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Stock A, Murray CC, Gregr EJ, Steenbeek J, Woodburn E, Micheli F, Christensen V, Chan KMA. Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 869:161719. [PMID: 36693571 DOI: 10.1016/j.scitotenv.2023.161719] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 01/04/2023] [Accepted: 01/15/2023] [Indexed: 06/17/2023]
Abstract
Understanding the cumulative effects of multiple stressors is a research priority in environmental science. Ecological models are a key component of tackling this challenge because they can simulate interactions between the components of an ecosystem. Here, we ask, how has the popular modeling platform Ecopath with Ecosim (EwE) been used to model human impacts related to climate change, land and sea use, pollution, and invasive species? We conducted a literature review encompassing 166 studies covering stressors other than fishing mostly in aquatic ecosystems. The most modeled stressors were physical climate change (60 studies), species introductions (22), habitat loss (21), and eutrophication (20), using a range of modeling techniques. Despite this comprehensive coverage, we identified four gaps that must be filled to harness the potential of EwE for studying multiple stressor effects. First, only 12% of studies investigated three or more stressors, with most studies focusing on single stressors. Furthermore, many studies modeled only one of many pathways through which each stressor is known to affect ecosystems. Second, various methods have been applied to define environmental response functions representing the effects of single stressors on species groups. These functions can have a large effect on the simulated ecological changes, but best practices for deriving them are yet to emerge. Third, human dimensions of environmental change - except for fisheries - were rarely considered. Fourth, only 3% of studies used statistical research designs that allow attribution of simulated ecosystem changes to stressors' direct effects and interactions, such as factorial (computational) experiments. None made full use of the statistical possibilities that arise when simulations can be repeated many times with controlled changes to the inputs. We argue that all four gaps are feasibly filled by integrating ecological modeling with advances in other subfields of environmental science and in computational statistics.
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Affiliation(s)
- A Stock
- Institute for Resources, Environment and Sustainability, University of British Columbia, AERL Building, 429-2202 Main Mall, Vancouver V6T 1Z4, BC, Canada.
| | - C C Murray
- Fisheries and Oceans Canada, Institute of Ocean Sciences, 9860 West Saanich Road, Sidney, BC V8L 5T5, Canada
| | - E J Gregr
- Institute for Resources, Environment and Sustainability, University of British Columbia, AERL Building, 429-2202 Main Mall, Vancouver V6T 1Z4, BC, Canada; SciTech Environmental Consulting, Vancouver, BC, Canada
| | - J Steenbeek
- Ecopath International Initiative (EII) Research Association, Barcelona, Spain
| | - E Woodburn
- Institute for Resources, Environment and Sustainability, University of British Columbia, AERL Building, 429-2202 Main Mall, Vancouver V6T 1Z4, BC, Canada
| | - F Micheli
- Hopkins Marine Station, Oceans Department, Stanford University, Pacific Grove, CA 93950, USA; Stanford Center for Ocean Solutions, Pacific Grove, CA 93950, USA
| | - V Christensen
- Ecopath International Initiative (EII) Research Association, Barcelona, Spain; Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC, Canada
| | - K M A Chan
- Institute for Resources, Environment and Sustainability, University of British Columbia, AERL Building, 429-2202 Main Mall, Vancouver V6T 1Z4, BC, Canada; Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC, Canada
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A northeast United States Atlantis marine ecosystem model with ocean reanalysis and ocean color forcing. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Gouveia MB, Gherardi DFM, Lentini CAD, Dias DF, Campos PC. Do the Brazilian sardine commercial landings respond to local ocean circulation? PLoS One 2017; 12:e0176808. [PMID: 28489925 PMCID: PMC5425177 DOI: 10.1371/journal.pone.0176808] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2016] [Accepted: 04/18/2017] [Indexed: 11/19/2022] Open
Abstract
It has been reported that sea surface temperature (SST) anomalies, flow intensity and mesoscale ocean processes, all affect sardine production, both in eastern and western boundary current systems. Here we tested the hypothesis whether extreme high and low commercial landings of the Brazilian sardine fisheries in the South Brazil Bight (SBB) are sensitive to different oceanic conditions. An ocean model (ROMS) and an individual based model (Ichthyop) were used to assess the relationship between oceanic conditions during the spawning season and commercial landings of the Brazilian sardine one year later. Model output was compared with remote sensing and analysis data showing good consistency. Simulations indicate that mortality of eggs and larvae by low temperature prior to maximum and minimum landings are significantly higher than mortality caused by offshore advection. However, when periods of maximum and minimum sardine landings are compared with respect to these causes of mortality no significant differences were detected. Results indicate that mortality caused by prevailing oceanic conditions at early life stages alone can not be invoked to explain the observed extreme commercial landings of the Brazilian sardine. Likely influencing factors include starvation and predation interacting with the strategy of spawning “at the right place and at the right time”.
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Affiliation(s)
- Mainara B. Gouveia
- National Institute for Space Research (INPE), Remote Sensing Division, São José dos Campos, São Paulo, Brazil
- * E-mail:
| | - Douglas F. M. Gherardi
- National Institute for Space Research (INPE), Remote Sensing Division, São José dos Campos, São Paulo, Brazil
| | - Carlos A. D. Lentini
- Instituto de Física – Departamento de Física da Terra e do Meio Ambiente, Universidade Federal da Bahia - UFBA, Salvador, Bahia, Brazil
- GOAT - Grupo de Oceanografia Tropical, Universidade Federal da Bahia - UFBA, Salvador, Bahia, Brazil
| | - Daniela F. Dias
- National Institute for Space Research (INPE), Remote Sensing Division, São José dos Campos, São Paulo, Brazil
| | - Paula C. Campos
- National Institute for Space Research (INPE), Remote Sensing Division, São José dos Campos, São Paulo, Brazil
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Carozza DA, Bianchi D, Galbraith ED. Formulation, General Features and Global Calibration of a Bioenergetically-Constrained Fishery Model. PLoS One 2017; 12:e0169763. [PMID: 28103280 PMCID: PMC5245811 DOI: 10.1371/journal.pone.0169763] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 12/21/2016] [Indexed: 11/19/2022] Open
Abstract
Human exploitation of marine resources is profoundly altering marine ecosystems, while climate change is expected to further impact commercially-harvested fish and other species. Although the global fishery is a highly complex system with many unpredictable aspects, the bioenergetic limits on fish production and the response of fishing effort to profit are both relatively tractable, and are sure to play important roles. Here we describe a generalized, coupled biological-economic model of the global marine fishery that represents both of these aspects in a unified framework, the BiOeconomic mArine Trophic Size-spectrum (BOATS) model. BOATS predicts fish production according to size spectra as a function of net primary production and temperature, and dynamically determines harvest spectra from the biomass density and interactive, prognostic fishing effort. Within this framework, the equilibrium fish biomass is determined by the economic forcings of catchability, ex-vessel price and cost per unit effort, while the peak harvest depends on the ecosystem parameters. Comparison of a large ensemble of idealized simulations with observational databases, focusing on historical biomass and peak harvests, allows us to narrow the range of several uncertain ecosystem parameters, rule out most parameter combinations, and select an optimal ensemble of model variants. Compared to the prior distributions, model variants with lower values of the mortality rate, trophic efficiency, and allometric constant agree better with observations. For most acceptable parameter combinations, natural mortality rates are more strongly affected by temperature than growth rates, suggesting different sensitivities of these processes to climate change. These results highlight the utility of adopting large-scale, aggregated data constraints to reduce model parameter uncertainties and to better predict the response of fisheries to human behaviour and climate change.
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Affiliation(s)
- David A. Carozza
- Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec, Canada
- * E-mail:
| | - Daniele Bianchi
- Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec, Canada
| | - Eric D. Galbraith
- Department of Earth and Planetary Sciences, McGill University, Montreal, Quebec, Canada
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Lindstrøm U, Planque B, Subbey S. Multiple Patterns of Food Web Dynamics Revealed by a Minimal Non-deterministic Model. Ecosystems 2017. [DOI: 10.1007/s10021-016-0022-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Maldonado MT, Surma S, Pakhomov EA. Southern Ocean biological iron cycling in the pre-whaling and present ecosystems. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2016; 374:rsta.2015.0292. [PMID: 29035257 DOI: 10.1098/rsta.2015.0292] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/17/2016] [Indexed: 06/07/2023]
Abstract
This study aimed to create the first model of biological iron (Fe) cycling in the Southern Ocean food web. Two biomass mass-balanced Ecopath models were built to represent pre- and post-whaling ecosystem states (1900 and 2008). Functional group biomasses (tonnes wet weight km-2) were converted to biogenic Fe pools (kg Fe km-2) using published Fe content ranges. In both models, biogenic Fe pools and consumption in the pelagic Southern Ocean were highest for plankton and small nektonic groups. The production of plankton biomass, particularly unicellular groups, accounted for the highest annual Fe demand. Microzooplankton contributed most to biological Fe recycling, followed by carnivorous zooplankton and krill. Biological Fe recycling matched previous estimates, and, under most conditions, could entirely meet the Fe demand of bacterioplankton and phytoplankton. Iron recycling by large baleen whales was reduced 10-fold by whaling between 1900 and 2008. However, even under the 1900 scenario, the contribution of whales to biological Fe recycling was negligible compared with that of planktonic consumers. These models are a first step in examining oceanic-scale biological Fe cycling, highlighting gaps in our present knowledge and key questions for future research on the role of marine food webs in the cycling of trace elements in the sea.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'.
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Affiliation(s)
- Maria T Maldonado
- Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, 2207 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4
| | - Szymon Surma
- Institute for the Oceans and Fisheries, University of British Columbia, 2202 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4
| | - Evgeny A Pakhomov
- Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, 2207 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4
- Institute for the Oceans and Fisheries, University of British Columbia, 2202 Main Mall, Vancouver, British Columbia, Canada V6T 1Z4
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Steenbeek J, Buszowski J, Christensen V, Akoglu E, Aydin K, Ellis N, Felinto D, Guitton J, Lucey S, Kearney K, Mackinson S, Pan M, Platts M, Walters C. Ecopath with Ecosim as a model-building toolbox: Source code capabilities, extensions, and variations. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2015.06.031] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Planque B, Lindstrøm U, Subbey S. Non-deterministic modelling of food-web dynamics. PLoS One 2014; 9:e108243. [PMID: 25299245 PMCID: PMC4191973 DOI: 10.1371/journal.pone.0108243] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Accepted: 08/20/2014] [Indexed: 11/24/2022] Open
Abstract
A novel approach to model food-web dynamics, based on a combination of chance (randomness) and necessity (system constraints), was presented by Mullon et al. in 2009. Based on simulations for the Benguela ecosystem, they concluded that observed patterns of ecosystem variability may simply result from basic structural constraints within which the ecosystem functions. To date, and despite the importance of these conclusions, this work has received little attention. The objective of the present paper is to replicate this original model and evaluate the conclusions that were derived from its simulations. For this purpose, we revisit the equations and input parameters that form the structure of the original model and implement a comparable simulation model. We restate the model principles and provide a detailed account of the model structure, equations, and parameters. Our model can reproduce several ecosystem dynamic patterns: pseudo-cycles, variation and volatility, diet, stock-recruitment relationships, and correlations between species biomass series. The original conclusions are supported to a large extent by the current replication of the model. Model parameterisation and computational aspects remain difficult and these need to be investigated further. Hopefully, the present contribution will make this approach available to a larger research community and will promote the use of non-deterministic-network-dynamics models as ‘null models of food-webs’ as originally advocated.
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Affiliation(s)
| | | | - Sam Subbey
- Institute of Marine Research, Bergen, Norway
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