Ecological Modelling最新文献

{"title":"Variability in habitat selection between herds for a widespread ungulate","authors":"Logan Hysen ,&nbsp;Ho Yi Wan ,&nbsp;Patrick Jantz ,&nbsp;Jeff Gagnon ,&nbsp;Samuel A. Cushman","doi":"10.1016/j.ecolmodel.2024.110991","DOIUrl":"10.1016/j.ecolmodel.2024.110991","url":null,"abstract":"<div><div>Understanding habitat selection is critical for the conservation of ungulate species. Our aim was to (1) quantify herd-specific habitat selection for American pronghorn (<em>Antilocapra americana</em>) in the southwestern United States and (2) produce a habitat suitability map that can aid in the prioritization of management actions. We used GPS telemetry locations for individual pronghorn from 2007–2013 representing six herds and remotely sensed habitat covariates to model habitat selection. To determine the effect of each habitat covariate on habitat selection, we fit integrated step selection functions (iSSFs) to the data for each pronghorn herd using a mixed-effects modeling framework. We included random effects of individual pronghorn to account for intra-specific variability in selection. We used the coefficient values from iSSFs to produce a habitat suitability map averaged across the six herds and then evaluated the predictions against data from independently tracked pronghorn herds. Our findings indicated that while there is between-herd variability in pronghorn habitat selection in northern Arizona, there were also some common relationships. All herds selected for areas with a greater proportion of grassland and shrubland, however the magnitude of that selection varied between herds. Most herds also selected areas with low topographic diversity and a lower proportion of developed land with different magnitudes of response between herds. The responses to these two covariates seem to be related to function responses to local limiting factors. The average habitat suitability map indicated large swaths of unsuitable area separating some herds with large areas of habitat in the northeast. Our results demonstrate the importance of investigating herd-level variation in habitat selection analyses for herd-forming species, meaning managers can make decisions that are tailored to a herd's unique situation but also contextualize those decisions within conservation efforts across the landscape.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110991"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particle tracking modelling in coastal marine environments: Recommended practices and performance limitations","authors":"Soizic Garnier ,&nbsp;Rory O'Hara Murray ,&nbsp;Philip A. Gillibrand ,&nbsp;Alejandro Gallego ,&nbsp;Peter Robins ,&nbsp;Meadhbh Moriarty","doi":"10.1016/j.ecolmodel.2024.110999","DOIUrl":"10.1016/j.ecolmodel.2024.110999","url":null,"abstract":"<div><div>Particle tracking models (PTMs) can simulate the dispersal of particulate and dissolved material in the marine environment that are difficult to observe directly – including organic matter and pollutants that may change in composition, biological organisms that can grow and swim, and pathogens that decay and attach to other material. However, the predicted dispersal patterns can carry significant uncertainties, arising from factors such as the accuracy of the driving hydrodynamics, PTM resolution, stochasticity of dispersal simulations, and parameterisation of the behavioural characteristics of the particles (e.g., microplastic settling speeds, eDNA decay, larval swimming behaviour). Numerous PTM software packages are available, offering different parameterisations, meaning that selecting an appropriate PTM to minimise model uncertainties and optimise computational efficiency is important. This review provides a foundational resource and best practices for PTM users, particularly for marine applications. It outlines essential questions that users should ask to help choose the appropriate PTM, and its parameterisation for the intended application. A case study is presented to illustrate potential uncertainties in particle dispersal using three different PTMs. The case study highlights that, in regions with complex coastlines, the land boundary condition can significantly impact particle trajectories and requires careful consideration. This resource will help standardise good practices for configuring PTMs to accurately represent (or account for uncertainty in) dispersal in the marine environment.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110999"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved SIF-based GPP estimation method based on quantifying the joint effect of photosynthetically active radiation and temperature on LUE/SIFyield","authors":"Lijiang Fu ,&nbsp;Junqing Chen ,&nbsp;Jinglu Tan ,&nbsp;Suleyman I. Allakhverdiev ,&nbsp;Ya Guo","doi":"10.1016/j.ecolmodel.2024.111017","DOIUrl":"10.1016/j.ecolmodel.2024.111017","url":null,"abstract":"<div><div>Accurate measurement and estimation of ecosystem gross primary production (GPP) are essential for understanding ecosystem carbon dynamics. Solar-induced chlorophyll fluorescence (SIF) is a convenient predictor of gross primary production (GPP) due to their linear relationship. However, the different responses of light use efficiency (LUE=GPP/APAR) and SIF quantum yield (SIF_yield=SIF_tot/APAR) to climatic variables make GPP-SIF relationship complicated. In this research, a hyperbolic function <em>f</em>(PAR), a parabolic function <em>f</em>(T) and a synergy function <em>f</em>(PAR,T) are used to model the effects of photosynthetically active radiation (PAR) and temperature (T) on LUE/SIF_yield, either separately or together based on field data from two different ecosystem sites on half-hourly or daily scales. Results show that the proposed synergy function can better predict actual LUE/SIF_yield. Further, with the effect of PAR and T on LUE/SIF_yield, the accuracies of SIF-based GPP estimation are improved when <em>f</em>(PAR,T) is included. This study provides an improved SIF-based GPP estimation method by quantifying the key roles of PAR and T on LUE/SIF_yield.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 111017"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid deep learning model based on CNN-GRU-BiLSTM for predicting the carbon removal capacity of the living standing tree using multi-source variables","authors":"Zehai Xu ,&nbsp;Qiaoling Han ,&nbsp;Yandong Zhao","doi":"10.1016/j.ecolmodel.2025.111026","DOIUrl":"10.1016/j.ecolmodel.2025.111026","url":null,"abstract":"<div><div>Accurate prediction of the carbon removal capacity of the living standing tree holds significant importance in understanding plant growth mechanisms, carbon balance, and environmental adaptability. This study investigated the entire <em>Radermachera sinica</em> trees using an enhanced static assimilation chamber integrated with IoT technology for automated, continuous monitoring. A novel self-designed stem water content sensor was introduced to capture the dynamics of stem water content, while simultaneously monitoring climate and soil factors. The relationship between the carbon removal capacity and multi-source variables was analyzed. A hybrid deep learning model based on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-GRU-BiLSTM) was developed to predict the carbon removal capacity, with performance evaluated against traditional machine learning and simpler deep learning models. The results indicate that: 1) The carbon removal capacity exhibited a distinct \"U-shaped\" diurnal variation pattern. 2) In most cases, there was close alignment between the transition moments of positive and negative values of the carbon removal capacity and stem water change rate, showing a significant positive correlation. 3) The hybrid model can effectively capture key influences of multi-source variables on carbon removal capacity, markedly improving regression prediction accuracy over traditional and alternative models. The RMSE, MAE, MAPE, and R² values can reach 1.1221 %, 0.75879 %, 0.29614 %, and 0.91191, respectively. The findings underscore the pivotal role of stem water dynamics in carbon removal prediction and provide a foundational model framework for assessing carbon sequestration and ecological responses in plant communities under variable environmental conditions.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 111026"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of phenotypic plasticity on diversification rates and adaptive evolution in simulated environments with different climatic and cost contexts","authors":"Emerson Campos Barbosa Júnior,&nbsp;Pavel Dodonov,&nbsp;Hilton F. Japyassú,&nbsp;Bruno Vilela","doi":"10.1016/j.ecolmodel.2024.110983","DOIUrl":"10.1016/j.ecolmodel.2024.110983","url":null,"abstract":"<div><div>Phenotypic plasticity can either hinder or facilitate genetic evolution, thus affecting macroevolution. However, the mechanisms and associations of phenotypic plasticity with biodiversity patterns remain unresolved. We investigated the effect of phenotypic plasticity on adaptive evolution in the context of climatic changes and plasticity costs, specifically examining the rates of trait evolution, speciation, extinction, and diversification. We employed an eco-evolutionary agent-based model, incorporating body temperature as a plastic trait that dynamically responds to fluctuations in environmental temperature. In all simulated scenarios, we found that an increase in plasticity led to a decrease in extinction and trait evolution rates. Speciation rates exhibited a non-linear relationship with plasticity, characterized by an asymmetric U-shaped curve. This intricate speciation pattern likely arises from the interplay of two conflicting forces: (1) low extinction rates foster larger population and range sizes, thereby augmenting the potential for speciation; (2) reduced trait evolution, stemming from stable selection, enhances population homogenization and connectivity, consequently inhibiting speciation. Still, overall diversification tends to increase with plasticity. In sum, our simulation unveils potential mechanisms through which phenotypic plasticity could affect macroevolution – specifically, our results support the hypothesis that plasticity should promote diversification, mainly by reducing extinction, while at the same time reducing the rate of change in the plastic trait.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110983"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter sensitivity and transferability for simulating ET and GPP of Dryland ecosystems across a climate gradient","authors":"Gerald N. Flerchinger ,&nbsp;Xiaosheng Chu ,&nbsp;Kathleen A. Lohse ,&nbsp;Patrick E. Clark ,&nbsp;Mark S. Seyfried","doi":"10.1016/j.ecolmodel.2024.110973","DOIUrl":"10.1016/j.ecolmodel.2024.110973","url":null,"abstract":"<div><div>Ecohydrology models are essential tools for projecting ecosystem shifts and quantifying water and carbon fluxes across ecosystems. Parameter uncertainty limits our ability to simulate ecosystem processes. We evaluated sensitivity of parameters in the widely used Farquhar and Ball-Barry algorithms for simulating evapotranspiration (ET) and gross primary productivity (GPP) for three sagebrush ecosystems across an elevation/climate gradient within the Reynolds Creek Critical Zone Observatory in southwestern Idaho, USA. This gradient spanned annual precipitation rates of 292 to 800 mm and GPP from 420 to 849 gC m⁻². We used a Monte-Carlo approach of 10,000 to 20,000 model runs to assess the distribution of optimal parameter values for each site. Distributions of best-case values for sagebrush parameters controlling carbon uptake were similar for the Wyoming big sagebrush and mountain big sagebrush sites but differed for the low sagebrush site. Differences in sagebrush transpiration parameters were consistent with soil water regimes for these different species and subspecies. Parameter values for the herbaceous understory showed less similarity between sites and years, which was attributed to the varying composition of grasses and forbs. Peak seasonal GPP was replicated by the model, with understory vegetation contributing up to 80 % of peak GPP. R² values for simulated GPP ranged from 0.78 to 0.90 for the optimization period and 0.61 to 0.89 for the validation period. A composite parameter set for each site obtained by combining parameter values whose best-case distributions did not significantly differ, resulted in no meaningful difference in simulations. This study reveals the extent to which parameters can be transferred across ecosystems and years, thereby reducing parameter uncertainty which is a critical step forward in assessing future trajectories of ecosystems and carbon fluxes.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110973"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian networks facilitate updating of species distribution and habitat suitability models","authors":"Adam Duarte ,&nbsp;Robert S. Spaan ,&nbsp;James T. Peterson ,&nbsp;Christopher A. Pearl ,&nbsp;Michael J. Adams","doi":"10.1016/j.ecolmodel.2024.110982","DOIUrl":"10.1016/j.ecolmodel.2024.110982","url":null,"abstract":"<div><div>Managers often rely on predictions of species distributions and habitat suitability to inform conservation and management decisions. Although numerous approaches are available to develop models to make these predictions, few approaches exist to update existing models as new data accumulate. There is a need for updatable models to ensure good modeling practices in an aim to keep pace with change in the environment and change in data availability to continue to use the best-available science to inform decisions. We demonstrated a workflow to deliver predictive models to user groups within Bayesian networks, allowing models to be used to make predictions across new sites and to be easily updated with new data. To demonstrate this workflow, we focus on species distribution and habitat suitability models given their importance to informing conservation strategies across the globe. In particular, we followed a standard process of collating species encounter data available in online databases and ancillary covariate data to develop a habitat suitability model. We then used this model to parameterize a Bayesian network and updated the model with new data to predict species presence in a new focal ecoregion. We found the network updated relatively quickly as new data were incorporated, and the overall error rate generally decreased with each model update. Our approach allows for the formal incorporation of new data into predictions to help ensure model predictions are based on all relevant data available, regardless of whether they were collected after initial model development. Although our focus is on species distribution and habitat suitability models to inform conservation efforts, the workflow we describe herein can easily be applied to any use case where model uncertainty reduction and increased model prediction accuracy are desired via model updating as new data become available. Thus, our paper describes a generalizable workflow to implement model updating, which is widely recognized as a good modeling practice but is also underutilized in applied ecology.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110982"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting budbreak dates for ‘Delaware’ grape considering chilling and heat requirements using PhenoFlex","authors":"Masahiro Kamimori ,&nbsp;Akihiro Hosomi","doi":"10.1016/j.ecolmodel.2024.110985","DOIUrl":"10.1016/j.ecolmodel.2024.110985","url":null,"abstract":"<div><div>Owing to recent climate change, budbreak in the ‘Delaware’ grape (<em>Vitis vinifera</em> × (<em>V. labrusca</em> × <em>V. aestivalis</em>)) in Osaka, Japan, is occurring earlier. Therefore, the development of phenology models for budbreak in ‘Delaware’ based on the relationship between budbreak and temperature (a key environmental factor) is important for both short-term (vineyard management or frost risk assessment) and long-term (assessment of suitable land or effect of future climate change) needs. The recently developed PhenoFlex modeling framework combines the Dynamic model and Growing Degree Hour models as sub-models for chilling and heat requirements with a flexible transition, showing good prediction accuracy in several temperate woody perennials. However, case studies of PhenoFlex in grapes are absent. In this present study, we aimed to evaluate the applicability of the PhenoFlex modeling framework to grapes using ‘Delaware’ dataset from 1963 to 2023 in Osaka, Japan. PhenoFlex showed a high prediction accuracy, with root mean square error values ranging from 2.28 to 2.79 days. Furthermore, the PhenoFlex model with the Dynamic model parameters adjusted using our data showed higher prediction accuracy and robustness than the model with the original parameters of the Dynamic model, indicating that the Dynamic model parameters should be set for each target species or varieties. The PhenoFlex modeling framework was effective in predicting budbreak in ‘Delaware’ grape although some points of chill response curves derived from the parameters fitted using PhenoFlex were unrealistic.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110985"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural differences across hydrological models affect certainty of predictions of nature-based solution benefits","authors":"Alanna J. Rebelo ,&nbsp;Julia Glenday ,&nbsp;Petra B. Holden ,&nbsp;Shaeden Gokool ,&nbsp;David Gwapedza ,&nbsp;Peni Metho ,&nbsp;Jane Tanner","doi":"10.1016/j.ecolmodel.2024.110940","DOIUrl":"10.1016/j.ecolmodel.2024.110940","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Quantifying uncertainty in hydrological modelling has critical implications for informing decisions around land-use/land-cover trade-offs and management and therefore is good practice. Different hydrological modelling tools commonly used in water resource management are known to produce diverse results. This uncertainty is frequently overlooked in both research and applied cases. The influence of structural differences in terms of water balance partitioning in different models (model fidelity) has particularly been neglected. This study aims to quantify the uncertainty resulting from hydrological model structural differences for a specific nature-based solution scenario: clearing of invasive alien trees from mountain catchments in a water scarce region of South Africa. Clearing of these trees and restoration to indigenous shrublands has been shown to make more water available in catchments by reducing evapotranspiration. However, questions of magnitude and spatial variation remain. The extent of the increase in water availability within a given catchment, the pathways through which this change occurs, and the spatial variability of benefits across landscapes need to be better understood. We perform a model inter-comparison study of five hydrological modelling tools applied to a case study, using the same input (forcing) data and catchment conceptualisation, with equivalent parameterisation. All five tools are frequently used to guide decisions in water resource management in South Africa. The models were all calibrated against the same streamflow observations, and all were found to produce satisfactory results in this regard. The nature-based solution of clearing invasive alien trees was predicted to increase mean annual streamflow by 2.66 Mm&lt;sup&gt;3&lt;/sup&gt; on average, but with considerable uncertainty. Mean annual streamflow estimates varied by 16.1 Mm&lt;sup&gt;3&lt;/sup&gt; among models under this scenario (126.7-142.8 Mm&lt;sup&gt;3&lt;/sup&gt;, 12 % of mean annual streamflow). In terms of spatial variability, modelling tools all simulated a streamflow increase (0.4-1.0 %) when invasive alien trees were cleared from the riparian zones instead of the uplands, again with uncertainty (121.4-141.1 Mm&lt;sup&gt;3&lt;/sup&gt;, 15 % of mean annual streamflow). For all of the models, the main water balance component accounting for these streamflow changes was evapotranspiration. However, the pathway for this reduced evapotranspiration differed among models. For some models, canopy interception was the primary driver of the decreased evapotranspiration with invasive alien tree clearing, while for others it was soil and groundwater controls. This raises the age-old concern around equifinality in hydrological modelling in data scarce catchments, i.e. getting the right result for different reasons, and highlights the need for additional hydrological process data for resolution. We recommend the best practice of trying to estimate the uncertainty arising from structural differences i","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110940"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecological role of marine mammals in the Magellan Strait: Insights from trophic modeling","authors":"Daniela Haro ,&nbsp;Fabio A. Labra ,&nbsp;Sergio Neira ,&nbsp;Juan Carlos Hernández-Padilla ,&nbsp;Francisco Arreguín-Sánchez","doi":"10.1016/j.ecolmodel.2024.110944","DOIUrl":"10.1016/j.ecolmodel.2024.110944","url":null,"abstract":"<div><div>Predators, such as marine mammals, impact the structure and functioning of marine communities. Due to their energy requirements, the wide variety of prey and the diversity of ecological niches they occupy, these organisms exert effects on diverse ecosystems. To evaluate the ecological role and trophic impact of the marine mammals in the Magellan Strait, Chile, we built a food web model using the Ecopath software. In this system, marine mammals occupied the third and fourth trophic levels and fed on prey from 20 functional groups, from zooplankton (i.e., sei whales, dolphins) to sea lions and seabirds (i.e., killer whales). Killer whales played the ecological role of key species in this ecosystem, potentially controlling the biomass of large predators and explaining 100 % of their mortality caused by predation. This potential control favored a biomass increase of fish such as salmon (52 % of their biomass), silverside (45 %) and Patagonian robalo (42 %). South American sea lions had a high trophic impact on the ecosystem groups’ biomass, being a significant predator of salmon (76 % mortality). The results support the hypothesis that humpback whales are the main consumer of Fuegian sprats and squat lobsters, with 43 % and 40.7 % of the total prey consumption, respectively. Trophic generality significantly and directly correlated with the trophic level of consumers (t = 5.92; r = 0.78, <em>p</em> &lt; 0.001), demonstrating that high trophic level organisms feed on a greater prey diversity. Trophic vulnerability and trophic level presented a significant inverse correlation (s = 3883.2; ρ = -0.69; <em>p</em> &lt; 0.001), indicating that functional groups at higher trophic levels had either few or no predators in the Magellan Strait ecosystem. The results do not allow us to conclude that higher trophic-level organisms have a greater impact on the food web. We suggest that the trophic impact is related to multiple factors like predator biomass, feeding habits and prey biomass consumption in a particular system. This study is the first model to evaluate the ecological role of marine mammals in the food web of the Magellan Strait, Chile.</div></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":"501 ","pages":"Article 110944"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}