Ecological Modelling最新文献

{"title":"Predicting the effects of hypoxia on oyster (Crassostrea virginica) growth and reproduction through the Dynamic Energy Budget model","authors":"","doi":"10.1016/j.ecolmodel.2024.110799","DOIUrl":"10.1016/j.ecolmodel.2024.110799","url":null,"abstract":"<div><p>Hypoxia in the world's oceans poses an increasing threat to marine organisms and ecosystems, and there is a need to scale individual effects in order to make predictions about the broader ecological consequences of reduced oxygen availability. Mechanistic models, such as the Dynamic Energy Budget (DEB) model, provide a useful framework for quantifying the effects of changing environmental conditions, such as hypoxia, on individual organismal response. While the standard DEB model is forced solely by temperature and food availability, recent additions to the model for some marine species have allowed for the modulation of DEB energy fluxes in response to oxygen availability. The eastern oyster, <em>Crassostrea virginica</em>, is a sessile marine invertebrate inhabiting coastal environments experiencing variability in oxygen availability for which a DEB model has already been parameterized, but dissolved oxygen has not yet been incorporated as a forcing variable. The present study uses observed oyster growth data from sites throughout the Chesapeake Bay experiencing a range of oxygen regimes to validate a DEB model for <em>C. virginica</em>, implementing a constraint on energy fluxes due to oxygen availability. The effect of dissolved oxygen is parameterized into the <em>C. virginica</em> DEB model using an oxygen correction factor to constrain assimilation, mobilization, and ingestion rates within the energy budget. The resulting model accurately predicts empirically measured oyster growth data, with an average deviation between simulated and observed shell lengths of 4.70 % across all sites assessed. The model is then used to make predictions about hypoxia's influence on growth and reproduction over the oyster's growing season using data from two additional sites in the Chesapeake Bay. Model outputs indicate that low oxygen exposure results in reduced growth for oysters, in both shell length (6.9 % reduction) and tissue mass (23.6 % reduction), as well as reductions in oyster fecundity (54.4 % reduction in number of eggs spawned) and alterations to spawning frequency during the summer, which collectively has the potential to negatively affect oyster ecology. Overall, the integration of dissolved oxygen into the <em>C. virginica</em> DEB model provides an important tool to make predictions about how oysters will respond to future oceanic and coastal deoxygenation.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780417","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":"Modeling the bioenergetics of two herbivorous fish species in the Mediterranean Sea: The native Sarpa salpa and the invasive Siganus rivulatus","authors":"","doi":"10.1016/j.ecolmodel.2024.110804","DOIUrl":"10.1016/j.ecolmodel.2024.110804","url":null,"abstract":"<div><p>Global warming has facilitated the invasion of several Red Sea species in the Mediterranean Sea; a phenomenon known as Lessepsian migration. In this work, two key Mediterranean herbivore fish, the native salema (<em>Sarpa salpa</em>) and the invasive marbled spinefoot (<em>Siganus rivulatus</em>) were studied via the lens of bioenergetic modeling based on Dynamic Energy Budget theory. Models for the two species were developed and subsequently used to simulate their performance under gradients of temperature and food as well as explore effects of historical changes in temperature between a past, colder (1982–1997), and a recent, warmer (1998–2022), period for the north and south Eastern Mediterranean Sea. Over this time, our results indicate a progressive benefit in growth for the invasive siganid compared to salema, offering, thus, a competitive advantage for the former, which may contribute to the interpretation of its rapid expansion in the region. In addition, the presented models allow for exploration of other effects such as competition for food resources or differences in reproduction traits, which are discussed in light of existing knowledge. This work contributes towards a better understanding of the complex phenomenon of bioinvasions by offering a mechanistic approach to study the metabolism and performance of invasive and native species under the climate-driven, dynamic environment of the Mediterranean Sea.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780415","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":"Reactive nitrogen losses from Canadian agricultural soils over 36 years","authors":"","doi":"10.1016/j.ecolmodel.2024.110809","DOIUrl":"10.1016/j.ecolmodel.2024.110809","url":null,"abstract":"<div><p>Agriculture is a major source of reactive nitrogen (Nr) losses through ammonia (NH₃) volatilization, nitrous oxide (N₂O) emissions and nitrate (NO₃⁻) leaching. A Canadian Agricultural Nitrogen Budget for Reactive N (CANBNr) model was developed to estimate the nitrogen (N) balance in soils, including N removals by harvested crops and Nr losses for the years 1981–2016 across Canada. Annual N inputs to farmland include commercial fertilizer N, livestock manure N, symbiotic and asymbiotic biological N fixation and atmospheric N deposition of NO_x and NH₃. The total annual N input for Canadian farmland was 5528 Gg N (103.2 kg N ha⁻¹) in 2016 where N removal by crops and Nr accounted for 72.5 % (74.9 kg N ha⁻¹) and 13.4 % (13.9 kg N ha⁻¹), respectively. The Nr losses from N₂O emissions, NH₃ volatilization and NO₃⁻ leaching accounted for 64 Gg N (1.2 kg N ha⁻¹), 330 Gg N (6.2 kg N ha⁻¹) and 348 Gg N (6.5 kg N ha⁻¹), which represents between1.2–6.3 % of total N input. A total of 777 Gg N (14.5 kg N ha⁻¹) remained in the soil as surplus N (14.1 % of total N input), which could be available to subsequent crops in dryer regions but might be subject to N₂O loss through nitrification or denitrification processes or NO₃⁻ leaching following heavy rains in humid regions. Nitrous oxide and ammonia emissions increased over a 36-year period due to increased fertilizer N inputs. The percentage of N inputs that was estimated to be lost as Nr increased from 17.9 % in 1981 to the peak level of 19.8 % in 2001 and then declined to 13.4 % in 2016. Total N removal by crops increased at a greater rate than N input during the 2001–2016 period resulting in an increased N uptake by crops over the last 15 years. The improved management of fertilizer N for agricultural systems represents a key opportunity for both farmers and policy makers to further reduce Nr losses from Canadian farmland without negatively impacting productivity.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736347","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":"Site-level and spatially-explicit modelling provides some insights on key factors driving seasonal dynamics of an intertidal seagrass","authors":"","doi":"10.1016/j.ecolmodel.2024.110802","DOIUrl":"10.1016/j.ecolmodel.2024.110802","url":null,"abstract":"<div><p>In a context of worldwide decline and given the critical ecological role of marine seagrasses to coastal ecosystem structure and functioning, regional conservation initiatives have emerged over the past thirty years to protect these important habitat-forming species. Yet, effective interventions need to account for site-specific processes and stressors. Thus, our ability to accurately predict seagrass dynamics is pivotal to support management interventions. To date, determinist process-based modelling has provided important insights on the drivers of seagrass dynamics. Here, we developed an original model framework that combines a coastal hydrodynamics ocean model with local data-driven models that rely on Boosted Regression Trees to predict seasonal dynamics of patch-level and plant-level seagrass features as a function of site-specific environmental conditions. Based only on a 12-month monitoring across nine sites, seagrass traits models successfully reproduce overall seasonal dynamics based mostly on inferred relationships with monthly light and temperature, and to a lesser extent, exposure to physical stressors (i.e., currents and waves). While models fail to finely capture spatial discrepancies across all sites (especially where seagrass demonstrates higher growth potential), spatially-explicit simulations highlight how seagrass-hydrodynamics feedback across the whole bay can dampen seagrass potential for growth due to exposure to shear stress. However, this original framework offers the potential to simulate long-term changes in the extent and status of seagrass meadows in Arcachon Bay, explicit resolving hydro-sediment dynamics effects on light appears as a priority to better capture the range of feedback processes between seagrass and coastal environmental conditions.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S030438002400190X/pdfft?md5=db522fbc756232c40bca8170112abf6b&pid=1-s2.0-S030438002400190X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729369","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":"Predicting the effectiveness of wildlife fencing along roads using an individual-based model: How do fence-following distances influence the fence-end effect?","authors":"","doi":"10.1016/j.ecolmodel.2024.110784","DOIUrl":"10.1016/j.ecolmodel.2024.110784","url":null,"abstract":"<div><p>Wildlife-vehicle collisions on roads pose a major threat to biodiversity and a danger to human motorists. Wildlife fencing prevents animals’ access to roads and reduces road mortality significantly. However, mitigation is often constrained by cost, and fences that are too short can be rendered ineffective because of the fence-end effect where collision locations are shifted towards the fence ends. We created an individual-based model to study processes related to the fence-end effect and predict the effectiveness of fences at preventing road crossings based on fence length, home-range size, and movement distances along the fence. The model was created using the JavaScript programming language, runs in a web browser, and includes a visualization that can help identify emerging patterns. The model generates a mathematical function that relates fence effectiveness to fence length. We parameterized the model for wood turtles (<em>Glyptemys insculpta</em>) and ran simulations for the equivalent of 1 year of movement. We compared 8 fence-following distances and 10 fence lengths up to the home-range diameter. The model recreated patterns characteristic of the fence-end effect, including the presence of high-risk collision zones located at the fence ends. Fence effectiveness was calculated by comparing the number of road encounters prevented by the fence to the number of road encounters without a fence present, and a mathematical function was created to predict effectiveness of fences longer than the home-range diameter. Fences shorter than the home-range diameter ranged from 0 to 69 % effective. Longer fences exhibited significantly higher effectiveness but never reached 100 % due to the fence-end effect. Fence effectiveness dropped proportionately to the animals’ fence-following distance. The predicted effectiveness can be used in road mitigation planning. Empirical data are needed to quantify fence-following behaviors of a range of species as they can significantly influence a fence's effectiveness.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304380024001728/pdfft?md5=d855862029d96a593ba7a6018441acf3&pid=1-s2.0-S0304380024001728-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636851","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":"Navigating simplicity and complexity of social-ecological systems through a dialogue between dynamical systems and agent-based models","authors":"","doi":"10.1016/j.ecolmodel.2024.110788","DOIUrl":"10.1016/j.ecolmodel.2024.110788","url":null,"abstract":"<div><p>Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304380024001765/pdfft?md5=e92096489da6a0716116cfb0523e9389&pid=1-s2.0-S0304380024001765-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729368","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":"The individual-based forest landscape and disturbance model iLand: Overview, progress, and outlook","authors":"","doi":"10.1016/j.ecolmodel.2024.110785","DOIUrl":"10.1016/j.ecolmodel.2024.110785","url":null,"abstract":"<div><p>Forest ecosystems are changing rapidly, and landscape-level processes such as disturbance and dispersal are key drivers of change. Consequently, forest landscape models are important tools for studying forest trajectories under changing environmental conditions and their impacts on ecosystem service provisioning. Here, we synthesize 12 years of development and application of the individual-based forest landscape and disturbance model iLand. Specifically, we describe the fundamental model logic and give an overview of model components introduced over the years. Additionally, we outline how to initialize, evaluate and parameterize the model for new applications. iLand is a process-based forest landscape model that simulates forest dynamics at the level of individual trees. It accounts for continuous processes (tree growth, mortality, and regeneration) as well as discontinuous disturbances (wind, wildfire, and biotic agents) and forest management. Simulations span multiple spatial and temporal scales, from individual trees to landscapes of 10⁵ hectares, and from hourly disturbance dynamics to centuries of forest development. Environmental conditions are represented by daily climate data and high-resolution soil information. The model was designed for flexibly addressing a wide range of research questions, features a rich graphical user interface and comprehensive scripting support. The model is open source and comes with extensive online model documentation. iLand has hitherto been applied in 50 peer-reviewed simulation studies across three continents. Applications primarily focused on the effects of climate change, disturbances and forest management on forest dynamics, ecosystem service provisioning and forest biodiversity. Future model development could address the representation of belowground processes, biotic interactions, and landscape dynamics beyond forest ecosystems. We conclude that process-based simulation of landscape-scale forest dynamics at the level of individual trees has proven a valuable approach of forest landscape modeling.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S030438002400173X/pdfft?md5=8473398ca304c3e91559a8ffbb2aa801&pid=1-s2.0-S030438002400173X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731822","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":"Using Hidden Markov Models to develop ecosystem indicators from non-stationary time series","authors":"","doi":"10.1016/j.ecolmodel.2024.110800","DOIUrl":"10.1016/j.ecolmodel.2024.110800","url":null,"abstract":"<div><p>Ecological indicators are important mechanisms for understanding ecosystem change and implementing Ecosystem Based Fishery Management, but the development of useful indicators must account for ecosystem shifts that result in non-stationary processes over time. This necessitates the adoption of more adaptable statistical modeling approaches. Hidden Markov Models (HMMs) provide a robust framework for distinguishing underlying ecosystem shifts from noisy time-series data. In this paper, we illustrate the power of HMMs to develop model-based ecological indicators of non-stationary systems, focusing on two case studies from the California Current Large Marine Ecosystem. In the first case study, we analyze four temperature time series from 1998 to 2022 that are used as indicators for environmental conditions experienced by juvenile salmon in the northern portion of the system. We apply a three-state HMM incorporating temporal trends to account for non-stationarity in the means over time due to overall ocean warming. Output from this model reveals increasing temperatures for all four metrics in the California Current, with most years being assigned to the warmest estimated state. In our second case study, we analyze nine time series of seabird densities in the northern California Current from 2003 to 2022, to demonstrate how HMMs can be useful to identify sets of indicators that reflect different ecosystem processes, including potential seabird predation pressure on juvenile salmon, and have different variances. We found the strongest support for the existence of two distinct temporal regimes in the seabird data, with an abrupt shift occurring after 2010. While mean densities changed slightly for some species, this regime shift can be best characterized with a shift in variances: sooty shearwaters (<em>Ardenna grisea</em>) and Cassin's auklets (<em>Ptychoramphus aleuticus</em>) represented species with densities becoming more variable, while common murres (<em>Uria aalge</em>) and gulls were estimated to have become less variable after 2010. Common murres, Cassin's auklets, sooty shearwaters, pink-footed shearwaters (<em>Ardenna creatopus</em>) and gulls all represent species that may be useful indicators of change in the northern California Current, because of their differential responses to this regime change. Overall, our analysis provides a first step illustrating the potential applications of HMMs to developing ecosystem indicators in non-stationary systems and a framework that is widely useful for applications to ecosystems around the world.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630653","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":"Climate warming impacts on ringed seal breeding habitat in Svalbard","authors":"Kit M. Kovacs ,&nbsp;Glen E. Liston ,&nbsp;Adele K. Reinking ,&nbsp;Sebastian Gerland ,&nbsp;Christian Lydersen","doi":"10.1016/j.ecolmodel.2024.110790","DOIUrl":"https://doi.org/10.1016/j.ecolmodel.2024.110790","url":null,"abstract":"<div><p>Global warming is occurring at an accelerated rate in the Arctic compared to other parts of the planet with sea-ice declines being among the most striking manifestations of Arctic climate-related changes. Impacts of ongoing Arctic environmental change have been documented for biota throughout marine ecosystems from protists to top predators. Ice-dependent species with specific habitat needs are particularly vulnerable to the ongoing changes. The ringed seal (<em>Pusa hispida</em>) is an ice-associated Arctic endemic species that gives birth and rests in snow caves built in drifts of snow over holes in the sea ice created and maintained by these seals. In this study we create a snow-on-sea-ice reproductive lair habitat model for ringed seals in the Svalbard Archipelago (Norway), a hot-spot of Arctic warming. We use SnowModel, a physics-based snow distribution and evolution simulation system, as the core for a lair habitat model. The model quantifies snow depth and blowing snow fluxes and also relates these variables to snow availability for seal lair habitat. This was accomplished by developing an ecologically informed snow variable that quantifies potential seal lair habitat availability as a function of blowing snow fluxes. Model simulations were performed for the period September 1987 – August 2021 (34 years) on a 500 m × 500 m grid using a daily time-step. Field observations of snow depth and gridded analyses of sea-ice concentration and near-surface (+10 m) atmospheric forcing (air temperature, relative humidity, precipitation, and wind speed and direction) were incorporated within the model simulations. The results show that both snow depth and potential seal lair habitat have been decreasing in Svalbard for the last two decades. If current trends continue, as expected, ringed seal lair habitat will cease to exist across much of the Svalbard Archipelago in the next decade, putting this important Arctic species at risk of regional extirpation.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304380024001789/pdfft?md5=4e7c2ac0eb37196012619fb633c49bd8&pid=1-s2.0-S0304380024001789-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605987","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":"The influence of phytoplankton size fractions on the carbon export ratio in the surface ocean","authors":"Zuchuan Li ,&nbsp;Yajuan Lin ,&nbsp;Nicolas Cassar","doi":"10.1016/j.ecolmodel.2024.110798","DOIUrl":"https://doi.org/10.1016/j.ecolmodel.2024.110798","url":null,"abstract":"<div><p>The fraction of primary production exported out of the surface ocean, also known as the carbon export ratio, is believed to be a function of phytoplankton size fractions. However, this relationship is often elusive in observations. Here, we explore this relationship by developing a metabolism-based mechanistic model of the carbon export ratio at the base of the mixed layer (<span><math><mrow><mi>e</mi><msub><mi>f</mi><mrow><mi>m</mi><mi>l</mi></mrow></msub></mrow></math></span>). Our <span><math><mrow><mi>e</mi><msub><mi>f</mi><mrow><mi>m</mi><mi>l</mi></mrow></msub></mrow></math></span> model is a function of phytoplankton size fractions, temperature, and light and nutrient availability in the mixed layer. Our model delineates a lower bound on <span><math><mrow><mi>e</mi><msub><mi>f</mi><mrow><mi>m</mi><mi>l</mi></mrow></msub></mrow></math></span> as a linear function of biomass ratios between phytoplankton size groups, supporting observational data from three cruises in the Southern Ocean. Finally, we develop a remotely-sensed estimate of <span><math><mrow><mi>e</mi><msub><mi>f</mi><mrow><mi>m</mi><mi>l</mi></mrow></msub></mrow></math></span> incorporating satellite estimates of phytoplankton size fractions. Models like the one presented in this study will benefit from the improved characterization of plankton communities with the upcoming hyperspectral satellite imaging. With the projected shifts in plankton ecosystem structure associated with climate change, projections of air-sea carbon fluxes will require an improved representation of the impact of phytoplankton size fractions on the carbon export ratio.</p><p>Plain language summary: Photosynthesis in the surface ocean converts CO₂ into organic matter, part of which is transferred to depth. The fraction of organic matter transferred to depth is believed to be related to phytoplankton size fractions. For example, an ecosystem dominated by large phytoplankton cells is believed to be more efficient at exporting carbon. However, this relationship is poorly resolved. Here, we develop a model to investigate the influence of phytoplankton size fractions on the fraction of organic matter transferred to depth. Our model is a function of phytoplankton size fractions, temperature, nutrient, and surface irradiance. Our model supports observations of a functional relationship between fraction of organic matter transferred to depth and phytoplankton biomass size fractions. Our model can be used to improve estimates of organic matter export to depth in the world's oceans based on satellite observations.</p></div>","PeriodicalId":51043,"journal":{"name":"Ecological Modelling","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596378","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}