeling of camera trap images for ecological analyses

O_LIEcological data are increasingly collected over vast geographic areas using arrays of digital sensors. Camera trap arrays have become the gold standard method for surveying many terrestrial mammals and birds, but these arrays often generate millions of images that are challenging to process. This causes significant latency between data collection and subsequent inference, which can impede conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve camera trap data processing speeds, but these models are not considered accurate enough for fully automated labeling of images. C_LIO_LIHere, we present a new approach to building and testing a high performance machine learning model for fully automated labeling of camera trap images. As a case-study, the model classifies 26 Central African forest mammal and bird species (or groups). The model was trained on a relatively small dataset (c.300,000 images) but generalizes to fully independent data and outperforms humans in several respects (e.g. detecting invisible animals). We show how the models precision and accuracy can be evaluated in an ecological modeling context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. C_LIO_LIResults show that fully automated labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in completely out-of-sample test data (n = 227 camera stations, n = 23868 images). Simple thresholding (discarding uncertain labels) improved the models performance when calculating activity patterns and estimating occupancy, but did not improve estimates of species richness. C_LIO_LIWe provide the user-community with a multi-platform, multi-language user interface for running the model offline, and conclude that high performance machine learning models can fully automate labeling of camera trap data. C_LI

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bioRxiv2020

Long-term assessment of ecosystem services at ecological restoration sites using Landsat time series

Reversing ecological degradation through restoration activities is a key societal challenge of the upcoming decade. However, lack of evidence on the effectiveness of restoration interventions leads to inconsistent, delayed, or poorly informed statements of success, hindering the wise allocation of resources, representing a missed opportunity to learn from previous experiences. This study contributes to a better understanding of spatial and temporal dynamics of ecosystem services at ecological restoration sites. We developed a method using Landsat satellite images combined with a Before-After-Control-Impact (BACI) design, and applied this to an arid rural landscape, the Baviaanskloof in South Africa. Since 1990, various restoration projects have been implemented to halt and reverse degradation. We applied the BACI approach at pixel-level comparing the conditions of each intervened pixel (impact) with 20 similar control pixels. By evaluating the conditions before and after the intervention, we assessed the effectiveness of long-term restoration interventions distinguishing their impact from environmental temporal changes. The BACI approach was implemented with Landsat images that cover a 30-year period at a spatial resolution of 30 m. We evaluated the impact of three interventions (revegetation, livestock exclusion, and the combination of both) on three ecosystem services; forage provision, erosion prevention, and presence of iconic vegetation. We also evaluated whether terrain characteristics could partially explain the variation in impact of interventions. The resulting maps showed spatial patterns of positive and negative effects of interventions on ecosystem services. Intervention effectiveness differed between land cover vegetation clusters, terrain aspect, and soil parent material. Our method allows for spatially explicit quantification of the long-term restoration impact on ecosystem service supply, and for the detailed visualization of impact across an area. This pixel-level analysis is specifically suited for heterogeneous landscapes, where restoration impact not only varies between but also within restoration sites.

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bioRxiv2021

Fractal triads efficiently sample ecological diversity and processes across spatial scales

The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylo-diversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.

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bioRxiv2021

Quantifying diversity, connectivity, and environmental resistance in dendritic ecological networks

O_LIA central theme in landscape ecology is the translation of individual movements within a population by deconstructing/interpreting the components of its topographical environment. Most such endeavors rely heavily on the concept of landscape resistance - a composite of an arbitrary number of features/covariates that, when identified/compiled, yield a surface inversely related to net movement. However, the statistical methodologies underlying this compilation have limited applicability when applied to dendritic ecological networks (DENs), including riverscapes. C_LIO_LIHerein we provide an analytical framework (ResistNet) that more appropriately annotates DEN segments by first aligning individual genetic distances with environmental covariates within a graph structure, then employing a genetic algorithm to optimise a composite model. C_LIO_LIWe evaluated the efficacy of our method by first testing it in silico across an array of sampling designs, spatial trajectories, and levels of complexity, then applying it in an empirical case study involving 13,218 ddRAD loci from N=762 Speckled Dace (Leuciscidae: Rhinichthys osculus), sampled across N=78 Colorado River localities. By doing so, we underscored the utility of ResistNet within a large-scale conservation study, as well as identified prerequisites for its appropriate application. C_LIO_LIOur contemporary framework not only allows an interpretation of meta-population/meta-community structure across DENs, but also highlights several innovative applications. These are: (a) Expanding an ongoing study design, and thus its hypotheses, into yet unsampled temporal and/or spatial arenas, and; (b) Promoting multi-species management through comparative analyses that extend across species and/or drainages. C_LI

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bioRxiv2021

Detecting the ecological footprint of selection

The structure of communities is influenced by many ecological and evolutionary processes, but the way this manifests in classic biodiversity patterns often remains unclear. Here, we aim to distinguish the ecological footprint of selection - through competition or environmental filtering - from that of neutral processes that are invariant to species identity. We build on existing Massive Eco-evolutionary Synthesis Simulations (MESS), which uses information from three biodiversity axes - species abundances, genetic diversity, and trait variation - to distinguish between mechanistic processes. In order to correctly detect and characterise competition, we add a new and more realistic form of competition that explicitly compares the traits of each pair of individuals. Our results are qualitatively different to those of previous work in which competition is based on the distance of each individuals trait to the community mean. We find that our new form of competition is easier to identify in empirical data compared to the alternatives. This is especially true when trait data are available and used in the inference procedure. Our findings hint that signatures in empirical data previously attributed to neutrality may in fact be the result of pairwise-acting selective forces. We conclude that gathering more different types of data, together with more advanced mechanistic models and inference as done here, could be the key to unravelling the mechanisms of community assembly.

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bioRxiv2021

Bycatch mitigation could prevent strong changes in the ecological strategies of seabird communities across the globe

Fisheries bycatch, the incidental mortality of non-target species, is a major threat to seabirds worldwide. Mitigating bycatch is an important factor to reduce seabird population declines and consequent changes in ocean trophic dynamics and ecosystem functioning. However, it remains an open question how and where mitigating bycatch at a global scale may conserve seabird traits and the ecological strategies that traits represent. Here we combine a dataset of species traits and distribution ranges for 341 seabirds with spatially resolved fishing effort data for gillnet, longline, trawl, and purse seine gears to: (1) understand spatial variation in seabird community traits; and (2) test whether mitigating fisheries bycatch may prevent shifts in traits of seabird communities and loss of ecological strategies. We find distinct spatial variation in the community weighted mean of five seabird traits (clutch size, body mass, generation length, foraging guild, and diet guild). Furthermore, our analysis suggests that successful bycatch mitigation could prevent strong shifts in the traits of seabird communities across the globe particularly in the North Atlantic and Southern Oceans. Specifically, changes in dominant foraging and diet guilds, and shifts towards communities with faster reproductive speeds (larger clutch sizes and shorter generation lengths) and smaller body masses could be avoided. Therefore, bycatch mitigation may have important indirect benefits for sustaining ecosystem functioning, as mediated by species traits. Incorporating species traits into management actions will provide valuable tools for marine spatial planning and when evaluating the success of conservation initiatives.

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