Ecological Modelling

Using linear inverse ecosystem modeling as a data assimilation tool, we compare spawning grounds of Atlantic and Southern Bluefin Tuna (ABT and SBT, respectively) based on results from field campaigns in the Gulf of Mexico (GoM) and eastern Indian Ocean off northwest Australia (Argo Basin). Both regions are warm, stratified, low-nutrient waters dominated by cyanobacteria (Prochlorococcus). Despite these similarities, the Argo Basin is more productive, with [~]1.5X higher net primary production and nearly 2X higher production of top trophic levels in the model (tuna larvae, planktivorous fish, and predatory gelatinous zooplankton). Higher primary production in the Argo Basin is mainly driven by higher N2 fixation and storm mixing of new nutrients in the upper and lower euphotic zone, respectively. Increased ecosystem efficiency (secondary production of top trophic levels / primary production) results from differences in plankton food web organization. In the GoM, protistan zooplankton are the direct consumers of nearly all phytoplankton production. In contrast, higher rates of herbivory by crustaceans feeding on nanophytoplankton combines with a higher impact of appendicularians on cyanobacteria to convert plankton production into larval tuna prey more efficiently in the Argo Basin. Despite similarities in the proportions of phytoplankton production mediated by cyanobacteria and other picoplankton in both systems, food web pathways to larval tuna and other planktivorous fish are substantially shorter in the Argo Basin. Our results highlight the impact of distinct zooplankton ecological niches on ecosystem efficiency and suggest a need for better inclusion of plankton food-web structure in models simulating climate impacts on fisheries production. HIGHLIGHTSO_LIDeveloped food web models of tuna spawning habitat (Indian Ocean & Gulf of Mexico) C_LIO_LISpawning habitats in the Argo Basin and Gulf of Mexico (GoM) are both oligotrophic C_LIO_LIArgo Basin had higher net primary production in part as a result of nitrogen fixation C_LIO_LIArgo Basin had higher rates of direct herbivory by metazoan zooplankton C_LIO_LIThis resulted in greater ecosystem efficiency in the Argo Basin. C_LI

Wind has a strong influence on the flight characteristics, movements, energetics, demography, life-history traits and biogeography of flying animals. With climate change affecting atmospheric circulation patterns at different time scales, understanding the links between wind and animal movements is crucial for predicting its impact on flying biodiversity. Most studies on the relationship between wind and seabird movements have, however, focused on local scales, exploring birds perceptive sensitivity to local wind. In this study, we examine low-level wind pattern oscillations in the Southern Indian Ocean at multiple time scales to explain the local- to large-scale movements of the Amsterdam albatross. Adult individuals exhibited smooth trajectories, strongly correlated with seasonal, intra-seasonal or interannual wind oscillations. Conversely, younger individuals displayed more erratic and exploratory movements, often being swept away by eastward moving low-pressure systems at a synoptic time scale. Our results suggest that Amsterdam albatrosses can learn and adapt to the annual and monthly low-level wind climatology and interannual variability of the Southern Indian Ocean. This also highlights the importance of investigating seabird movements in relation to broader-scale wind patterns to support their conservation in a changing climate due to human activities. A robust assessment of regional circulation response to climate change for upcoming decades could help project the impact of climate change on seabird movements and mitigate its effects.

We developed an agent-based model (ABM) to assess how area-based controls on fishing methods can reduce fishing mortality and population declines. The model incorporates the behavior and distributions of dolphins and fishing vessels, and realistic displacement of fishing effort when protection is extended. Our case study is New Zealand dolphin - Hectors and Maui dolphins. The model was designed and calibrated using pattern-oriented modeling. Our results show that mortality due to entanglement in fishing gears has been reduced thanks to a gradual increase in dolphin protection. However, current protection is not as effective as previously thought, and scarce populations are negatively affected by Allee effects. Neither national nor international goals for reducing bycatch are met by current dolphin protection. The IUCN has recommended banning gillnet and trawl fisheries in New Zealand waters < 100m deep. For most New Zealand dolphin populations, this would be effective in achieving national and international goals for reducing bycatch. Only two populations would require additional protection. This modelling approach is also suitable for assessing impacts of bycatch and ship strikes for other marine species, making it suitable for informing management decisions in many regions.

The hockey-stick (HS) stock recruitment relationship (SRR) has been widely used as an empirical alternative to conventional SRRs such as the Beverton-Holt (BH) and Ricker (RI) models. However, the management performance and risks associated with estimating maximum-sustainable-yield (MSY) reference points (RPs) based on HS remain insufficiently understood. This study first defines deterministic and stochastic MSY RPs under the HS model and provides an overview of their properties. We then conduct simulation experiments to investigate the bias and management consequences that arise when MSY RPs are estimated from the HS model (HS-derived MSY RPs) rather than from the true SRR (e.g., BH) across a range of biological and stochastic parameters, with particular focus on scenarios with insufficient data contrast. Our results show that HS-derived MSY RPs tend to exhibit higher bias but lower variance than MSY RPs derived from the true SRR. Management strategy evaluation simulations further reveal that management procedures combining HS-derived MSY RPs with adaptive model learning and some precautionary measures gradually reduce this bias and achieve average spawning biomass and yield that are comparable to those obtained under management based on the true BH SRR. We also show that the management effectiveness of the precautionary measures depends on life-history traits and recruitment variability. These findings indicate that although HS-derived MSY RPs may be biased and require cautious use, combining them with appropriate precautionary measures allows management to remain robust while limiting variability and yield losses. This broadens the range of management options that are available for supporting sustainable fisheries management.

Fuel availability and fuel dryness are consistently shown to be the primary drivers of wildfire intensity and burnt area. Here we hypothesise that differences in the timing of fuel build up and drying determine the optimal time for wildfire occurrence. We use gross primary production (GPP) as a measure of biomass production and hence fuel availability, and vapour pressure deficit (VPD) as a measure of fuel drying. We use the phase difference in the seasonal time course and magnitude of GPP and VPD to cluster regions that should therefore have distinct wildfire behaviour. We then show that each of the resultant clusters is distinctive in terms of one or more fire properties, specifically number of ignitions, burnt area, size, speed, duration, intensity, and length of the wildfire season. The emergence of distinct regimes as a function of two biophysical drivers reflects the fact that both vegetation and wildfire properties are a consequence of eco-evolutionary adaptions to environmental conditions. We then examine the degree to which human activities or vegetation properties modify these fire regimes within each of these clusters. Variability in GPP and VPD largely explains the within-cluster variation in fire properties. The type of vegetation cover has an influence on burnt area and carbon emissions in particular, while human activities are more important for fire properties such as size, rate of spread and duration largely through their influence of landscape fragmentation. Although both human activities and vegetation properties modify wildfire regimes, the ability to distinguish wildfire regimes using GPP and VPD alone emphasizes that land management, fire use and fire suppression are constrained by environmental conditions. This eco-evolutionary optimality approach to characterising wildfire regimes provides a basis for designing a simple fire model for Earth System modelling.

Mediterranean Basin, one of the most important hot spots for reptiles, is also expected to experience significant impacts with climate change, posing a severe risk for the herpetofauna of the region. This study uses the snake-eyed lizard Ophisops elegans as a model organism to investigate the potential impacts of past and future climate change on reptile distributions in the region. An ecological niche model (ENM) was developed with the Maxent algorithm, with location points from GBIF and bioclimatic variables from the CHELSA dataset, then projected onto past LGM ([~]21 kya) and future (2071-2100 SSP3-7.0 and SSP5-8.5) scenarios. Results show that the present-day distribution of O. elegans is primarily driven by temperature seasonality and precipitation, indicating a preference for coastal Mediterranean climates with dry summers. The LGM projection suggests a fragmented and contracted range, confined to coastal refugia around the Mediterranean and Caspian Seas. Future projections for 2071-2100 show consistent and alarming contraction of suitable habitats under both SSP scenarios. In conclusion these findings indicate that O. elegans is vulnerable to significant habitat loss under projected climate change. This severe impact on a wide-spread species implies that the herpetofauna of the Mediterranean Basin may face a significant threat in future.

O_LISpecies diversity potentially has a dual effect on communities: a generally positive effect on overall community biomass, reflecting the expression of species response and interaction traits, and a poorly characterised effect on mass-specific species contribution to ecosystem functions, reflecting the expression of their effect traits. Disentangling the effects of biodiversity on total biomass from those on effect trait expression would help settle a long-standing debate by clarifying how biodiversity relates to both facets of species effects on ecosystem functioning. C_LIO_LIFollowing the classical BEF approach, we calculate expected ecosystem function based on observed functioning in monoculture. We then derive a net biodiversity effect (NBE) and decompose it into four components: the classical complementarity and selection effects on total community biomass, and complementarity and selection effects on effect trait expression. The latter two reflect, respectively, a complementarity or facilitation in how effect traits influence the function, and how species with the highest potential for increasing the function become dominant in the community. C_LIO_LIWe illustrate this NBE decomposition with three ecosystem functions (nitrogen retention capacity, soil hydraulic conductivity improvement, and forage digestibility) measured in assembled communities under controlled experimental conditions of perennial grassland plants. Regarding nitrogen retention, we find a positive complementary effect via total biomass, but a negative biodiversity effect via effect trait expression. For hydraulic conductivity improvement, biodiversity effects are mostly mediated by total biomass. As for forage digestibility, we found a positive complementarity effect on trait expression, outweighed however by a negative selection effect. This analysis reveals how biodiversity may have contrasting effects on ecosystem functions via its impact on biomass and effect trait expression. C_LI SynthesisSeparating between the effect of biodiversity on plant community biomass and on effect trait expression at the community level is one important step towards understanding the pathways by which diverse plant communities drive ecosystem functioning.

The Allee effect is a phenomenon where individual fitness is reduced in small populations, for example because of mate-finding difficulties or increased predation. Allee effects matter in conservation biology because they can drive small populations to extinction. The severity of Allee effects can depend on traits such as mate-search rate and defense against predators. Many natural populations exhibit considerable intraspecific trait variation (ITV) in such traits, but most studies so far assume these traits to be constant. Thus the impact of ITV on populations with Allee effect is largely unknown. Here we create two individual-based stochastic models that simulate a small population experiencing either a mate-finding Allee effect or a predator-driven Allee effect. We analyze how ITV, trait inheritance, and mutation affect the proportion of surviving populations. Under the mate-finding Allee effect, higher ITV hindered population survival and increased Allee thresholds. This can be explained by Jensens inequality and the negative curvature of the mate-finding function. Under the predator-driven Allee effect, ITV effects were weak, but higher mutation standard deviations were beneficial, likely because they provided more substrate for selection to act on. We thus recommend to take into account ITV when dealing with threatened populations with an Allee effect.

Peatlands are an important terrestrial carbon sink which, when drained, can produce substantial CO2 efflux. Low productivity forestry planted on drained peatlands can become a net carbon source if losses from drained soils exceed sequestration by the trees. Decision support tools which assist resource allocation and intervention planning in forest-to-bog restoration are needed to mediate this substantial environmental harm. Predicting carbon mitigation benefits associated with forest-to-bog restoration is a major challenge, however, due to the lack of long-term monitoring programs and the fact that mitigation times depend on processes distant from the intervention. Here we introduce the PEATREST life cycle assessment (LCA) which predicts carbon fluxes associated with forest-to-bog restoration, including due to processes far from restored sites. The LCA estimates mitigation timescales defined as the time following intervention at which the restored peatland is predicted to sequester or store more carbon than the forestry would have if retained. HighlightsO_LIHere we develop a novel forest-to-bog Life cycle assessment (LCA) tool C_LIO_LIThe LCA predicts carbon mitigation times following peatland restoration C_LIO_LIThe model combines a variety of process-based and empirical sub-models C_LIO_LIExample implementations for two different restoration scenarios are explored C_LIO_LISensitivity analysis highlights the model inputs that most impact outcomes C_LI Graphical abstract(A single, concise figure that serves as a visual summary of the main research findings described in your manuscript.) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/715261v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@f243f5org.highwire.dtl.DTLVardef@14bc4c7org.highwire.dtl.DTLVardef@164261borg.highwire.dtl.DTLVardef@1db3b_HPS_FORMAT_FIGEXP M_FIG The PEATREST Life cycle assessment (LCA) generates compound time series of carbon sequestration and carbon storage for two scenarios: the forest-to-bog peatland restoration (PR) and a counterfactual (CF) of forestry retention. By comparing the two scenarios, the LCA predicts the carbon mitigation timescales (vertical dashed lines). These are defined as the time following harvesting at which the peatland is predicted to sequester more (emit less), or to have stored more (lost less) carbon, than the forestry would have if retained. C_FIG

Recent progress in mathematical kinship modelling has allowed one to predict the probable numbers of kin for a typical population member. In the models, kin may be structured by age and sex, both in static or time-variant demographies. Knowing the probable numbers of kin in different stages - such as parity, health status, or geographic location - however, remains an open challenge in Kinship Demography. Knowing how population structure delimits kin to distinct stages is an advance - for instance, the probability of having one sister at home and one sister away has different social implications from the probability of having two sisters. We present a novel analytical framework, grounded in branching process theory, that provides kin-number distributions jointly structured by age and stage. Using recursive compositions of probability generating functions (PGFs), we derive the joint age, stage, and age x stage kin-number distributions. All marginal distributions over either dimension naturally emerge. Simple extensions of the PGF approach additionally yield: the joint distribution of an individuals own stage and their kins stage; the probable numbers of kin deaths, both in total and by generation number; and the probabilities of being kinless and/or orphaned. We demonstrate the framework through novel results in an application using UK parity-specific fertility and mortality data. HighlightsO_LIA new method calculates probability generating functions for the number of kin structured by age and stage C_LIO_LIThe model allows predicting the probable numbers of kin organised by age and stage C_LIO_LIRecursive nesting of probability generating functions in branching processes is used C_LIO_LIAn application is presented highlighting the novel results C_LI

Trees accumulate somatic mutations throughout their long lifespan, resulting in genetic mosaicism among branches. While recent genomic studies quantified these mutations, they were largely limited to describing static patterns of variation. In this study, we developed a mathematical model to infer the dynamic processes of somatic mutation accumulation from snapshot genomic data obtained from four tropical trees (Dipterocarpaceae), which dominate tropical rain forests in Southeast Asia. Our model focus on genetic differences between shoot apical meristems (SAMs) at branch tips and explicitly incorporate stem cell dynamics within SAMs during shoot elongation and branching, enabling us to quantify somatic genetic drift arising from stem cell lineage replacement. By comparing model predictions with empirical data from Dipterocarpaceae trees, we estimated key parameters governing stem cell dynamics and somatic mutation rates. Our results indicate that both shoot elongation and branching involve replacement of stem cell lineages, leading to a moderate degree of somatic genetic drift. Accounting for stem cell dynamics resulted in slightly lower mutation rate estimates than previous approaches that ignored these processes. Using the estimated parameters, we further performed stochastic simulations to predict patterns of somatic mutations, including features not directly observed in the sampled trees, such as occasional deviations of somatic mutation phylogenies from physical architecture. Together, our modeling framework provides insights into how genetic mosaicism is shaped within tropical trees and reveals the stem cell dynamics underlying their long-term growth and accumulation of somatic mutations. (236 words) Highlights- We built mathematical models to predict the genetic differences between branch tips by somatic mutations. - The model considers the varying dynamics of stem cells in shoot meristem during shoot elongation and branching. - We compared the model prediction with empirical data from tropical trees, Dipterocarpaceae, and estimated the dynamics of stem cells and mutation rate. - Somatic mutation dynamics were shaped by somatic genetic drift arising from stem cell lineage replacement during shoot elongation and branching. - Accounting for stem cell dynamics led to slightly smaller estimates of mutation rates compared with previous estimates that ignored the dynamics. - Our models offer insights into how genetic variability is shaped in the tropical trees and the stem cell dynamics underlying their long-term growth.

AimUnderstanding whether invasive species retain or shift their ecological niches has traditionally relied on scalar overlap metrics that quantify the magnitude of niche change, but not its structure. Here, we test whether biological invasions involve a reorganisation of the environmental axes along which native and invasive ranges are differentiated, and whether the dominant axes of this reorganisation are consistently associated with invasion pathway type (intercontinental vs. within-continent). LocationGlobal (North America, Europe, Africa, Asia, Australasia). Time periodContemporary (environmental variables representing long-term averages, 1980-2021). Major taxa studiedFreshwater crayfish (Decapoda: Astacidea): Procambarus clarkii, Faxonius limosus, Pacifastacus leniusculus, Faxonius virilis, Faxonius rusticus. MethodsWe analysed native and invasive occurrences for five globally important crayfish invaders using [~]400 hydrologically resolved environmental variables from the Global Crayfish Database of Geospatial Traits. Classification models were used to quantify environmental differentiation between native and invasive ranges, and feature contributions were aggregated by environmental domain (climate, topography, soil, land cover). Patterns were evaluated across intercontinental and within-continent invasion pathways and assessed for robustness using cross-validation, permutation tests, sample-size sensitivity, and comparisons with classical niche overlap metrics. ResultsNative and invasive occurrences were consistently distinguishable across all species (accuracy 96.5-99.9%). A pathway-dependent pattern emerged: intercontinental invaders were primarily differentiated along climatic dimensions (58-76% of model importance), whereas within-continent invaders showed a more balanced contribution of climatic and topographic variables ([~]42% each), including strong signals from river network position. This contrast was stable across cross-validation folds (SD < 1.6%), and supported by permutation tests (P = 0.001). Classical niche overlap metrics (Schoeners D = 0.30-0.62) did not capture this qualitative distinction. Main conclusionsBiological invasions involve not only changes in niche position but a reorganisation of the environmental axes that distinguish species distributions. Our results suggest that the dominant axes of this reorganisation differ systematically with invasion pathway, reflecting whether species encounter novel climatic regimes or primarily shift within existing climatic space along topographic and network-position gradients. By resolving which environmental dimensions underpin native-invasive differentiation, this approach provides a complementary perspective to scalar overlap metrics and a basis for more mechanistic interpretations of invasion processes.

Honeypot ants represent an example of convergent evolution, where a group of workers specialized in storing liquid food in their crops (i.e., stomach) has independently evolved multiple times across different ant genera. While seasonal resource scarcity and arid conditions are thought to drive the evolution of repletism, the role of environmental variables in this process has not been tested. With this is mind, species ensemble models were computed to assess suitability and richness areas, and the importance of predictors. Predictor importance was compared between genera and groups occupying a similar geographical area. Niche overlap and similarity between honeypot ant species were also evaluated to determine whether they occupy similar environmental spaces. Similarity was mainly found within genera, and Leptomyrmex and Myrmecocystus showed striking niche differences. Overall, Leptomyrmex distribution was mainly influenced by atmospheric bioclimatic variables like precipitation and temperature, while Myrmecocystus had soil bioclimatic variables as the most important predictors for their current distribution. Our results indicate that honeypot ants species currently do not occupy the same environmental space, and are not experiencing the same contemporary environmental stressors. While our results suggest that contemporary environmental factors cannot explain the convergence of honeypot ants, future research will examine past climatic conditions along with investigations into the ant genomes to understand more about the causes and consequences of the convergence.

Methods for estimating and modeling the long-term and short-term adult flight dynamics of the conifer silk moth Dendrolimus superans (Lepidoptera: Lasiocampidae) are examined. The analysis uses light trap adult catch data collected over 21 years, from 2005 to 2025. Three models of adult flight are considered: a flight-initiation model driven by weather factors, an autoregressive model of long-term catch dynamics, and a binary model of seasonal catch. For the flight-initiation model, we propose estimating the accumulated temperature sum ST from the date when the first derivative of the remote sensing vegetation index NDVI becomes positive until the date of the first adult capture of the season. ST is shown to be sufficiently stable across all years of observation, with flight each year beginning after this temperature sum is reached. The second model demonstrates that the long-term light trap catch time series is well described by a second-order autoregressive model AR(2), in which the catch of the current year depends on catches from the two preceding years. This long-term series is compared with a previously studied larval population density series of the Siberian silk moth; both are shown to be AR(2) series with similar coefficient values, which suggesting that adult catch data may serve as a proxy for absolute larval population density. In the third model, we describe the transition from absolute-scale seasonal catch dynamics (number of adults per day) to a binary scale (0, 1), where 0 denotes days on which no adults were attracted to the trap, and 1 denotes days on which at least one individual was captured. The seasonal absolute catch series is thereby transformed into a binary series of zeros and ones, and relationships between adjacent values in such a binary series are examined. A linear relationship between the absolute and binary seasonal dynamics series is demonstrated, making it possible to estimate absolute catches from binary catch values and to analyze seasonal flight in sparse pest populations. This potentially opens new avenues for understanding how outbreak populations function at chronically low density. Author summaryForest pests can cause catastrophic damage, yet predicting their outbreaks remains challenging. During periods of low population density, standard monitoring methods become labor-intensive and uninformative, while the transition to an outbreak often occurs unexpectedly. Using a 21-year dataset of adult Siberian silk moth (Dendrolimus superans) captures from light traps, we developed an approach combining three complementary models. First, we showed that moth flight begins upon reaching a specific temperature sum, with the starting point determined by NDVI vegetation index dynamics rather than a calendar date--making the forecast more ecologically relevant. Second, long-term adult population dynamics follow a second-order autoregressive model AR(2), matching the dynamics previously observed for larval populations. This establishes light trap data as a reliable proxy for absolute population density when ground surveys are impractical. Third, we introduced a method to analyze seasonal flight using binary data (presence/absence of moths per day), which we showed is linearly related to absolute abundance. This enables studying population dynamics during periods of extremely low density, when traditional methods fail. Our approach opens new possibilities for early warning systems to detect when a population risks transitioning from a latent state to an outbreak phase.

Marine protected areas (MPAs) are increasingly promoted as climate mitigation tools, yet guidance on their placement to maximize resilience against climate stressors like marine heatwaves remains limited. Here, we develop MPA placement guidelines that explicitly consider a mechanistic pathway through which MPAs could enhance kelp forest resilience to heatwaves: protecting fishery-targeted urchin predators to prevent kelp overgrazing. Using a spatially explicit, tri-trophic model of California kelp forests, we evaluate alternative MPA configurations across a hypothetical coastline where half the habitat experiences an increased probability of experiencing heatwaves. We found that effective MPA placement depends on whether MPAs are being newly established or reconfigured within an existing network, and that among-patch connectivity and spillover played vital roles in the relative effectiveness of different MPA configurations. Changes in resilience occurred primarily at the patch scale, with trade-offs between increased within-MPA resilience and decreased resilience in some fished areas, resulting in minimal coastwide population effects. For example, for new MPAs, large single MPAs within heatwave-prone areas maximized within-MPA resilience gains, while multiple small MPAs in heatwave refugia best supported whole-coast resilience. When reconfiguring established networks, expanding existing MPAs in refugia areas was most effective. We also demonstrate the importance of considering MPA recovery timescales: for example, relocating old MPAs to heatwave refugia yielded minimal short-term benefits due to the loss of rebuilt, previously fished, predator biomass. Our findings demonstrate that climate-adaptive marine planning should explicitly consider the spatiotemporal implications of trophic cascades, connectivity, and transient population dynamics to support ecosystem resilience.

Interactions among co-circulating parasite species influence infection risk and disease progression. Such interactions can occur within hosts, for example altering susceptibility, or indirectly through host demography or movement, potentially affecting landscape-scale transmission. Despite their ubiquity, the spatial implications of these interactions have received limited attention. We combine spatially-explicit modelling with laboratory experiments to investigate how different parasite-parasite interactions influence disease spread. We model within-host, demographic, and dispersal-related interactions across a linear landscape, showing that within-host interactions modifying host susceptibility have the strongest effects on parasite prevalence, spatial heterogeneity, and rate of spread. Furthermore, these effects are amplified when parasites invade sequentially, generating pronounced patch-level spatial priority effects. We tested these predictions experimentally using a protist host (Paramecium caudatum) and two bacterial parasites (Holospora undulata and H. obtusa). Consistent with model predictions, we found that H. obtusa reduces prevalence and spatial spread of H. undulata through reductions in host susceptibility, and found evidence for spatial priority effects, observing reduced H. undulata prevalence when introduced after H. obtusa. Our theoretical and experimental results highlight that parasite-parasite interactions can have important implications for parasite spatial epidemiology, but the magnitude of those effects depend on the interaction type and the timing of invasion.

Assessing the risk of pesticides to birds requires models that can extrapolate laboratory data to realistic exposure scenarios. In this work, we propose a new modeling framework BIRDkiss (Bird - Impact on Reproduction via Diet, keep it simple and suitable) that accounts for both a simplified Dynamic Energy Budget (DEBkiss) of organisms and the toxicokinetic-toxicodynamic (TKTD) of chemical substances according to a trait-based approach, thereby reducing the number of parameters to identify and strengthening the statistical robustness of the critical endpoints. The BIRDkiss model describes how food intake and toxicant exposure affect growth and egg production in birds over time. The model is fully embedded within an R package, including routines for calibration, validation and prediction under single-compound scenarios performed via Bayesian inference using standard data from the OECD avian reproduction tests. The BIRDkiss model also allows the simulations of scenarios under both varying food availability and multi-compound exposures based on the two classical mixture-toxicity paradigms: Concentration Addition (CA) and Independent Action (IA). The results of calibration for single compounds show good results matching with observed weights and egg counts. From these calibrations, predictions for new exposure scenarios can be readily generated. For mixtures, the IA algorithm is simpler and does not require to scale variables as in CA. Simulations indicate that high food levels do not further increase egg production (saturation), whereas substantial food reductions markedly decrease reproduction because energy is reallocated to maintenance. Exposure to chemicals combined to low food availability amplify the decline in reproductive output. The ready-to-use mechanistic, open-source BIRDkiss tool enables predicting the impact of pesticides on avian reproduction under realistic dietary exposure profiles. The implementation of CA and IA models is a first step toward mechanistic assessment of chemical mixtures, although validation still requires empirical mixture data. The model highlights the importance of food availability and shows that chemical stress can exacerbate the negative effects of nutritional stress. Integrating such models into regulatory frameworks could improve the ecological relevance of risk assessments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/712277v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@102246dorg.highwire.dtl.DTLVardef@1a58f65org.highwire.dtl.DTLVardef@695cd7org.highwire.dtl.DTLVardef@14e4329_HPS_FORMAT_FIGEXP M_FIG C_FIG

Despite its ubiquity in natural flows, the effects of turbulence on fish locomotion and behavior remain poorly understood. The prevailing hypothesis is that these effects depend on the spatial and temporal scales of the turbulence relative to the fishs size and swimming speed. But in conventional facilities, turbulence usually increases with mean flow, which forces higher swimming speeds and can leave these relative scales unchanged. We therefore present a novel experimental facility that leverages a jet array to decouple the turbulence from the mean flow and systematically control its scales. This approach allows the ratio of turbulent to fish inertial scales to be varied over an order of magnitude, providing a controlled framework for quantifying fish-turbulence interactions. The facility also supports experiments probing strategies fish may use to cope with turbulence, including collective behaviors. Insights from this work have broader implications for ecological studies and engineering applications, including the design of effective fishways and bio-inspired underwater vehicles.

Cyanobacterial colonies often exploit their buoyancy and large size to float upwards rapidly and form dense surface blooms, which can degrade water quality, threaten ecosystems and public health, and impose substantial economic costs. Yet, how the morphology of cyanobacterial colonies controls their vertical velocity remains poorly understood. We conducted detailed three-dimensional morphological characterization of colonies of the cyanobacterium Microcystis in lake samples at the single-colony level and performed controlled flotation experiments in stratified flows. Using particle tracking in a vertical density gradient, we separately quantified the contributions of colony shape and buoyant density at the level of individual colonies. Our results show that the shape factor in Stokes law varies systematically with colony size. Consequently, the vertical velocity of colonies does not scale with the square of colony size but only with a power of 1.13, as larger colonies have a more irregular shape and therefore experience enhanced drag. We therefore correct the commonly used Stokes law to account for the size-dependent change in the shape factor. Interestingly, implementation of this power law relationship in a vertical migration model shows widespread chaotic dynamics in the migration trajectories of Microcystis colonies. These results highlight the importance of morphological plasticity in cyanobacterial colonies and can inform predictive models and hydrodynamic control strategies for toxic blooms. Our methodology to simultaneously determine the density, shape factor and velocity is broadly applicable to suspended aggregates with complex shapes in freshwater and marine systems.

The yellownose skate (Dipturus chilensis) is an endangered skate with a narrow distribution in the southeastern Pacific, facing intense fishing pressure and potential climate threats. Using a species distribution model, we projected the current and future distribution of D. chilensis under contrasting climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for mid-century (2050) and end-of-century (2100). Our models, which demonstrated robust predictive performance significantly better than random expectations, identified maximum temperature and minimum oxygen as the primary environmental drivers of habitat suitability. Projections revealed a consistent poleward range shift towards the Channels and Fjords of Southern Chile ecoregion across all scenarios. While localized habitat loss was projected in Central Chile and Araucanian ecoregions, particularly under high emissions (SSP5-8.5), these losses were outweighed by southern expansions, leading to a net increase in total suitable habitat by 2100. These findings underscore the critical need for climate-adaptive management strategies, including the protection of emerging southern refugia and dynamic fisheries regulations, to ensure the long-term persistence of D. chilensis.