Biology subjects

Salguero-Gomez, R.

Publications and source records attributed to Salguero-Gomez, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Elevational filtering reshapes mountain understory communities through diversity decline, functional shifts, and species replacement

Mountain understory communities are structured along elevational gradients, as environmental constraints filter which species and ecological strategies can persist and where. Yet, how community differentiation, local diversity, functional strategies, and species replacement are shaped by elevational gradients remains poorly resolved. Here, we examine 107 herbaceous species across 27 sites spanning 2,202-3,696 m a.s.l. and three elevational zones. We determine how elevation reorganizes multiple dimensions of community assembly. We hypothesize that increasing elevation differentiates community composition, reduces -diversity, favors persistence-oriented strategies, and drives {beta}-diversity primarily through species turnover, rather than nestedness. We test our hypotheses by integrating taxonomic, functional, and {beta}-diversity patterns across the elevational gradient. A coherent pattern of community reassembly emerges as elevation increases: communities become increasingly distinct with elevational separation and locally less diverse toward higher elevations. Functional composition changes concurrently, with perennial and chamaephytic groups increasing as annual and therophytic groups decline. We find that increasing community differentiation primarily reflects species replacement rather than progressive species loss. Species turnover accounts for 97.95% of total Sorensen {beta}-diversity and increases with elevational separation, whereas nestedness declines. Our findings therefore show that elevation reorganizes herbaceous understory communities through linked changes in diversity, functional strategies, and species identity. Indeed, environmental filtering favors persistence-oriented strategies while progressively replacing species across elevations, linking taxonomic and functional differentiation within a common process of mountain understory community assembly.

ecology↗

bioRxiv2026

Sensitivity of stochastic growth rate and lifetime reproductive output to kin effects: A nonlinear feedback model

Humans exhibit a coevolutionary relationship between the trajectory of social evolution and senescence. A focal individual's kinship network is a function of his/her life history, while that network in turn shapes the focal individual's life history through kin effects on reproduction, generating a dynamic, nonlinear feedback loop across the population. Assessing directional selection on kin effects, therefore, requires explicitly modelling the feedback between the kinship network and life history. Here, we develop such a framework and apply it to examine selection pressures on the cooperative behaviour of closely related kin, including mothers, grandmothers, and older sisters. We hypothesise that the negative association of grandmothers, mothers, and older sisters (>10 years) with child mortality are under positive selection because increasing the absolute values of these kin effects would increase the stochastic growth rate. Furthermore, we hypothesise that the positive association of older sisters with child mortality is subject to negative selection, as increasing this effect would reduce the stochastic growth rate. We estimate kin-specific slope coefficients for child mortality using a Bayesian hierarchical model and solve a system of nonlinear equations linking kinship structure and survival to account for the nonlinear feedback between kinship and child survival. Thereafter, we compute stochastic sensitivities of both the stochastic growth rate and the mean lifetime reproductive output to these slope coefficients. Consistent with our hypotheses, all slope coefficients for kin presence on child mortality are negatively sensitive to both fitness metrics. We discuss the applications of our framework to examine the coevolution of social behaviours and senescence in age-structured populations.

evolutionary biology↗

bioRxiv2026

Wildlife depredation stabilizes ecological-economic systems and can lead to improved economic outcomes

Human-wildlife conflicts over shared resources are often interpreted as direct competition, whereby resources consumed by wildlife are unavailable to people. Yet, individuals of wild populations can differ in their propensity to exploit resources, and the demographic, ecological, and economic consequences of such behavioural heterogeneity remain poorly understood. We developed an integrated demographic-bioeconomic model of Atlantic cod (Gadus morhua), grey seals (Halichoerus grypus), and a coastal gillnet fishery. Stage-structured population models incorporated density dependence, predator-prey interactions, adaptive harvesting, and transitions of adult seals into a bold, depredating state. We compared four scenarios of increasing complexity over 50 years across 100 coupled demographic realisations and examined sensitivity to the energetic contribution of depredated catch, harvest levels, and social amplification of depredation behaviour. Cod and seals persisted in all demographic realisations, supporting our ecological-persistence hypothesis. Contrary to our predictions, however, adding bold seals neither reduced long-term fishery performance nor destabilised the system. Median net present value was approximately three-fold greater in the full system than without depredation, and was greater in 76% of paired realisations. The full system was also the most temporally stable: median cod variability declined by 74% relative to the otherwise equivalent fishery without depredation, while seal variability declined by 45%. Behavioural depredation therefore altered predator population structure and imposed direct catch losses, but these effects were counterbalanced by indirect demographic feedbacks. Synthesis and applications. Depredating predators should not necessarily be viewed simply as competitors that reduce contested resources. Behavioural heterogeneity can introduce compensatory pathways that alter population stability and long-term economic outcomes. Management should therefore consider not only predator abundance and immediate catch losses, but also how harvest creates opportunities for behavioural change and how those changes propagate through coupled ecological-economic systems.

ecology↗

bioRxiv2026

Long-term liana infestation leaves architectural legacies in host trees

Lianas are an integral component of tropical forests, and their increasing abundance may alter forest structure and function. Whether infested host trees are shorter and smaller-crowned because lianas suppress them, or because lianas preferentially colonise already small trees, has remained unresolved. We used a longitudinal dataset of individual liana infestation histories spanning fourteen years (2011-2025), combined with terrestrial laser scanning (TLS)-derived structural metrics for 251 trees across 67 species on Barro Colorado Island, Panama. Bayesian analyses showed that persistently infested trees were shorter (11.0%), with smaller crown projected area (22.3%) and crown volume (25.9%) than never-infested trees; the 95% credible intervals for these effects excluded zero. Pre-infestation crown architecture did not credibly predict subsequent colonisation. These patterns are more consistent with persistent infestation imposing lasting architectural legacies than with strong pre-existing architectural susceptibility, suggesting rising liana abundance may progressively reduce forest stature and canopy density, with implications for tropical carbon storage projections.

ecology↗

bioRxiv2026

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordingly, we propose a physics-informed hybrid gated recurrent unit (PI-HGRU) model for spatiotemporal dynamic prediction of rice sheath blight disease, caused by a fungus. Our model embeds differential equations describing disease transmission dynamics into a hybrid gated recurrent unit (HGRU) framework as mechanistic constraints, thereby enabling collaborative modelling between epidemiological processes and data-driven learning. We conducted model training and evaluation using a long-term, multi-source dataset spanning 17 years (2000-2016) and covering 16 major rice-producing provinces in southern China. These rich data include spatiotemporally aligned field disease observations, remote sensing data, meteorological data, and soil property data. In addition, to address the challenges of irregular sampling intervals and inconsistent sequence lengths in disease survey data, we adopted a sliding time-window-based prediction framework. We further conducted a time-window sensitivity analysis to determine appropriate configurations of the input time window and lag time, enabling the model to represent the cumulative and delayed effects of environmental factors. Our PI-HGRU framework substantially outperforms the purely data-driven HGRU baseline model, improving the squared Pearson correlation coefficient (r2) by 22.8% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by 10.2% and 18.0%, respectively. Furthermore, analysis of the models intermediate variables showed that the transmission rate {beta}(t) exhibited interpretable relationships with environmental conditions within the input time window, providing a process-related link between environmental drivers and modeled disease transmission dynamics. Overall, our work demonstrates that integrating epidemiological mechanisms into deep learning models in a physics-informed manner can improve predictive accuracy and stability while enhancing model interpretability, highlighting its potential for large-scale disease forecasting and precision disease management.

bioinformatics↗

bioRxiv2026

Life history traits predict the contribution of transient dynamics to variation in population growth

The performance of any natural population in variable environments depends on contemporaneous changes in its vital rates (e.g., survival, reproduction) as well as legacies carried by its population structure. Yet whether the relative contribution of these two pathways can be predicted from life history remains unknown. Here, we use stochastic simulations of 1,986 matrix population models from 137 species to quantify the contribution of transient dynamics to variation in population growth rate, and test its associations with key life history traits. Longer generation times were associated with reductions in transient contributions, contrary to theoretical expectations. Greater stage-specific survival heterogeneities were associated with increases in transient contributions, whereas greater iteroparity was associated with decreases in plants but increases in animals. These associations were robust to body size, phylogenetic relationships, and vital-rate variability. Life history traits therefore provide a strong predictor for when population structure shapes population responses to environmental variability.

ecology↗

bioRxiv2026

No single measure is enough: Recovery of the Critically Endangered Mobula mobular requires integrated maximum bycatch mitigation and nursery area protection.

As anthropogenic threats have intensified over the past 500 years, we find ourselves in the midst of a sixth mass extinction, with continued losses of biodiversity threatening ecosystem stability. This biodiversity loss has caused species extinctions across taxa, and placed several others at high risk of functional extinction. These disturbance-driven impacts represent one of the most acute biodiversity crises facing global marine systems. Species exhibiting slow life histories characteristically have low resilience to disturbance. Here, we assess the risk of functional extinction and identify policy pathways for population recovery of the slow-living, Critically Endangered elasmobranch, the spinetail devil ray (Mobula mobular). We develop a stochastic, state-structured Integral Projection Model (IPM) parameterised with demographic data collected from fishery landings data in India, the world's largest mobulid fishery, and supplemented with data on vital rates from published literature. Using the IPM, we estimate that the population is declining at approximately 12% annually, experiencing substantial limiting pressure from fisheries overexploitation and failing to approach its biological maximum growth potential. Our results indicate that populations of M. mobular will be at high risk of functional extinction if 'business as usual' harvest scenario persists for another decade. We further show that long-term population recovery is only possible if survival increases significantly across all size classes, especially among large reproductive females, alongside a concurrent increase in fecundity. We conclude that no single policy measure is sufficient to recover population of M. mobular along the southeastern coast of India. Instead, combined protection through maximum bycatch mitigation and protection of nursery areas in no-take zones will be required for population recovery. This research demonstrates that recovery of overexploited populations often requires integrated resource management across life stages, and that the Critically Endangered M. mobular warrants urgent conservation action to avoid functional extinction.

ecology↗

bioRxiv2026

Still Lost in Definitions: How Resilience Is Used in Ecology

Resilience is a central concept in ecology and environmental policy, yet its meaning and quantification remain inconsistent across subfields. Clarifying how resilience is defined and measured across the subfields of ecology is therefore a critical step towards delivering coordinated efforts to strengthen resilience research. Here, we analyse 594 studies published between 1977 and 2025 to determine how resilience is quantified across ecological contexts. Using large language models to extract structured data and conditional inference forests to assess predictors of metric choice, we show that resilience is most commonly ([~]25%) quantified using recovery rate and recovery degree, but no single metric dominates. Crucially, study attributes like organisational level, methodological approach, and disturbance regime explain only a small fraction of variation in metric selection. Despite this apparent inconsistency, more than 90% of studies draw from a shared set of six quantitative dimensions of resilience. This combination of weak constraint and latent convergence suggests that resilience metrics function as a flexible but implicitly standardised toolkit rather than as context-specific constructs. We argue that this hidden structure provides a foundation for a unified, multidimensional resilience framework that can support synthesis across ecological systems and improve the translation of resilience science into conservation and policy.

ecology↗

bioRxiv2026

Climate change drives contrasting redistribution patterns in endemic and endangered Himalayan Gentiana

O_LIRapid climate warming threatens mountain biodiversity, particularly species with narrow climatic niches and limited dispersal capacity. Mountain ecosystems are especially vulnerable because steep environmental gradients restrict opportunities for species redistribution. The Kashmir Himalaya, a globally important biodiversity hotspot experiencing accelerated warming, has already undergone an increase of approximately 0.8 {degrees}C during the 20th century and is projected to warm by 2.5-2.8 {degrees}C by the 2050s. Despite climatic changes, future persistence of many threatened plants remains poorly understood. C_LIO_LIHere, we evaluate present and future habitat suitability for two Himalayan taxa, Gentiana cachemirica, an endemic species, and Gentiana kurroo, a critically endangered species. Specifically, we quantify how climate change and topographic variability influence species distribution and persistence under multiple emission scenarios. C_LIO_LIWe applied species distribution models (SDMs) to presence data of both species and forecasted habitat suitability under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). We hypothesized that G. cachemirica, a narrow-niche, low-dispersal species, is expected to lose habitat due to thermal sensitivity, while G. kurroo may persist or expand under favorable scenarios because of broader tolerance and higher dispersal. We also expected microclimatic refuges to buffer populations, whereas extreme warming would accelerate habitat decline. C_LIO_LIThe predictions of our SDMs under current conditions indicate a highly restricted and fragmented habitat for G. cachemirica, covering 651 km{superscript 2}, but a broader suitable area (2,452 km{superscript 2}) for G. kurroo. In agreement with our hypotheses, our forecasts indicate severe habitat contraction (55-70%) for G. cachemirica across all SSPs, but scenario-dependent responses for G. kurroo, including modest expansion under low-emission scenarios and severe declines under high-emission scenarios. Centroid analyses suggest pronounced climate-driven range shifts, with G. kurroo projected to migrate up to 33 km toward the east-southeast by 2100, while G. cachemirica is projected to display limited dispersal capacity. C_LIO_LISynthesis. Our findings suggest that climatic niche breadth, dispersal limitation, and topographic buffering strongly mediate species responses to warming in mountain ecosystems. Endemic specialists are projected to experience disproportionate habitat fragmentation and range restriction, highlighting the importance of conserving climatic refugia and elevational connectivity under rapid environmental change. C_LI

ecology↗

bioRxiv2026

Variability and persistence in inflation reshape human population dynamics by amplifying sensitivity and eroding demographic buffering

Inflation affects human fertility and survival; persistent inflation can translate into fluctuations in annual population growth rate. Existing studies have yet to systematically examine inflations impact on long-term growth rates across populations. Here, we examine whether high inflation variance and autocorrelation amplify long-term growth rates sensitivity to inflation, across 32 countries over 1971-2024. Additionally, we examine the effect of increasing inflation variance and autocorrelation on humans capacity for demographic buffering, i.e., reducing the temporal variance in vital rates most sensitive to growth. First, we construct inflation-dependent matrix population models using a hierarchical Bayesian framework. We then calculate the stochastic sensitivity and elasticity of population growth rate to inflation to quantify demographic buffering across a variance-autocorrelation parameter space. We find that the stochastic sensitivities of the growth rate increase with inflations variance and autocorrelation. However, we do not find support for the expected adverse effects of inflation variance and autocorrelation on humans capacity to buffer against inflation. Our framework provides a valuable tool for examining the impact of resource uncertainty on life history strategies in stochastic environments. We discuss how our framework can be extended to study the resource-dependent population dynamics within variable environments and across the tree of life.

ecology↗

bioRxiv2026

Stage-dependent biotic interactions may not be important for stochastic competitive dynamics with little variation in stage structure

O_LIStage-dependent interactions, in which different life cycle stages (e.g., juveniles, adults) exert different per-capita competitive effects, are widespread across ecological communities. However, whether explicitly accounting for such ontogenetic variation improves forecasts of stochastic community dynamics remains unclear. We tested how the strength of stage dependence and species life-history strategy influence the predictive accuracy of community models that either include or ignore stage-specific interactions. C_LIO_LIWe constructed stochastic two-species competition models using stage-structured matrix population models spanning five virtual life histories along the fast-slow continuum. Density dependence was imposed separately on juvenile survival, adult survival, progression, retrogression, or fertility, and the strength of stage dependence varied from adult-driven to juvenile-driven competition. We then fitted deterministic projection models with and without stage-dependent interaction terms to simulated time series and quantified predictive performance over 100 time-step forecasts using mean absolute percentage error (MAPE). C_LIO_LIIncreasing stage dependence consistently reduced the predictive accuracy of models that ignored stage structure. However, absolute prediction errors remained small across all scenarios (MAPE < 0.7%), even under strong stage dependence. The influence of life-history strategy depended on which vital rate was density dependent: when juvenile survival was density dependent, faster life histories showed larger errors; when progression, retrogression, or fertility were density dependent, slower life histories exhibited greater errors; and when adult survival was density dependent, no consistent life-history effect emerged. Across simulations, temporal variation in population structure was low (coefficient of variation < 0.036), and prediction error was strongly associated with the magnitude of structural fluctuations rather than life-history pace per se. C_LIO_LISynthesis. Stage-dependent interactions can, in principle, alter stochastic competitive dynamics, but their practical importance for ecological forecasting depends on the extent to which population stage structure fluctuates through time. When environmental stochasticity dominates and stage structure remains near equilibrium, simpler models that ignore stage dependence provide robust approximations of community dynamics. Our results identify conditions under which demographic detail is necessary for forecasting and highlight the central role of structural variability in linking life-history strategy to community-level dynamics. C_LI

ecology↗

bioRxiv2026

Tipping Points and Emergent Phases of Species Diversity in Mutualistic Ecological Networks under Global Warming

Global warming poses a severe threat to ecosystem resilience and biodiversity, potentially driving species loss, declining diversity, and ecosystem collapse. Accurately predicting tipping points of ecological resilience under global warming remains a fundamental challenge. Here, we develop a theoretical framework to identify climate-induced tipping points in mutualistic ecological networks, capturing critical transitions under warming scenarios. We show that rising temperatures, increasing intraspecific competition, and deteriorating mutualistic interaction can drive ecosystems through three distinct dynamical phases of species diversity: stable full coexistence, stable partial coexistence, and total extinction. Notably, ecosystems with low species abundances are particularly vulnerable, facing heightened risks of diversity loss and extinction. Moreover, the warming rate modulates these transitions, as slower warming grants endangered species a window of time within which to adjust and attenuate the loss of resilience. Applying this framework to assess various emission mitigation pathways, we find that "pollute first, clean up later" scenarios pose a threat to biodiversity, leading to irreversible biodiversity losses even after subsequent emission reductions. Our findings offer critical insights into the timing and mechanisms of ecological tipping points under climate change and underscore the urgency of proactive mitigation strategies.

ecology↗

bioRxiv2026

Looking backward for the future: long-term population recovery, habitat associations, and future climatic vulnerability of the critically endangered Sino-Mongolian beaver (Castor fiber birulai) in China

Despite the successful population recovery of the Eurasian beaver (Castor fiber) across much of Eurasia, its subspecies, the Sino-Mongolian beaver (C. f. birulai), remains critically endangered, with an estimated population of approximately 1,500 individuals confined to a small number of isolated and fragmented refugia along the China-Mongolia border. Effective conservation of this highly threatened subspecies requires a holistic perspective that integrates constraints on population dynamics, habitat associations, and future climatic vulnerability. Here, we combined systematic annual field surveys conducted between 2003 and 2023 with historical survey records from 1975 to 1989 in northern Xinjiang, China, to synthesize long-term spatiotemporal population dynamics, evaluate habitat preferences based on nine local environmental variables, and assess future climatic vulnerability using ensemble species distribution models (SDMs) under projected climate change scenarios. We detected a significant and phased population recovery, with beaver colony numbers increasing from 27 (approximately 100 individuals) in 1975 to 227 (681-908 individuals) in 2023. This recovery closely corresponded with major conservation milestones, including the establishment and upgrading of nature reserves, strengthened legislative protection, and enhanced multi-stakeholder collaboration. Habitat analyses further indicated that the Sino-Mongolian beaver preferentially occupied areas characterized by minimal anthropogenic disturbance and stable hydro-geomorphic conditions. Critically, SDM projections revealed that only 14% of the current study area presently exhibits high climatic suitability, and these highly suitable habitats are expected to disappear entirely by the 2050s. Together, our findings provide a comprehensive overview of the historical population recovery and conservation trajectory of the Sino-Mongolian beaver in China, and offer robust scientific support for developing adaptive management strategies in the face of ongoing climate change and increasing human pressures.

ecology↗

bioRxiv2026

From theory to application: Elasticity-consistent aggregation of Leslie matrix population models for comparative demography

O_LIEcology has entered the big data era. This influx of data has now enabled ecologists to address questions about life history and demographic patterns across the tree of life. Supporting this momentum, the COMADRE and COMPADRE databases represent a boon to comparative demography. However, these initiatives also present challenges due to the complexities of the life cycles that they describe. Matrix population models can vary in sampling frequency, life cycle stage width, life cycle complexity, and census type (e.g. pre- or post-reproduction). Complicating this picture is the fact that there are myriad matrix model representations of the same population, and model representation influences key demographic parameters. Thus, a key challenge in exploiting large demographic datasets is fair comparison of models constructed with different projection intervals and complexities. C_LIO_LIOne way to compare models of different complexity is to reduce ( aggregate) the larger models to the dimensionality of the smaller models. The commonly used aggregator (i.e. the standard aggregator), although it yields stable growth rate and stable stage distribution consistent with the original demographic model, does not provide consistent reproductive values and elasticities. To address this limitation, we extend and validate an existing elasticity-consistent aggregator, overcoming several of its methodological and biological limitations. Specifically, we derive the aggregator using balancing and interstage flows, which removes the requirement of matrix primitivity, and we restrict aggregation to Leslie-to-Leslie models, thereby preventing biologically infeasible survival probabilities exceeding one. We further introduce explicit metrics of aggregation effectiveness and apply the approach to 12 Leslie matrix population models representing animal populations from diverse taxonomic classes. C_LIO_LIThe elasticity-consistent aggregator returns a Leslie matrix that preserves key properties of the original matrix, including irreducibility and primitivity, and yields consistent estimates of population growth rate, stable age distribution, and reproductive values. Moreover, across all aggregated models from the 12 examined animal populations, the elasticity-consistent aggregator produced more accurate estimates than the standard aggregator for 86% of generation times, 60% of Demetrius entropies, and 76% of net reproductive rates. C_LIO_LIBy preserving key properties of the original model, the elasticity-consistent aggregator provides a useful framework for comparing matrix population models of varying complexity in comparative demography. Such a method helps seize the promise of big data in ecology and discover principles across the tree of life, from microbes to fungi, plants, and animals. C_LI

ecology↗

bioRxiv2025

Retrospective image analysis for long-term demography using Google Earth imagery

O_LIEcosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, Google Earth, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to detect species and track their population dynamics remains under-explored. C_LIO_LIWe introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving two major Mediterranean invasive plant taxa with contrasting growth forms. Specifically, we: (1) utilise deep learning to automatically detect individuals of prickly pear (Opuntia sp.) across various Mediterranean habitats and image resolutions; (2) reconstruct 10 years of spatially explicit recruitment rates for Opuntia along a climatic gradient; and (3) quantify nearly 20 years of growth dynamics for the clonal invader Carpobrotus sp. in two contrasting environments. C_LIO_LIOur object detection model, trained with Google Earth imagery, achieves 60-80% success in identifying individuals of Opuntia, regardless of habitat type. Model performance increases with target species colour consistency and contrast, as well with the usage of basic data augmentation techniques. Detection is constrained by individual area (<4 m{superscript 2}) but captures 80% of the examined population. C_LIO_LIBeyond detection, our time-series analysis of publicly available imagery enables detailed population monitoring. With 10-year image series available for Spain, Greece, and the UK, and 20 years for Portugal, we successfully estimate annual recruitment and growth rates and their climatic sensitivity, identify productive and unproductive years, estimate individual age, characterise population structure, model size-age relationships, and identify recruitment hotspots for targeted management. C_LIO_LIOur pipeline opens new avenues for cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene. C_LI

ecology↗

bioRxiv2025

LiDAR and hyperspectral-based structured population models show future forest fire frequency may compromise forest resilience

AbstractForest disturbances are accelerating biodiversity loss and altering tree productivity worldwide. Post-disturbance recovery time is critical for identifying vulnerable areas and targeting conservation but varies with environmental conditions. Monitoring recovery at scale requires tracking tree dynamics, yet traditional ground-based approaches are resource-intensive. We present a pipeline to parameterise integral projection models (IPMs) using LiDAR and hyperspectral data to assess post-fire recovery across large, forested areas. Focusing on the fire-adapted Picea mariana, we model passage times to reproductive heights and life expectancy under different fire regimes as indicators of recovery. To do this, we combined hyperspectral-derived species maps and LiDAR-based crown heights to track individual tree survival and growth at the Caribou-Poker Creek Research Watershed (BONA) from 2017-2023. We incorporated fire history, aspect, slope, elevation, and surrounding canopy height into our models and found partial support for their expected effects on survival and growth. Once accounting for topography and competition, we estimated passage times to reproductive maturity (11-22 years). Life expectancy in the absence of fire is shortest on North-facing slopes with recent fire (579 years). Sensitivity analyses highlight fire history and aspect as key modulators of population resilience, with elevation exerting strong influence on life expectancy across all conditions. Our results demonstrate that remotely sensed IPMs can effectively quantify forest recovery at scale, revealing that in some contexts, stands of P. mariana may not recover between fire disturbances. We discuss the implications of these findings for resilience-based forest management and highlight both the challenges and opportunities of using LiDAR and hyperspectral data to build demographic models for forecasting forest dynamics.

ecology↗

bioRxiv2025

Individual heterogeneity drives a plant macroparasites life history

The population dynamics and life histories of macroparasites are fundamental to examine their impacts on hosts and ecosystems. Still, macroparasite population models often ignore parasite individual heterogeneity and are rarely applied to parasitic plants, where demographic strategies are less well understood. Using a 10-year dataset on European mistletoe (Viscum album), we examine how this macroparasites traits influence its performance. V. album survival and reproduction depend on its size and, to a lesser extent, height on the host tree, with a strong growth-reproduction trade-off. We parameterise these relationships in an integral projection model to compare the life history strategy of V. album with two other macroparasites and 490 free-living plants. Contrary to our hypothesis, V. album and other macroparasites do not follow an extreme strategy, suggesting that parasitic plants may not escape the life history constraints experienced by non-parasitic plants. Our results highlight how incorporating parasite heterogeneity can improve macroparasite models.

ecology↗

bioRxiv2025

Support for the social buffering hypothesis, especially under unpredictable precipitation regimes

Sociality is hypothesised to buffer populations against environmental stochasticity by stabilising key vital rates, yet this has rarely been tested across diverse taxa. We compiled demographic time series from 87 populations for 66 animal species spanning 12 taxonomic classes to test whether sociality promotes demographic buffering and whether this relationship depends on environmental context. Using stochastic elasticities and phylogenetically comparative analyses, we find broad support for the social buffering hypothesis: more social species exhibit lower sensitivity of population growth to vital rate variance (lower stochastic elasticity to vital rate variances, |T{sigma}|), particularly in juvenile survival and reproduction. Contrary to expectations, juvenile vital rates, but not adult survival, showed the strongest buffering patterns in social taxa, suggesting life-stage-specific social mechanisms. Furthermore, we show that social species invest more in maximising the average performance of adult survival and reproduction (higher stochastic elasticity to vital rate means, T), consistent with adaptive strategies favouring canalisation of fitness-related traits. This buffering benefit is especially relevant in highly unpredictable environments: in climates with erratic precipitation, social species have greater demographic advantages, exhibiting even lower T{sigma} values. Together, these findings provide support for the social buffering hypothesis, especially in highly stochastic environments. Social traits confer some demographic stability under moderate climatic conditions, but may become more relevant under extreme stochasticity--an insight particularly important for understanding population resilience in a rapidly changing world.

animal behavior and cognition↗