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bioRxiv2023

The historical ecological background of West Nile virus in Portugal provides One Health opportunities into the future

West Nile (WNV) is a zoonotic arbovirus with an expanding geographical range and epidemic activity in Europe. Not having yet experienced a human-associated epidemic, Portugal remains an outlier in the Mediterranean basin. In this study, we apply ecological niche modelling informed by WNV historical evidence (1969-2022) and a multitude of environmental variables from across Portugal. We identify that ecological backgrounds compatible with WNV historical circulation are mostly restricted to the south, characterized by a warmer and drier climate, high avian diversity, specific avian species and land types. We estimate WNV ecological suitability across the country, identifying overlaps with the distributions of the three relevant hosts (humans, birds, equines) for public and animal health. From this, we propose a category-based spatial framework providing first of a kind valuable insights for future WNV surveillance under the One Health nexus. We also forecast that climate trends alone will contribute to pushing adequate WNV ecological suitability northwards, toward regions with higher human density. This unique perspective on the past, present and future ecology of WNV addresses existing national knowledge gaps, enhances our understanding of the evolving emergence of WNV, and offers opportunities to prepare and respond to the first human-associated epidemic in Portugal.

ecology↗

bioRxiv2024

EcologicalNetworksDynamics.jl: A Julia package to simulate the temporal dynamics of complex ecological networks

O_LISpecies interactions play a crucial role in shaping biodiversity, species coexistence, population dynamics, community stability and ecosystem functioning. Our understanding of the role of the diversity of species interactions driving these species, community and ecosystem features is limited because current approaches often focus only on trophic interactions. This is why a new modelling framework that includes a greater diversity of interactions between species is crucially needed. C_LIO_LIWe developed a modular, user-friendly, and extensible Julia package that delivers the core functionality of the bio-energetic food web model. Moreover, it embeds several ecological interaction types alongside the capacity to manipulate external drivers of ecological dynamics like temperature. These new features represent important processes known to influence biodiversity, coexistence, functioning and stability in natural communities. Specifically, they include: a) an explicit multiple nutrient intake model for producers, b) competition among producers, c) temperature dependence implemented via the Boltzmann-Arhennius rule, and d) the ability to model several non-trophic interactions including competition for space, plant facilitation, predator interference and refuge provisioning. C_LIO_LIThe inclusion of the various features provides users with the ability to ask questions about multiple simultaneous processes and stressor impacts, and thus develop theory relevant to real world scenarios facing complex ecological communities in the Anthropocene. It will allow researchers to quantify the relative importance of different mechanisms to stability and functioning of complex communities. C_LIO_LIThe package was build for theoreticians seeking to explore the effects of different types of species interactions on the dynamics of complex ecological communities, but also for empiricists seeking to confront their empirical findings with theoretical expectations. The package provides a straightforward framework to model explicitly complex ecological communities or provide tools to generate those communities from few parameters. C_LI

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bioRxiv2024

The development of ecological systems along paths of least resistance

A long-standing question in biology is whether there are common principles that characterize the development of ecological systems (the appearance of a group of taxa), regardless of organismal diversity and environmental context. Classic ecological theory holds that these systems develop following a sequenced orderly process that generally proceeds from fast-growing to slow-growing taxa and depends on life-history trade-offs. However, it is also possible that this developmental order is simply the path with the least environmental resistance for survival of the component species and hence favored by probability alone. Here, we use theory and data to show that the order from fast-to slow-growing taxa is the most likely developmental path for diverse systems when local taxon interactions self-organize to minimize environmental resistance. First, we demonstrate theoretically that a sequenced development is more likely than a simultaneous one, at least until the number of iterations becomes so large as to be ecologically implausible. We then show that greater diversity of taxa and life histories improves the likelihood of a sequenced order from fast-to slow-growing taxa. Using data from bacterial and metazoan systems, we present empirical evidence that the developmental order of ecological systems moves along the paths of least environmental resistance. The capacity of simple principles to explain the trend in the developmental order of diverse ecological systems paves the way to an enhanced understanding of the collective features characterizing the diversity of life.

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bioRxiv2025

A general framework for cycles in ecology

Theory predicts that indirect interactions in ecological networks sustain species diversity through oscillatory dynamics. However, a framework linking interaction structure to these cycles is lacking. We develop an analytical toolbox combining assembly graphs with a mathematical decomposition of interaction matrices into symmetric and anti-symmetric components. We find that assembly cycles--closed loops of species invasions--are suppressed when symmetric interactions dominate, reflecting strong intraspecific competition. Conversely, anti-symmetric dominance, indicating competitive asymmetries, leads to cycles like rock-paper-scissors and novel multispecies invasion patterns. As asymmetries increase, new cycles emerge involving both sequential and simultaneous invasions. Applying this approach to 21 plant community interaction matrices, we find few cycles due to prevalent self-limiting effects. Our work clarifies when indirect interactions drive cycles and introduces a simple ratio assessing symmetric versus anti-symmetric contributions, constraining cycle emergence, and species coexistence in nature. Significant statementUncertainties of the fundamental blocks that maintain biodiversity hinder our understanding of the dynamics we can observe in ecological communities. By applying a well-known mathematical approach to decomposing interactions among species within ecological communities, we discover great potential for observing rock-paper-scissors and more complex cyclic dynamics in nature. However, we predict and confirm analyzing multiple datasets that cyclic dynamics are rare because of two phenomena that pervade ecological communities. These are differences in species performance and the dominance of self-limiting effects. Our framework underscores the importance of studying simple properties of biotic interactions to predict complex ecological dynamics.

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bioRxiv2025

Extracting massive ecological data on state and interactions of species using large language models

The contemporary ecological crisis calls for integration and synthesis of ecological data describing the state, change and processes of ecological communities. However, such synthesis depends on the integration of vast amounts of mostly scattered and often hard-to-extract information that is published and dispersed across hundreds of thousands of scientific papers, for example describing species-specific interactions and trophic relationships. Recent advancements in natural language processing (NLP) and in particular the emergence of large language models (LLMs) offer a novel, and potentially revolutionary solution to this persistent challenge, for the first time creating the opportunity to access and extract virtually all data ever published. Here, we demonstrate the transformative potential of LLMs by extracting all types of biological interactions among species directly from a corpus of 83,910 scientific articles. Our approach successfully extracted a network of 144,402 interactions between 36,471 taxa. Performance analysis shows that the model exhibits a high sensitivity (70.0%) and excellent precision (89.5%). Our approach proves that LLMs are capable of carrying out complex extraction tasks on key ecological data on a very large scale, paving the way for a multitude of potential applications in ecology and beyond.

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bioRxiv2025

Advancing ecological assessment: The integration of eDNA metabarcoding into an estuarine fish index

In the face of increasing anthropogenic pressures on estuarine ecosystems, methods to efficiently and reliably assess their ecological status are essential. This study explores the integration of environmental DNA analysis into the AZTIs Fish Index (AFI) to assess ecological status of estuarine ecosystems. Surface water eDNA sampling and bottom trawl surveys were performed across multiple estuaries in the Basque Country, Spain, and resulting species data were used to calculate AFI scores. eDNA metabarcoding consistently detected higher fish species richness than bottom trawling, while the latter remained more effective at capturing demersal species. In general, ecological classifications from eDNA- and bottom trawl derived data displayed low concordance, largely due to differing species assemblages and metric contributions. These results emphasize the respective strengths and weaknesses of each methodology and the necessity for method-specific calibration. Considering that the AFI is calibrated using bottom trawl data, its direct application to eDNA-derived species lists may lead to some inconsistencies. This study underscores the critical necessity to establish eDNA-specific reference conditions and to recalibrate index thresholds accordingly. While eDNA approach may not entirely replace traditional methods, its scalability, sensitivity, and minimal ecological disturbances establish it as an essential complementary application within estuarine assessment programs. This research strongly supports the urgent advancement of eDNA-based indices and the critical enhancement of reference conditions for their effective incorporation into ecological assessment frameworks under the Water Framework Directive.

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bioRxiv2025

Factors Influencing Emerald Ash Borer Ecological Interactions

Emerald ash borer beetles (Agrilus planipennis) in North America are a destructive invasive species that increase tree mortality continent-wide, resulting in major ecological and economic impacts. Trees that are infested experience mortality rates which can exceed 99%, disrupting ecological communities and threatening the $218 billion forestry industry in North America. Given the ecological and economic impact of these pests, we seek to identify biological interactions and gain a better understanding of what ecological factors might influence these relationships. We use DNA metabarcoding from multiple markers to analyze the fungal, parasitic, plant, and microbial interactions of these beetles, and assess the relative importance of life stage (e.g., larvae, pupae, and adults), collection location, habitat, and date on the detection of ecological interactions. We detected 30 different taxonomic orders including 29 order-level interactions in larva1-stage individuals (3 animal, 17 bacteria, and 9 fungi), 64 in larva2-stage individuals (8 animal, 24 bacteria, and 32 fungi), 10 in larva3-stage individuals (2 animal, 3 bacteria, and 5 fungi), 74 in the pupae (5 animal, 31 bacteria, and 38 fungi), and 82 in the adult beetles (4 animal, 48 bacteria, 29 fungi, and 1 parasitic alveolate). These detections include several likely agents of biocontrol including the known commercially available Beauveria fungus, and several potential parasites including Wolbachia and ichneumonid wasps. A random forest model suggests the detection of interactions is best predicted by collection date and life stage, with interactions more likely to be detected in pupal samples which may be the ideal target for future analysis, where cost and time constraints prevent the more thorough analysis of all life stages.

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bioRxiv2025

Sensitivity analysis of factors influencing the ecology of mosquitoes involved in the transmission Rift Valley fever virus transmission

Vector-borne diseases are a major global health concern, with Rift Valley fever (RVF) serving as a key example due to its impact on both human and animal health. Predicting and controlling such diseases requires understanding how environmental factors shape mosquito ecology. Due to mosquito abundance, distribution, and behavior being influenced by ecological conditions, identifying the drivers of these dynamics is essential for anticipating transmission risk. This study aims to assess the sensitivity of ecological factors governing the ecology of mosquitoes, using a deterministic, compartmental model of RVF transmission in Kenya. We conducted a local and global sensitivity analysis on ten model parameters: four species-specific parameters for each of the two mosquito species of interest (Culex spp. and Aedes spp.) and two parameters shared across species. The focal species-specific parameters were the area scanned before oviposition, the proportion of area suitable for egg laying, the number of eggs laid, and the maximum density of eggs. The two shared parameters represented livestock population size and the influence of livestock on vector fecundity and gonotrophic cycles. Parameter ranges and distributions were defined using a scoping literature review. Sobol sensitivity analysis was performed under two environmental scenarios: (i) constant temperature and water body area, and (ii) periodically varying temperature and water body area, implemented using a time-dependent Sobol framework. Results revealed species-specific differences in parameter influence. For Culex spp., uncertainty in the area scanned for oviposition was highly influential, while for Aedes spp., the pairing of the proportion of area that eggs are laid on and the maximum density of eggs emerged as dominant. These findings highlights the need for improved empirical data on spatial oviposition patterns across water bodies, as current evidence remains limited. By identifying the ecological parameters that most critically shape mosquito population dynamics and/or influence model outputs, and thereby the transmission potential of RVF, this work supports more targeted vector surveillance and strengthens public health decision-making in RVF-endemic regions. Author summaryRift Valley fever is a disease spread by mosquitoes that affects both people and animals. Understanding what drives mosquito populations can help us predict and control the spread of Rift Valley fever. In this study, we used a published mathematical model to conduct a sensitivity analysis to explore how different parameters within the model affect mosquito numbers. In total, we investigated ten parameters, where the analysis emphasizes the role of water bodies used by mosquitoes to lay their eggs. We found that this factor plays a major role in how many mosquitoes are present. However, there is still a lot we do not know about exactly where mosquitoes choose to lay their eggs. More research in this area could improve how we predict and manage Rift Valley fever and other mosquito-borne diseases outbreaks. Our findings highlight which environmental details matter most, and they can help guide better strategies for mosquito control and disease prevention.

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bioRxiv2025

Expert-led priorities for a response diversity research agenda in ecology

Response diversity aims to capture and explain the variation in ecological responses to environmental change. Response diversity is expected to drive ecological stability since a wider variety of responses to one or more environmental factors should stabilise fluctuations of ecosystem functions. However, uptake of empirical response diversity research has been slow. Here we assess current thinking around response diversity by conducting a targeted expert survey of response diversity researchers. Our survey revealed that one barrier to a unified research agenda on response diversity is the lack of agreement among respondents on the definition of response diversity, and to which dimension(s) of ecological stability response diversity might relate. When asked to select the temporal, spatial, and biological scales at which response diversity may be most relevant for ecological stability, respondents chose a wide range of scales indicating differences in how experts view response diversitys stabilising effect. Respondents considered studies incorporating both biotic interactions and abiotic environmental responses to be especially challenging, as were those thinking about responses to multiple environmental changes simultaneously. Moreover, respondents thought inconsistencies in the definitions of, and methods for measuring, response diversity were a major challenge facing the field. Despite these barriers, the survey revealed a desire for globally coordinated research efforts on response diversity in the form of syntheses, workshops, and distributed experiments, but that a standardised response diversity metric across diverse use-cases could be too restrictive. Our findings suggest we can shift response diversity from a loose collection of conceptual studies and inconsistent empirical applications towards a quantitative and coordinated research programme mechanistically linking biodiversity and ecological stability. As such, we are launching the Response Diversity Network--a research community interested in the science and application of response diversity-- whose activities we hope will benefit both individual studies of response diversity and globally coordinated research efforts.

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bioRxiv2025

Experimental community ecology in decline: A call to embrace technology

Community dynamics are complex and thus challenging to infer from observational data alone. Experiments, with their ability to control variables and isolate mechanisms, are a powerful tool for uncovering the causal processes that drive community dynamics. They therefore allow us to move beyond correlations and to directly test theoretical predictions. Yet, because experiments are often logistically demanding and resource-intensive, they are less frequently employed than observational approaches in community ecology. Here, we trace the past three decades of experimental research in community ecology through a systematic literature review. We focus on the motivation behind experiments, their links to ecological theory, the types of questions they address, their scale, and the methods used to do this. Our results corroborate the historically tight relationship between experiments and ecological theory and document a gradual increase in experimental complexity --particularly related to the use of molecular methods. However, persistent gaps remain in the taxa and ecosystems studied, with aquatic ecosystems, fungi, and microbes still underrepresented compared to terrestrial plants and animals. Moreover, experiments are still limited in their spatial and temporal scale; they are typically short-term, local, and reliant on manual methods. The integration of high-throughput technologies with experimental workflows is still in its infancy, even though they are increasingly common in biomonitoring. To illustrate the potential of such tools in experimental research, we present a proof-of-concept study. It shows how automated technologies can be incorporated at different stages of the experimental workflow to expand the scale of experiments while reducing the reliance on human labor and potentially lowering financial costs. We conclude that many of the long-lasting biases and challenges in experimental community ecology could be addressed by combining technological innovations with broader collaboration among research groups. Coordinated networks, standardized protocols, and the integration of long-term and large-scale experimental designs can substantially improve in situ replication as well as cross-site comparability. Such efforts are essential for developing a more comprehensive mechanistic understanding of community dynamics across diverse ecosystems.

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bioRxiv2025

Crimean-Congo Haemorrhagic Fever Virus Circulates within Broad Ecological Networks of Ticks and Vertebrates

We produced spatial datasets of the known distribution of Orthonairovirus haemorrhagiae (formerly Crimean-Congo haemorrhagic fever virus, CCHFV) by compiling human cases, virus isolations, and serological data from humans and animals, spanning from Europe to southern Africa, with the aim on virus range modelling. Models based solely on climatic variables produced unrealistic and overly extensive predictions, overestimating suitability in northern Europe and confirming that climate alone poorly explains CCHFV range. Approaches using only tick species distributions underperformed in many parts of Africa, reflecting the complexity of vector-host interactions and the absence of a strict tick-virus association. The integration of human-biting tick distributions, livestock density, and chorotypes yielded the most robust results, accurately capturing over 90% of known occurrences, by identifying vertebrate assemblages most likely to amplify the virus while reducing dimensionality. Chorotypes, representing clusters of co-occurring hosts, enhanced model performance and improved the delineation of both the northern and African ranges limits of the virus. Our results support a generalist epidemiological model in which multiple tick species, together with a broad range of vertebrate hosts, sustain CCHFV circulation. This ecological flexibility, rather than strict vector specificity, likely explains the CCHFV wide biogeographical range and occasional lineage expansions across continents. This study provides the most comprehensive assessment to date of the abiotic and biotic determinants of the distribution of CCHFV. Although gaps remain, this study demonstrates that coupling abiotic and biotic predictors provides a more accurate and ecologically meaningful understanding of CCHFV distribution. Author SummaryCrimean-Congo haemorrhagic fever virus (CCHFV) is a tick-borne pathogen causing severe disease in humans across Africa, Asia and Europe. Understanding where the virus may circulate is crucial for anticipating outbreaks and guiding surveillance. Previous studies have mostly relied on climate-based models, assuming that temperature and humidity determine where infected ticks can prevail. However, such models often overestimate risk, predicting virus presence in regions where no cases or vectors exist. In this study, we combined climatic data with ecological information on ticks, livestock, and wildlife to create a more realistic prediction of CCHFV distribution. By incorporating host-vector interactions and vertebrate communities, our models accurately captured most known occurrences and revealed the ecological patterns underlying virus transmission. Our results suggest that CCHFV persists within broad networks of tick and vertebrate species rather than a single vector. This integrated approach offers a powerful framework to identify emerging risk areas and forms a good starting point to further improve our understanding of the ecological drivers of tickborne viral diseases.

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bioRxiv2018

Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect

O_LIGlobally, protected areas are being established to protect biodiversity and to promote ecosystem resilience. The typical spatial conservation planning process leading to the creation of these protected areas focuses on representation and replication of ecological features, often using decision support systems such as Marxan. Unfortunately, Marxan currently requires manual input or specialised scripts to explicitly consider ecological connectivity, a property critical to metapopulation persistence and resilience.\nC_LIO_LI\"Marxan Connect\" is a new open source, open access Graphical User Interface (GUI) designed to assist conservation planners in the systematic operationalization of ecological connectivity in protected area network planning.\nC_LIO_LIMarxan Connect is able to incorporate estimates of demographic connectivity (e.g. derived from tracking data, dispersal models, or genetics) or structural landscape connectivity (e.g. isolation by resistance). This is accomplished by calculating metapopulation-relevant connectivity metrics (e.g. eigenvector centrality) and treating those as conservation features, or using the connectivity data as a spatial dependency amongst sites to be included in the prioritization process.\nC_LIO_LIMarxan Connect allows a wide group of users to incorporate directional ecological connectivity into conservation plans. The least-cost conservation solutions provided by Marxan Connect, combined with ecologically relevant post-hoc testing, are more likely to support persistent and resilient metapopulations (e.g. fish stocks) and provide better protection for biodiversity than if connectivity is ignored.\nC_LI

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bioRxiv2019

Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale

Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual); or (2) using remote sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: 1) image segmentation, to identify individual trees and estimate structural traits; 2) ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and 3) predictions for segmented crowns for the full remote sensing footprint at the NEON sites. The R2 values on held out test data ranged from 0.41 to 0.75 on held out test data. The ensemble approach performed better than single partial least squares models. Carbon performed poorly compared to other traits (R2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over-segmentation. The pipeline produced good estimates of DBH (R2 of 0.62 on held out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R2 on test data of between to 0.26. We used the pipeline to produce individual level trait data for [~]5 million individual crowns, covering a total extent of [~]360 km2. This large dataset allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.

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bioRxiv2019

Integrating Economic dynamics into Ecological Networks: The case of fishery sustainability

Summary/AbstractUnderstanding and sustainably managing anthropogenic impact on ecosystems requires studying the integrated economic -ecological dynamics driving coupled human-natural systems. Here, we expand ecological network theory to study fishery sustainability by incorporating economic drivers into food-web models to evaluate the dynamics of thousands of single-species fisheries across hundreds of generated food-webs and two management strategies. Analysis reveals harvesting high population biomass species can initially support fishery persistence, but threatens long term economic and ecological sustainability by indirectly inducing extinction cascades in non-harvested species. This dynamic is exacerbated in open access fisheries where profit driven growth in fishing effort increases perturbation strength. Results demonstrate the unique insight into both ecological dynamics and sustainability garnered from considering economically dynamic fishing effort in the network. One Sentence SummaryIntegrating economic drivers into ecological networks reveal non-linear drivers of sustainability in fisheries.

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bioRxiv2025

Virtual aquatic ecology: stepped simulations of gross production within constraints explain biomass changes

A simulator, ECOLPS in R, is developed and trialed for ecological studies of closed aquatic ecosystems. Its constraint-based approach contrasts with function-based models widely applied in ecology. Total gross production ({Sigma}GP) by wild components (= species/life stages, grouped by ecological roles) is maximized within constraints over short time steps using linear programming, thereby enabling simulations of opportunistic growth and harvesting by competing components. Constraints use integrated terms from an [n]-component generalization of the Lotka-Volterra (LV) predator-prey model, and from mass-based models for fisheries, non-living organics, nutrients and essential habitats. Seasonality uses programmed temperature and light indices. Trial simulations, total 12, sought first to confirm conformance with LV theory. Further trials of gradually increasing ecosystem complexity were designed to simulate wellknown ecological events. They found biomass oscillations depending on starting biomasses and seasons, predator satiation, competitive exclusion, predator diets dependent on available prey, instability of unconstrained 3-level food chains, limitation of GP by essential habitats and nutrients, recycling of nutrient and biomass, trophic cascades and wasp-waist systems caused by fishing, and seasonal succession. Step-wise studies of simulated series explained results. A constrained-GP hypothesis is proposed. Priorities for further developments are suggested. ECOLPS simulations could support cause-and-effect investigations, field work, and aquatic ecological risk assessments.

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bioRxiv2026

Ecological interactions mediate evolutionary responses to temperature in microbial communities

Microbial populations play a pivotal role in ecosystem-level responses to rising temperatures and both their ecology and evolution can be directly influenced by warming. However, predicting microbial evolution and its ecological consequences is challenging because different genotypes within a population might respond uniquely to shifts in the abiotic and biotic environment. To understand how, we quantified evolutionary and ecological responses across temperatures in a protist of wide geographic distribution in the presence and absence of other microbial species with whom they interact (i.e., heterospecifics). In the absence of heterospecifics, we found that intraspecific interactions and warming selected in favor of a particular genotype, reducing genotypic diversity. In the presence of heterospecifics, 1) genetic diversity was further reduced under warming, resulting in temperature-dependent selection; but, 2) the magnitude of this change depended on the sign (+, 0, -) of the net ecological effect on the focal species by the heterospecifics, and this effect was itself temperature-dependent. Together, our results demonstrate that both intra- and interspecific interactions can mediate how temperature shapes microbial population rapid evolutionary responses, underscoring the importance of the ecological context in predicting evolutionary outcomes under climate change.

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bioRxiv2026

Transporting Causal Effects in Ecology: Concepts, Models and Software

O_LIStatistical methods related to causal inference are fundamental in ecological research as ecologists often deal with causal research questions. Consequently, recent years have seen an increase in articles discussing causal inference in ecological context. However, generalizing causal findings across ecological systems that differ in environmental context still remains a challenge. While we may assess causal relationships in one location or population from experimental or observational data, replicating these findings in different settings can be impractical, expensive, or sometimes impossible. C_LIO_LIWe introduce causal effect transportability to ecological research - a formal framework for transferring causal effects assessed in one domain (the source) to estimate outcomes in different domain (the target), where broader data collection may be infeasible. Using structural causal models, this framework provides formal criteria for determining when causal effects can be validly transferred between populations and derives appropriate statistical adjustment formulas when the transportation is possible. Recent algorithmic developments, implemented in accessible R software packages, automate the mathematical derivations and make transportability analysis more practical for ecologists. C_LIO_LIWe demonstrate the framework through a case study examining the effect of tree canopy cover on dissolved oxygen concentrations across different watersheds. We succeed to show that transported estimates outperform naive applications of source population models. C_LIO_LICausal effect transportability offers critical tools for predicting ecological responses across heterogeneous settings, with particular relevance when experimental replication is constrained by cost, ethics, or urgency, and when management decisions require extrapolating findings to novel environmental contexts. C_LI

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bioRxiv2026

Integrating social-ecological dimensions of fisheries non-compliance in a stochastic framework

Non-compliance with regulations threatens the sustainability of fisheries worldwide. Understanding the interconnected feedbacks of this complex social-ecological problem is key for sustainability but rarely integrated into fisheries management. We provide an adaptive stochastic modelling framework that integrates economic, social behavior, and ecological aspects of the Chilean kelp fishery, which plays a critical economic and ecological role in coastal social-ecological ecosystem. High levels of non-compliance is threatening sustainability, fishers well-being, and ecosystem health. Our model considers inherent environmental uncertainties and enables the assessment of different harvesting and compliance scenarios and the role of market-based economic incentives in reducing non-compliance. Results show that, unlike the sustainability obtained under an idealized full-compliance scenario, under dynamic compliance the social, economic, and ecological feedbacks leads to system collapse. Importantly, price premiums can promote compliance and sustainability, but the probability of collapse, albeit small, still exist. Our generalizable stochastic modeling framework evidenced that accounting for inherent uncertainty in natural resource management is key to designing interventions for sustainability.

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