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bioRxiv2021

Spatial integrated models foster complementarity between monitoring programs in producing large-scale ecological indicators

Over the last decades, large-scale ecological projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large-scale population dynamics from a single monitoring program is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, integrated models combine multiple data types into a single analysis to quantify population dynamics of a targeted population. When working at large geographical scales, integrated spatial models have the potential to produce spatialised ecological estimates that would be difficult to obtain if data were analysed separately. In this paper, we illustrate how spatial integrated modelling offers a relevant framework for conducting ecological inference at large scales. Focusing on the Mediterranean bottlenose dolphins (Tursiops truncatus), we combined 21,464 km of photo-identification boat surveys collecting spatial capture-recapture data with 24,624 km of aerial line-transect following a distance-sampling protocol. We analysed spatial capture-recapture data together with distance-sampling data to estimate abundance and density of bottlenose dolphins. We compared the performances of the distance sampling model and the spatial capture-recapture model fitted independently, to our integrated spatial model. The outputs of our spatial integrated models inform bottlenose dolphin ecological status in the French Mediterranean Sea and provide ecological indicators that are required for regional scale ecological assessments like the EU Marine Strategy Framework Directive. We argue that integrated spatial models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales.

ecology↗

bioRxiv2021

Fine scale prediction of ecological community composition using a two-step sequential machine learning ensemble

Prediction is one the last frontiers in ecology. Indeed, predicting fine scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models informed by ecological deterministic processes to predict species abundances using reasonably easy to obtain data. To overcome the classical procedure in ecology of parameterizing complex population models of multiple species interactions and poor predictive power, we followed instead a sequential data-driven modeling approach. We use this framework to predict species abundances over 5 years in a highly diverse annual plant community. Our models show a surprisingly high spatial predictive accuracy (RSE ~ 0.13) using only easy to measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggest that predicting the temporal dimension of our system requires longer time series data. Such data would likely capture additional sources of variability that determine temporal patterns of species abundances. In addition, we show that these data-driven models can also inform back mechanistic models of important missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Being able to gain predictive power at fine-scale species composition while maintaining a mechanistic understanding of the underlying processes can be a pivotal tool for conservation, specially given the human induced rapid environmental changes we are experiencing. Here, we document how this objective can be achieved by promoting the interplay between classic modelling approaches in ecology and recently developed data-driven models. Author summaryPrediction is challenging but recently developed machine learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the complexity of gathering information on the needed explanatory variables, which often comprise not only physical variables such as temperature or soil nutrients, but also information about the complex network of species interactions regulating species abundances. Here we present a two-step sequential modelling framework that overcomes these constraints. We first infer potential species abundances training models just with easily obtained abiotic variables, and then use this outcome to fine-tune the prediction of the realized species abundances when taking into account the rest of the predicted species in the community. Overall, our results show a promising way forward for fine scale prediction in ecology.

ecology↗

bioRxiv2021

Defining dual-axis landscape gradients of human influence for studying ecological processes

Ecological processes are strongly shaped by human landscape modification, and understanding the reciprocal relationship between ecosystems and modified landscapes is critical for informed conservation. Single axis measures of spatial heterogeneity proliferate in the contemporary gradient ecology literature, though they are unlikely to capture the complexity of ecological responses. Here, we develop a standardized approach for defining multi-dimensional gradients of human influence in heterogeneous landscapes and demonstrate this approach to analyze landscape characteristics of ten ecologically distinct US cities. Using occupancy data of a common human-adaptive songbird collected in each of the cities, we then use our dual-axis gradients to evaluate the utility of our approach. Spatial analysis of landscapes surrounding ten US cities revealed two important axes of variation that are intuitively consistent with the characteristics of multi-use landscapes, but are often confounded in single axis gradients. These were, a hard-to-soft gradient, representing transition from developed areas to non-structural soft areas; and brown-to-green, differentiating between two dominant types of soft landscapes: agriculture (brown) and natural areas (green). Analysis of songbird data demonstrated that occupancy responds to both hard-to-soft (decreasing with development intensity) and brown-to-green gradient (increasing with more natural area). Overall, our results reveal striking consistency in the dominant sources of variation across ten geographically distinct cities and suggests that our approach advances how we relate variation in ecological responses to human influence. Our case study demonstrates this: robins show a remarkably consistent response to a gradient differentiating agricultural and natural areas, but city-specific responses to the more traditional gradient of development intensity, which would be overlooked with a single gradient approach. Managing ecological communities in human dominated landscapes is extremely challenging due to a lack of standardized approaches and a general understanding of how socio-ecological systems function, and our approach offers promising solutions.

ecology↗

bioRxiv2021

Evaluating the efficacy of a consumer-centric method for ecological sampling: Using bonobo (Pan paniscus) feeding patterns as an instrument for tropical forest characterization

O_LICharacteristics of food availability and distribution are a key component of a species ecology. Objective measurement of food resources, such as vegetation plot sampling, do not consider aspects of selection by the consumer and therefore may produce imprecise measures of availability. Further, in most animal ecology research, traditional ecological surveying often is time-intensive and supplementary to ongoing behavioral observation. We propose a method to integrate ecological sampling of an animals environment into existing behavioral data collection systems by using the consumer as the surveyor. Here, we introduce the consumer-centric method (CCM) of assessing resource availability for its ability to measure food resource abundance, distribution, and dispersion. This method catalogues feeding locations observed during behavioral observation and uses aggregated data to characterize these ecological metrics. C_LIO_LIWe evaluated the CCM relative to traditional vegetation plot surveying using accumulated feeding locations across three years visited by a tropical frugivore, the bonobo (Pan paniscus), and compared it with data derived from over 200 vegetation plots across their 50km2+ home range. C_LIO_LIWe demonstrate that food species abundance estimates derived from the CCM are comparable to those derived from traditional vegetation plot sampling after approximately 600 observation days or 60 spatially explicit feeding locations. The agreement between the methods further improved when accounting for aspects of consumer selectivity in objective vegetation plot sampling (e.g., size minima). Estimates of density from CCM correlated with plot-derived estimates and were relatively insensitive to home range inclusion and other species characteristics, but were sensitive to sampling frequency (e.g., consumption frequency). Agreement between the methods in relative distribution of resources performed better across species than expected by chance, although measures of dispersion correlated poorly. C_LIO_LIWe demonstrate that while providing a robust measure to quantify local food availability, the CCM has an advantage over traditional sampling methods as it incorporates sampling biases relevant to the consumer. Therefore, as this method can be incorporated into existing observational data collection and does not require additional ecological surveying, it serves as a promising method for behavioral ecological data collection for animal species who re-use space and consume immobile food items. C_LI

ecology↗

bioRxiv2022

Monitoring of urban ecological environment including air quality using satellite imagery

Rapid urbanisation has highlighted problems in the urban ecological environment and stimulated research on the evaluation of urban environments. In previous studies, key factors such as greenness, wetness, and temperature were extracted from satellite images to assess the urban ecological environment. Although air pollution has become increasingly serious as urbanisation proceeds, information on air pollution is not included in existing models. The Sentinel-5P satellite launched by the European Space Agency in 2017 is a reliable data source for monitoring air quality. By making full use of images from Landsat 8, Sentinel-2A, and Sentinel-5P, this work attempts to construct a new remote sensing monitoring index for urban ecology by adding air quality information to the existing remote sensing ecological index. The proposed index was tested in the Beijing metropolitan area using satellite data from 2020. The results obtained using the proposed index differ greatly in the central urban region and near large bodies of water from those obtained using the existing remote sensing monitoring model, indicating that air quality plays a significant role in evaluating the urban ecological environment. Because the model constructed in this study integrates information on vegetation, soil, humidity, heat, and air quality, it can comprehensively and objectively reflect the quality of the urban ecological environment. Consequently, the proposed remote sensing index provides a new approach to effectively monitoring the urban ecological environment.

ecology↗

bioRxiv2023

Habitat degradation homogenizes ecological responses to typhoons across a subtropical island

Climate change is increasing the frequency, intensity, and duration of extreme weather events across the globe. Understanding the capacity for ecological communities to withstand and recover from such events is critical. Typhoons are extreme weather events that are expected to broadly homogenise ecological dynamics through structural damage to vegetation and longer-term effects of salinization. Given their unpredictable nature, monitoring ecological responses to typhoons is challenging, particularly for mobile animals such as birds. Here, we report spatially variable ecological responses to typhoons across terrestrial landscapes. Using a high temporal resolution passive acoustic monitoring network across 24 sites on the subtropical island of Okinawa, Japan, we found that typhoons elicit divergent ecological responses among Okinawas diverse terrestrial habitats, as indicated by increased spatial variability of biological sound production (biophony) and individual species detections. This suggests that soniferous communities are capable of a diversity of different responses to typhoons. That is, spatial insurance effects among local ecological communities provide resilience to typhoons at the landscape scale. Even though site-level typhoon impacts on soundscapes and bird detections were not particularly strong, monitoring at scale with high temporal resolution across a broad spatial extent nevertheless enabled detection of spatial heterogeneity in typhoon responses. Further, species-level responses mirrored those of acoustic indices, underscoring the utility of such indices for revealing insight into fundamental questions concerning disturbance and stability. Our findings demonstrate the significant potential of landscape-scale acoustic sensor networks to capture the understudied ecological impacts of unpredictable extreme weather events.

ecology↗

bioRxiv2023

A social-ecological geography of southern Canadian Lakes

Anthropogenic pressures, including urban and agricultural expansion, can negatively influence a lakes capacity to provide aquatic ecosystem services (ES). However, identifying lakes most at risk of losing their ES requires integrating information on lake ecological state, global change threats, and ES demand. Here, we provide a social-ecological framework that combines these features within a regional context based on an ecological evaluation of the state of 659 lakes across Canada. From deviation of impacted lakes to reference ones, we identified much higher concentrations of total nitrogen and chloride as the main indicators of altered lake ecological state in all regions identified. Lake ecological state was mapped using an additive colour model along with regional scores of threat levels and recreational ES demand. Population density and agriculture were linked to high lake vulnerability. Lakes in Southern Ontario were most concerning, being highly altered, under threat, and heavily used. Lakes near urban centers along coasts were altered and used, but less threatened, whereas those in the Prairies were altered and threatened, but less used. Our novel framework provides the first social-ecological geography of Canadian lakes, and, is a promising tool to assess lake state and vulnerability at scales relevant for management. Plan language summaryPlain language title: Assessing overall lake health across Canada to identify sites for restoration and conservation Canadians love to swim, fish, and navigate in and on the countless lakes across the country. But Canadian lakes are under a considerable amount of pressure from human activities in their watershed. The expansion of cities, intensive farming, wetland loss, and industrial development all results in the transfer of pollutants to aquatic habitats, threatening the health of lakes and the ecosystem services they provide. Where are lakes being used across Canada? What condition are they in and is their use under threat from different pressures? To answer these questions, we combined information from many different sources, including a national scale lake assessment, through the NSERC Strategic Network Cluster Lake Pulse to create the first social-ecological geography of southern Canadian lakes. Regionally specific baseline conditions were established from lakes considered healthy due to limited human activities in their watershed. When lakes with impacted watershed were compared to healthy ones within their specific region, two early warning signals of human pressure, pollution from nitrogen found in fertilizers and sewage, and chloride found in road salt, determined whether a lake was altered. We combined these two health indicators, with information on future potential lake threats and use by the population for recreational purposes. Using a colour-coded mapping technique, we were able to identify regions where lakes were altered, threatened, and used. These regions occurred primarily around dense urban areas, of southern Ontario and Quebec, and major cities on the east and west coast. Lakes were altered and threatened, but seemingly less used in the Prairie Provinces. The novel approach is very adaptable, easy to understand, and can be used at more regional levels for management to determine priority sites for conservation and restoration, as well as in science communication to describe overall lake health.

ecology↗

bioRxiv2023

Predicting the timing of ecological phenomena across regions using citizen science data

Spatial predictions of intra-annual ecological variation enhance ecological understanding and inform decision-making. Unfortunately, it is often challenging to use statistical or machine learning techniques to make such predictions, due to the scarcity of systematic, long-term observational data. Conversely, opportunistic time-stamped observation records, supported by highly informative data such as photographs, are increasingly available for diverse ecological phenomena in many regions. However, a general framework for predicting such phenomena using opportunistic data remains elusive. Here, we introduce a novel framework that leverages the concept of relative phenological niche to model observation records as a sample of temporal environmental conditions in which the represented ecological phenomenon occurs. We demonstrate its application using two distinct, management-relevant, ecological events: the emergence of the adult stage of the invasive Japanese beetle (Popillia japonica), and of fruiting bodies of the winter chanterelle mushroom (Craterellus tubaeformis). The framework accounts for spatial and temporal biases in observation data, and it contrasts the temporal environmental conditions (e.g., in temperature, precipitation, wind speed, etc.) associated with the observation of these events to those available in their occurrence locations. To discriminate between the two sets of conditions, we employ machine-learning algorithms (boosted regression trees and random forests). The proposed approach can accurately predict the temporal dynamics of ecological events across large geographical scales. Specifically, it successfully predicted the intra-annual timing of occurrence of adult Japanese beetles and of winter chanterelle mushrooms across Europe and North America. We further validate the approach by successfully predicting the timing of occurrence of adult Japanese beetles in Northern Italy, a recent hotspot of invasion in continental Europe, and the winter chanterelle mushroom in Denmark, a country with a high number of records of this mushroom. These results were also largely insensitive to temporal bias in recording effort. Our results highlight the potential of opportunistic observation data to predict the temporal variation of a wide range of ecological phenomena in near real-time. Furthermore, the conceptual and methodological framework is intuitive and easily applicable for the large number of ecologists already using machine-learning and statistical-based predictive approaches.

ecology↗

bioRxiv2024

Quantifying irreversibility of ecological systems

Irreversibility--the asymmetry of population dynamics when played forward versus backward in time--is a fundamental property of ecological dynamics. Despite its early recognition in ecology, irreversibility has remained a high-level and unquantifiable concept. Here, we introduce a quantitative framework rooted in non-equilibrium statistical physics to measure irreversibility in general ecological systems. Through theoretical analyses, we demonstrate that irreversibility quantifies the degree to which a system is out of equilibrium, a property not captured by traditional ecological metrics. We validate this prediction empirically across diverse ecological systems structured by different forces, such as rapid evolution, nutrient availability, and temperature. In sum, our study provides a rigorous formalism for quantifying irreversibility in ecological systems, with the potential to integrate dynamical, energetic, and informational perspectives in ecology.

ecology↗

bioRxiv2024

Understanding Ecological Systems Using Knowledge Graphs: An Application to Highly Pathogenic Avian Influenza

Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of important interactions underpinning system structure, resilience, and dynamics. Knowledge graphs have been successfully applied to organize heterogeneous data systematically and to predict new linkages representing unobserved relationships in complex systems. Though not previously applied broadly in ecology, knowledge graphs have much to offer in an era of global change when system dynamics are responding to rapid changes across multiple scales simultaneously. We developed a knowledge graph to demonstrate the methods utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad animal host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include a wide range of data related to HPAI including pathogen-host associations, animal species distributions, and human population demographics, using a semantic ontology that defines relationships within the data and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying new relationships and features of HPAI ecology, underscoring the generalizable value of knowledge graphs to ecology including their utility in revealing previously known relationships between entities and generating testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

ecology↗

bioRxiv2024

Improving the integration of AI into existing ecological inference workflows

O_LIArtificial Intelligence (AI) has revolutionised the process of identifying species and individuals in audio recordings and camera trap images. However, despite developments in sensor technology, machine learning, and statistical methods, a general AI-assisted data-to-inference pipeline has yet to emerge. C_LIO_LIWe argue that this is, in part, due to a lack of clarity around several decisions in existing workflows, including: the choice of classifier used (e.g., semi- vs. fully automated); how classifier confidence scores are used and interpreted; and the availability and selection of appropriate statistical methods for drawing ecological inferences. C_LIO_LIHere, we attempt to conceptualise a general workflow associated with automated tools in ecology. We motivate this perspective using our experiences with occupancy modelling using monitoring data collected through passive acoustic monitoring and camera trapping, identifying priority areas for future developments. C_LIO_LIWe offer an accessible guide to support the ecological community in navigating and capitalising on rapid technological and methodological advances. We describe how different error types arise from both sensor-based monitoring and from classifiers themselves; how different error types are handled at each stage of the workflow; and finally, implications and opportunities associated with deciding on methods used at each step of the pipeline. C_LIO_LIWe recommend that "black box" tools like neural network classification algorithms should be embraced in ecology, but widespread uptake requires more formal integration of AI into the existing ecological inference workflows. Like ecological AI more broadly, however, successful development of new data-to-inference pipelines is a multidisciplinary endeavour that requires input from everyone invested in collecting, processing, analysing, and using ecological monitoring data. C_LI

ecology↗

bioRxiv2025

Ecological ubiquity and phylogeny drive nestedness in phages-bacteria networks and shape the bacterial defensome

Identifying the ecological and evolutionary factors that shape phage-bacterial interactions is key to understanding their dynamics in microbial communities. Yet, such interactions remain poorly characterised in plant agroecosystems. Here, we investigate the ecological determinants of the interaction between a highly diverse set of 23 phages isolated from diseased apricot trees and 44 bacterial strains from the Pseudomonas syringae species complex collected either from diseased apricot trees, healthy plants or non-agricultural environment. Based on their ecological origin, we expected phages to preferentially infect bacterial strains from the same ecological context, forming modular host-range patterns. Contrary to these expectations, we discovered a significantly nested structure, suggesting generalised infection dynamics rather than local adaptation, primarily driven by the broad ecological dynamics of this pathosystem. Analysis of the bacterial genomes showed that both the profiles of anti-phage defence systems and the distribution profiles of prophages are strongly shaped by bacterial phylogeny. Furthermore, while the number of defence systems showed limited correlation with the breadth of bacterial sensitivity to phages, prophage abundance exhibited a strong, non-linear link with phage virulence. Together, these findings provide an ecological and evolutionary perspective on phage-bacterium infection networks and new insights into a better understanding of the role of phages in agricultural ecosystems. Author SummaryViruses that infect bacteria, known as phages, are part of microbial communities and influence the abundance, diversity, and traits of their hosts. In an agriculture-related context, they are commonly considered as potential biocontrol agents, but studying the bases of fundamental phage-bacterial interactions may help us better understand the plant microbiome and its applications. Many factors influence these interactions, and identifying which ones matter most remains a challenge. In our study, we investigated how phages from diseased plants interact with bacteria collected from diseased and healthy plants, as well as from surrounding environments. We expected phages to mainly infect bacteria from similar environments, but instead observed that they often infected bacteria regardless of their source. This suggests that phage activity in this system has few barriers, reflecting the wide ecological distribution of their bacterial hosts. We further investigated how bacteria defend against phages by identifying both defence systems and prophages within their genomes and using this information to explore their contribution to bacterial resistance or sensitivity to phages. Together, our findings offer new insights into how phage-bacterium relationships evolve and function in plant ecosystems.

ecology↗

bioRxiv2025

Contrasting ecological communities in rice paddy fields under conventional and no-fertilizer farming practices

Global crop production is essential for providing energy and nutrients to humans, but agricultural systems contribute substantially to environmental issues, making it crucial to find sustainable methods for crop production. In this study, we conducted extensive field monitoring of two rice paddy fields in Kyoto Prefecture, Japan, where a rice variety, Hinohikari, was cultivated to investigate how ecological communities influence rice growth and yields under two contrasting farming practices: conventional farming and no-fertilizer farming. We had three objectives: (1) to monitor rice growth and ecological dynamics to create a comprehensive ecological time series using quantitative environmental DNA metabarcoding and complementary monitoring methods, (2) to identify ecological variables that causally affect rice performance using nonlinear time series analysis, and (3) to examine the effects of the two farming practices on ecological variables and rice performance. As expected, the no-fertilizer paddy field showed lower rice growth and yields but still produced 40-50% of the yields of the conventional field. Ecological monitoring revealed contrasting ecological communities between the two fields, particularly among plant species on the paddy ridges and microbial taxa. Twenty-five taxa had statistically clear causal influences on rice performance. While the per-abundance influences of the causal taxa were largely similar in both paddy fields, their abundances were different, contributing to the differences in the overall effects of these taxa on the rice performance. These findings suggest that the abundance of causal taxa, along with nutrient conditions, may have driven differences in rice growth between the two paddy fields.

ecology↗

bioRxiv2018

Developing an automated iterative near-term forecasting system for an ecological study

O_LIMost forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform.\nC_LIO_LIIterative near-term ecological forecasting involves repeated daily to annual scale forecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a long-term study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modeling and forecasting, archiving, and presentation of the forecasts.\nC_LIO_LIThe forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.\nC_LIO_LIAutomated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems.\nC_LI

ecology↗

bioRxiv2019

Invasion but not hybridisation is associated with ecological niche shift in monkeyflowers

BackgroundThe ecological niche occupied by novel hybrids can influence their establishment as well as the potential to coexist with their parents. Hybridisation generates new phenotypic combinations, which, in some cases, may allow them to occupy ecological niches outside the environmental envelope of parental taxa. In other cases, hybrids may retain similar ecological niches to their parents, resulting in competition and affecting their coexistence. To date, few studies have quantitatively assessed niche shifts associated with hybridisation in recently introduced populations while simultaneously characterising the niche of parental species in both native and introduced ranges.\n\nAimsIn this study, we compared the ecological niche of a novel hybrid plant with the niches of its two parental taxa in the non-native geographic range. We also characterised and compared the parental taxas ecological niche of native and introduced populations in order to assess potential niche changes during the invasion process independent of hybridisation.\n\nMethodsWe studied monkeyflowers (Mimulus spp., Phrymaceae) that were introduced from the Americas to Europe and New Zealand in the last 200 years. We focused on a novel hybrid, triploid, asexual taxon (M. x robertsii) that occurs only in the British Isles where its two parents (M. guttatus and M. luteus) come into secondary contact. We assembled more than 12,000 geo-referenced occurrence records and eight environmental variables of the three taxa across native and introduced ranges, and conducted ecological niche model analysis using maximum entropy, principal component and niche dynamics analysis.\n\nResultsWe found no evidence of niche shift in the hybrid, M. x robertsii compared to introduced populations of both of their parental taxa. The hybrid had a niche more similar to M. luteus, which is also the rarest of the parental taxa on the introduced range. Among parental monkeyflowers, M. guttatus showed niche conservatism in introduced populations in Europe, but a niche shift in New Zealand, while M. luteus showed a niche shift in Europe. However, the evidence of niche shift should be treated with caution due to the occurence of non-analog climatic conditions, small population size and unfilling niche dynamics.\n\nConclusionsOur results suggest that hybridisation in non-native monkyeflowers did not result in a shift in ecological niche. This niche conservation could create competition between parental and derived taxa, the outcome of which will depend on relative competitive abilities. Further work is needed to establish if the expansion of the hybrid in the introduced range is causally related to the apparent rarity of one of the parents (M.luteus). Finally, the comparison of native and non-native populations of parental taxa, suggest that whether invasions result in niche shifts or not depends on both taxon and geographic region, highlighting the idiosyncratic nature of biological invasions.

ecology↗

bioRxiv2026

Environmental sensing capacity predicts bacterial ecological strategies and environmental preferences

The ability to sense environmental variation is a prerequisite for ecological success. Sensor domains enable bacteria to detect nutrients, neighboring organisms, and physicochemical conditions, but whether variation in these sensing systems reflects ecological specialization remains unresolved. Here, we analyzed sensor domains across 51,343 bacterial genomes and 255 soil metagenomes spanning a climatic gradient to determine whether sensory repertoires encode bacterial ecological strategies and environmental preferences. Sensory repertoires exhibited strong phylogenetic conservatism and revealed signatures of genome streamlining, indicating that environmental sensing reflects trade-offs associated with maintaining sensory complexity. Taxa occupying environmentally heterogeneous habitats, particularly free-living aerobic generalists, encoded the largest sensory repertoires, consistent with selection for expanded environmental information processing. To link sensory function with ecological adaptation, we mapped experimentally-characterized ligand-binding motifs (LBMs) across genomes and metagenomes. Distinct LBM profiles discriminated host-associated and free-living taxa, aerobic and anaerobic lineages, and generalists and non-generalists, revealing a tight coupling between sensory capacity and ecological strategy. Across soil communities, motifs associated with osmoprotection and oxygen sensing were consistently enriched under increasing aridity, linking sensory function to environmental filtering in natural ecosystems. These findings identify environmental sensing as an important organizational axis of bacterial trait-based ecology that integrates evolutionary history, ecological lifestyle, and adaptation to local conditions. Environmental sensing should be considered for predicting microbial niches and responses to environmental change.

ecology↗

bioRxiv2026

Coupling demography and interactions reshapes the structural stability of ecological communities

The assembly of ecological communities depends on both species' demography and the interactions among them. Ecological theory assumes that both ecological processes vary independently. However, empirical evidence demonstrates otherwise. How the structural stability of ecological communities changes when species' intrinsic growth rates and interactions are coupled remains unknown. To address this knowledge gap, we present several seemingly disconnected examples, such as the stress-gradient hypothesis, the covariance between environment and competition, higher-order interactions, and niche packing, that actually fall along a continuum of changes ranging from positive to negative interactions. Although the specific details of each scenario differ, they all share a common feature. When demography and interactions are coupled in multispecies models of population dynamics, the limits of structural stability in ecological communities shift non-linearly, and the domain of viability increases or decreases compared to a non-coupling scenario. We mathematically demonstrate that whether these changes are positive or negative depends on how the strength of intraspecific interactions shifts relative to interspecific interactions as species' growth rates increase. These new non-linear relationships imply two important shifts of thinking. Theoretically, the definition of structural stability must be generalized and its estimation requires the use of probabilistic measures based on ecological limits. Empirically, if abiotic and biotic barriers operate simultaneously, we need to account for such non-linear processes that can significantly modify population dynamics. Otherwise, we will not make accurate predictions of how ecological communities are assembled and biodiversity is maintained.

ecology↗

bioRxiv2020

Host immunity alters successional ecology and stability of the microbiome in a C. elegans model

A growing body of data suggests that the microbiome of a species can vary considerably from individual to individual, but the reasons for this variation - and the consequences for the ecology of these communities - remain only partially explained. In mammals, the emerging picture is that the metabolic state and immune system status of the host affects the composition of the microbiome, but quantitative ecological microbiome studies are challenging to perform in higher organisms. Here we show that these phenomena can be quantitatively analyzed in the tractable nematode host Caenorhabditis elegans. Mutants in innate immunity, in particular the DAF-2/Insulin Growth Factor (IGF) pathway, are shown to contain a microbiome that differs from that of wild type nematodes. We analyze the underlying basis of these differences from the perspective of community ecology by comparing experimental observations to the predictions of a neutral sampling model and conclude that fundamental differences in microbiome ecology underlie the observed differences in microbiome composition. We test this hypothesis by introducing a minor perturbation to the colonization conditions, allowing us to assess stability of communities in different host strains. Our results show that altering host immunity changes the importance of inter-species interactions within the microbiome, resulting in differences in community composition and stability that emerge from these differences in host-microbe ecology. ImportanceHere we use a Caenorhabditis elegans microbiome model to demonstrate how genetic differences in innate immunity alter microbiome composition, diversity, and stability by changing the ecological processes that shape these communities. These results provide insight into the role of host genetics in controlling the ecology of host-associated microbiota, resulting in differences in community composition, successional trajectories, and response to perturbation.

microbiology↗