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bioRxiv2021

Ecological memory of prior nutrient exposure in the human gut microbiome

Many ecosystems retain an ecological memory of past conditions that affects responses to future stimuli. However, it remains unknown what mechanisms and dynamics may govern such a memory in microbial communities. Here, in both a human dietary intervention cohort and an artificial gut, we show that the human gut microbiome encodes a memory of past carbohydrate exposures. Changes in the relative abundance of primary degraders were sufficient to enhance metabolism, and these changes were mediated by transcriptional changes within hours of initial exposure. We further found that ecological memory of one carbohydrate species impacted metabolism of others. These findings demonstrate that the human gut microbiomes metabolic potential reflects dietary exposures over preceding days and changes within hours of exposure to a novel nutrient. One Sentence SummaryRecent nutrient exposures are encoded into the structure and activity of human gut microbial communities, which enables more efficient future metabolic responses.

ecology↗
bioRxiv2021

A mechanistic density functional theory for ecology across scales

Our ability to predict the properties of a system typically diminishes as the number of its interacting constituents rises. This poses major challenges for understanding natural ecosystems, and humanitys effects on them. How do macroecological patterns emerge from the interplay between species and their environment? What is the impact on complex ecological systems of human interventions, such as extermination of large predators, deforestation, and climate change? The resolution of such questions is hampered in part by the lack of a holistic approach that unifies ecology across temporal and spatial scales. Here we use density functional theory, a computational method for many-body problems in physics, to develop a novel computational framework for ecosystem modelling. Our methods accurately fit experimental and synthetic data of interacting multi-species communities across spatial scales and can project to unseen data. Our mechanistic framework provides a promising new avenue for understanding how ecosystems operate and facilitates quantitative assessment of interventions.

ecology↗
bioRxiv2021

Climatic niche conservatism shapes the ecological assembly of Hawaiian arthropod communities

Spatial variation in climatic conditions along elevation gradients provides an important backdrop by which communities assemble and diversify. Lowland habitats tend to be connected through time, whereas highlands can be continuously or periodically isolated, conditions that have been hypothesized to promote high levels of species endemism. This tendency is expected to be accentuated among taxa that show niche conservatism within a given climatic envelope. While species distribution modeling approaches have allowed extensive exploration of niche conservatism among target taxa, a broad understanding of the phenomenon requires sampling of entire communities. Species-rich groups such as arthropods are ideal case studies for understanding ecological and biodiversity dynamics along elevational gradients given their important functional role in many ecosystems, but community-level studies have been limited due to their tremendous diversity. Here, we develop a novel semi-quantitative metabarcoding approach that combines specimen counts and size-sorting to characterize arthropod community-level diversity patterns along two elevational gradients across two volcanoes on the island of Hawai i. We find that arthropod communities between the two transects become increasingly distinct compositionally at higher elevations. Resistance surface approaches suggest that climatic differences between sampling localities are an important driver in shaping beta-diversity patterns, though the relative importance of climate varies across taxonomic groups. Nevertheless, the climatic niche position of OTUs between transects was highly correlated, suggesting that climatic filters shape the colonization between adjacent volcanoes. Taken together, our results highlight climatic niche conservatism as an important factor shaping ecological assembly along elevational gradients and suggest topographic complexity as an important driver of diversification.

ecology↗
bioRxiv2021

A comparative network approach to assess the social-ecological underpinnings of zoonotic outbreaks at human-wildlife interfaces

O_LIPandemics caused by wildlife-origin pathogens, like COVID-19, highlight the importance of understanding the ecology of zoonotic transmission and outbreaks among wildlife populations at human-wildlife interfaces. To-date, the relative effects of human-wildlife and wildlife-wildlife interactions on the likelihood of such outbreaks remain unclear. C_LIO_LIIn this study, we used social network analysis and epidemiological Susceptible Infected Recovered (SIR) models, to track zoonotic outbreaks through wild animals joint propensities to engage in social-ecological co-interactions with humans, and their social grooming interactions with conspecifics. C_LIO_LIWe collected behavioral and demographic data on 10 groups of macaques (Macaca spp.) living in (peri)urban environments across Asia. Outbreak sizes predicted by the SIR models were related to structural features of the social networks, and particular properties of individual animals connectivity within those networks. C_LIO_LIOutbreak sizes were larger when the first-infected animal was highly central, in both types of networks. Across host-species, particularly for rhesus and bonnet macaques, the effects of network centrality on outbreak sizes were stronger through macaques human co-interaction networks compared to grooming networks. C_LIO_LIOur findings, independent of pathogen-transmissibility, suggest that wildlife populations in the Anthropocene are vulnerable to zoonosis more so due to their propensities to aggregate around anthropogenic factors than their gregariousness with conspecifics. Thus, the costs of zoonotic outbreaks may outweigh the potential/perceived benefits of jointly interacting with humans to procure anthropogenic food. From One Health perspectives, animals that consistently interact with both humans and conspecifics across time and space are useful targets for disease spillover assessments and control. C_LI

ecology↗
bioRxiv2021

Landscaping the behavioural ecology of primate stone tool use

Ecology is fundamental to the development, transmission, and perpetuity of primate technology. Previous studies on tool site selection have addressed the relevance of targeted resources and raw materials for tools, but few have considered the broader foraging landscape. In this first landscape-scale study of the ecological contexts of wild chimpanzee (Pan troglodytes verus) tool-use, we investigate the conditions required for nut-cracking to occur and persist over time at discrete locations in Bossou (Guinea). We examine this at three levels: selection, frequency of use, and inactivity. We find that, further to the presence of a nut tree and availability of raw materials, abundance of food-providing trees as well as proximity to nest sites were significant predictors of nut-cracking occurrence. This suggests that the spatial distribution of nut-cracking sites is mediated by the broader behavioural landscape and is influenced by non-extractive foraging of predictable resources, as well as non-foraging activities. Additionally, the number of functional tools was greater at sites with higher frequency of nut-cracking and was negatively correlated with site inactivity. Our findings indicate that the technological landscape of the Bossou chimpanzees shares affinities with the favoured places model of hominin site formation and provides new insights for reconstructing ancient patterns of landscape use. ResumeLecologie est fondamentale pour le developpement, la transmission et la perennite de la technologie des primates. Des etudes anterieures ont identifie la disponibilite des ressources cibles ainsi que les matieres premieres pour les outils comme des facteurs influents dans la selection des emplacements pour les activites technologiques. Cependant, il y a peu detudes qui abordent cette recherche a lechelle du paysage et du comportement fourrager. Dans cette premiere etude paysagere sur lutilisation doutils par le chimpanze sauvage (Pan troglodytes verus), nous recherchons les conditions ecologiques qui influencent la selection, lutilisation et linactivite des emplacements utilises pour le cassage des noix en Bossou, Guinee. Nos resultats montrent quen plus de la presence dun noyer et de la disponibilite des matieres premieres, labondance darbres nourriciers ainsi que la proximite des sites de nidification etaient des predicteurs significatifs de loccurrence du cassage des noix. Cela suggere que la distribution spatiale des sites de cassage de noix est influencee par le paysage comportemental et est influencee par le fourrage non-extractive de ressources previsibles, ainsi que par des activites non-fourragers. Nos resultats indiquent que le paysage technologique des chimpanzes de Bossou partage des affinites avec le modele des << lieux favoris >> de la formation des sites hominides et fournit de nouvelles perspectives pour reconstruire les modes dutilisation du paysage anciens.

ecology↗
bioRxiv2021

Towards precision ecology: Relationships of multiple sampling methods quantifying abundance for comparisons among studies

Because different sampling techniques will provide different abundance values, it is currently difficult to compare results among many studies to form holistic understandings of how abundance influences ant ecology. Using three sampling methods in the same location we found pitfall traps best confirmed A. gracilipes presence recording the fewest zero values (9.1%), card counts were the least reliable (67.1%), and tuna lures were intermediate (30.1%). The abundance of A. gracilipes from card counts ranged from 0 to 20, in pitfall traps from 0 to 325, and the full range of tuna lure abundance scores (0-7) were sampled. We then determined the relationships between these three standard ant sampling techniques for the abundance of yellow crazy ant Anoplolepis gracilipes. Irrespective of the data transformation method, the strongest relationship was between pitfall traps and tuna lures, and the least strong was between pitfall traps and card counts. We then demonstrate the utility of this knowledge by analysing A. gracilipes abundance reported within published literature to show where the populations in those studies sit on an abundance spectrum. We also comment on insights into the relative utility of the three methods we used to determine A. gracilipes abundance among populations of varying abundance. Pitfall traps was the most reliable method to determine if the species was present at the sample level. Tuna lures were predominantly reliable for quantifying the presence of workers, but were limited by the number of workers that can gather around a spoonful of tuna. Card counts were the quickest method, but were seemingly only useful when A. gracilipes abundance is not low. Finally we discuss how environmental and biological variation needs to be accounted for in future studies to better standardise sampling protocols to help progress ecology as a precision science.

ecology↗
bioRxiv2021

Diversity and distribution of sediment bacteria across an ecological and trophic gradient

Can we cluster bacterial sediment communities based on lake size, depth, and trophic status? Or, are bacterial microbial communities an emergent property of their geography, integrating regional physical and climatic conditions? Lakes in Minnesota are uniquely situated to address these questions because of their wide geographic range and variability in size and basin land-use. In this study, we selected twenty lakes with varying morphological and chemical properties across four ecological regions of Minnesota. Our objectives were to (i)) evaluate the diversity and spatial variation of the bacterial community at the sediment-water interface and (ii) determine how lake location and watershed land-use impact aqueous chemistry and influence community structure. Our data indicate that sediment communities from similar depth intervals are more likely to cluster by ecological region rather than any individual lake properties (e.g., trophic status, TP concentration, lake depth). However, composition is tied to a given lake, wherein samples from the same core were more alike than samples at similar depths across lakes. Our results illustrate the diversity within lake sediment microbial communities and provide insight into relationships between taxonomy, physicochemical, and geographic properties of north temperate lakes.

ecology↗
bioRxiv2021

Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome

The human gut microbiome harbors substantial ecological diversity at the species level, as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of co-colonizing lineages. Here, we analyze the daily dynamics of intra-specific genetic variation in the gut microbiomes of four healthy, densely longitudinally sampled hosts. First, we find that overall genetic diversity in a large majority of species is stationary over time, despite short-term fluctuations. Next, we show that fluctuations in abundances in approximately 80% of strains analyzed can be predicted with a stochastic logistic model (SLM)--an ecological model of a population experiencing environmental fluctuations around a fixed carrying capacity which has previously been shown to capture statistical properties of species abundance fluctuations. The success of this model indicates that strain abundances typically fluctuate around a fixed carrying capacity, suggesting that most strains are dynamically stable. Finally, we find that the strain abundances follow several empirical macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains.

ecology↗
bioRxiv2021

Feasibility conditions of ecological models: Unfolding links between model parameters

Over more than 100 years, ecological research has been striving to derive internal and external conditions compatible with the coexistence of a given group of interacting species. To address this challenge, numerous studies have focused on developing ecological models and deriving the necessary conditions for species coexistence under equilibrium dynamics, namely feasibility. However, due to mathematical limitations, it has been impossible to derive analytic expressions if the isocline equations have five or more roots, which can be easily reached even in 2-species models. Here, we propose a general formalism to obtain the set of analytical conditions of feasibility for any polynomial population dynamics model of any dimension without the need to solve for the equilibrium locations. We additionally illustrate the application of our methodology by showing how it is possible to derive mathematical relationships between model parameters, while maintaining feasibility in modified Lotka-Volterra models with functional responses and higher-order interactions (model systems with at least five equilibrium points)--a task that is impossible to do with simulations. This work unlocks the opportunity to increase our systematic understanding of species coexistence.

ecology↗
bioRxiv2021

Doors and corners of variance partitioning in statistical ecology

Variance partitioning is a common tool for statistical analysis and interpretation in both observational and experimental studies in ecology. Its popularity has led to a proliferation of methods with sometimes confusing or contradicting interpretations. Here, we present variance partitioning as a general tool in a model based Bayesian framework for summarizing and interpreting regression-like models. To demonstrate our approach we present a case study comprising of a simple occupancy model for a metapopulation of the Glanville fritillary butterfly. We pay special attention to the thorny issue of correlated covariates and random effects, and highlight uncertainty in variance partitioning. We recommend several alternative measures of variance, which jointly can be used to better interpret variance partitions. Additionally, we extend the general approach to encompass partitioning of variance within and between groups of observations, an approach very similar to analysis of variance. While noting that many troublesome issues relating to variance partitioning, such as uncertainty quantification, have been neglected in the literature, we likewise feel that the rather general applicability of the methods as an extension of statistical model-based analyses has not been fully utilized by the ecological research community either.

ecology↗
bioRxiv2021

NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. Additionally, the GAM transformation increased the F1 score of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

ecology↗
bioRxiv2021

Data-Poor Ecological Risk Assessment of Multiple Stressors

O_LIEcological Risk Assessment is a formal process widely applied to terrestrial, marine, and freshwater ecosystems to evaluate the likelihood of adverse ecological effects occurring as a result of exposure to natural or anthropogenic stressors. For many species, data is sparse and semi-quantitative methodologies provide valuable insight for ecosystem management. Recent statistical developments have improved the quality of these analyses yet a rigorous theoretical framework to assess the cumulative impact of multiple stressors is lacking. C_LIO_LIWe present EcoRAMS, a web application and open-source software module that provides easy-to-use, statistically-robust ecological risk assessments of multiple stressors in data-poor contexts. The software receives attribute scores for two variables (e.g. exposure-sensitivity, productivity-susceptibility, severity-likelihood) via CSV templates and outputs results according to a probabilistic metric of risk. C_LIO_LIWe demonstrate comparative results across a range of assumptions, using simulated and empirical datasets including up to five stressors. Accounting for multiple stressors even when data is limited provides a more detailed analysis of risk that may otherwise be understated in single stressor analyses. C_LIO_LIThis application will allow quantification of risk across data-poor contexts for which statistical results have been previously unavailable. The web app format of EcoRAMS.net lowers the barrier of use for practitioners and scientists at any level of statistical training. C_LI

ecology↗
bioRxiv2021

Modern microbial mats from the Chihuahuan Desert provide insights into ecological stability throughout Earth's history

Microbial mats are complex ecological assemblages that are found in the Precambrian fossil record and in extant extreme environments. Hence, these structures are regarded as highly stable ecosystems. In this work, we assess the ecological stability in a modern, fluctuating, hypersaline pond from the Cuatro Cienegas Basin. From the 2016 to 2019 metagenomic sampling of this site, we found that this microbial site is sensitive to disturbances, which leads to high taxonomic replacement. Additionally, the mats have shown to be functionally stable throughout time, and could be differentiated between dry and rainy seasonal states. We speculate that this microbial system could represent modern analogs of ancient microbial mats where functions were preserved over time, whereas composition was subject to diversification in the face of local and planetary perturbations.

ecology↗
bioRxiv2021

Host exposure to symbionts and ecological drift generate divergence in parasite community assembly

The initial colonization of a host by symbionts, ranging from parasites to mutualists, can generate priority effects that alter within-host interactions and the trajectory of parasite community assembly. At the same time, variation in parasite communities among hosts can also stem from stochastic processes. Community ecology theory posits that multiple processes (e.g. dispersal, selection and drift) interact to generate variation in community structure, but these processes are rarely considered simultaneously during community assembly. To test the role of these processes in a parasite community, we experimentally simulated dispersal of three symbionts by factorially inoculating individual plants of tall fescue with two foliar fungal parasites, Colletotrichum cereale and Rhizoctonia solani, and a hypothesized mutualist endophyte, Epichloe coenophiala. We then tracked parasite infections longitudinally in the field. After the initial inoculations, hosts were exposed to a common pool of parasites in the field, which we expected to cause parasite communities to converge towards a similar community state. To test for convergence, we analyzed individual hosts parasite community trajectories in multivariate space. In contrast to our expectation, there was no signal of convergence. Instead, parasite community trajectories generally diverged over time between treatment groups and the magnitude of divergence depended on the symbiont species inoculated. Parasite communities of hosts that were inoculated with only the mutualist, Epichloe, showed significant trends of divergence relative to all other symbiont inoculation treatments. In contrast, hosts inoculated with only Rhizoctonia did not exhibit clear trends of divergence when compared to other parasite inoculations. Further, co-inoculation with both parasite species resulted in faster rates of divergence and greater temporal change in parasite communities relative to hosts inoculated with only the parasite Colletotrichum. As predicted by existing theory, parasite communities showed evidence of drift during the beginning of the experiment, which contributed to among-host divergence in parasite community structure. Overall, these data provide evidence that initial dispersal of symbionts produced persistent changes in parasite community structure via ecological selection, that drift was important during the early stages of parasite community assembly, and together, dispersal, selection and drift resulted in parasite community divergence. Open Research statementThe data and code that support the findings of this study are available through Zenodo at https://doi.org/10.5281/zenodo.5714452

ecology↗
bioRxiv2021

Idea paper: An envelope model of ecological disturbance

Disturbance is common in natural ecosystems, but increasingly defines them. While there are many descriptions for the dynamics of an ecosystems response to disturbance, there are few descriptions for the dynamics of the disturbance itself. I describe a novel application of a model based on the production of amplitude envelopes in acoustics and electronic music synthesis, with varying parameters Attack, Decay, Sustain, and Release (ADSR). I show that varying the parameters of the ADSR model is sufficient to produce and vary the qualitative disturbance regimes described by previous authors, and is capable of producing dynamics not previously considered. I tested the utility of the ADSR model by applying it to a logistic growth model. I found that manipulating the attack and release parameters of the ADSR model changes the population dynamics estimated by these models. This implies that responses to disturbance are determined not only by the resilience and resistance of the ecological system, but also the dynamics of the disturbance itself. My hope is that the ADSR model will prove useful to researchers in either describing disturbances in long-term ecological data, or in producing disturbances for simulations or experiments.

ecology↗
bioRxiv2021

Areas Important for Ecological Connectivity Throughout Canada

Governments around the world have acknowledged the importance of conserving ecological connectivity to help reverse the decline of biodiversity. In this study we employed recent methodological developments in circuit theory to conduct the first pan-Canadian analysis of multi-species connectivity for all terrestrial regions of the country, at a spatial grain sufficient to support local land-management decisions. We developed a movement cost surface with a limited number of thematic categories using the most recently updated land cover data available for the country. We divided the country into 17 tiles and used a wall-to-wall, omnidirectional mode of Circuitscape on each tile in order to assess ecological connectivity throughout entire landscapes as opposed to strictly among protected areas. The resulting raw current density map of Canada revealed heterogenous patterns of current density across the country, strongly influenced by geography, natural barriers, and human development. We included a validation analysis of the output current density map with independent wildlife data from across the country and found that mammal and herpetofauna locations were predicted by areas of high current density. We believe our current density map can be used to identify areas important for connectivity throughout Canada and thereby contribute to efforts to conserve biodiversity.

ecology↗
bioRxiv2021

Ecological landscapes guide the assembly of optimal microbial communities

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia. Author summaryResearch shows that microbial communities comprised of specific species combinations can cure disease, improve agricultural output, or synthesize valuable chemicals. But finding the species combinations that generate high-performing communities is challenging because there are too many species combinations to test exhaustively. So, scientists use heuristic strategies that test only a few species combinations to search for high-performing communities. However, these heuristic strategies often fail to find the best species combinations, and we still do not understand when they fail. Here, we develop a framework to analyze these heuristic strategies, building on the concept of fitness landscapes studied in evolution and computer science. We apply this framework to data from simulated microbial community models to identify biological properties that affect the success of heuristic search strategies, such as the extent to which microbes compete for the same metabolites. Further, we establish statistical measures of the landscape structure that can help estimate search success from preliminary data. We validate our findings using experimental data from communities of soil microbes. Together, our results develop a conceptual framework to analyze and develop heuristic search strategies and identify guiding principles to help scientists choose species and environmental conditions that make finding high-performing microbial communities easier.

ecology↗
bioRxiv2021

Continental-scale Hyperspectral tree species classification in the National Ecological Observatory Network

Advances in remote sensing imagery and machine learning applications unlock the potential for developing algorithms for species classification at the level of individual tree crowns at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. Little is known about how well these approaches generalize across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental-extent model for tree species classification that can be applied to the network, including a wide range of US terrestrial ecosystems. We compared the performance of a model trained with data from 27 NEON sites to models trained with data from each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geographic location, topography, and ecological conditions on the accuracy and precision of species predictions (72 out of 77 species). On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.70 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Predicted species were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON, this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the entire US, which make up to roughly 90% of the upper canopy of the studied ecosystems. This suggests the potential for integrating information from multiple datasets and locations to develop broad scale general models for species classification from hyperspectral imaging.

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