## InVEST Fisheries Production Model

The InVEST Fisheries Production model produces estimates of harvest volume and economic value of single-species fisheries. The model is an age- or stage-structured population model, and is presented as a generic model that can be adapted to most species and geographies.

Fisherieseconomicpopulation modelspecies
571

#### Authorship

Affiliation:
Stanford University
Homepage:
View
Is authorship not correct? Feed back

#### Classification(s)

Application-focused categoriesNatural-perspectiveOcean regions

#### Model Description

English {{currentDetailLanguage}} English

## Summary

Wild capture fisheries provide a significant source of protein for human consumption and directly employ nearly 40 million fishers worldwide (FAO 2014). However, poor harvesting practices and habitat loss and degradation can reduce the ability of ecosystems to support healthy, productive fisheries. The InVEST Fisheries Production model produces estimates of harvest volume and economic value of single-species fisheries. The model is an age- or stage-structured population model, and is presented as a generic model that can be adapted to most species and geographies. Inputs to the model include parameters for life history characteristics (e.g., age at maturity, recruitment, migration and natural mortality rates), behavior of the fishery (e.g., fishing pressure), habitat dependencies (e.g., importance and availability of nursery habitat), and, optionally, economic valuation (e.g., price per unit biomass). The model outputs the volume and economic value of harvest within the area(s) designated by the user. It is best to compare outputs from multiple runs of the model, where each run represents different scenarios of habitat extent, environmental conditions and/or fishing pressure. A library of four sample models is provided, which the user can adapt to their own species or region, or the user can choose to build a model from scratch.

## Introduction

Marine and aquatic ecosystems provide habitat for fish and shellfish, which in turn provide food and livelihoods for millions of people worldwide (FAO 2014). The ability of ecosystems to support fisheries depends on having intact habitat for fish, and on maintaining harvests at sustainable levels. A consideration of how changes in habitat or harvesting practices will impact the production of wild fish is thus important when weighing decisions which impact marine or aquatic ecosystems.

The status and ecology of fish stocks are often assessed by compiling multiple types of data into a single model that gives estimates of production under different scenarios. Unfortunately, such complex stock assessments are often not possible due to a lack of data and/or resources. In addition, traditional stock assessments generally do not take into account habitat dependencies or spatial dynamics, both of which are essential for understanding how local or regional fisheries production might respond under different scenarios. Therefore, a tool is needed that is flexible enough so that it can be adapted to different species, localities, and qualities of data, and which can be used to assess the potential consequences of decisions on the production of wild capture fisheries.

## The Model

The InVEST model of ecosystem services from fisheries is an age- or stage-structured, deterministic, population dynamics model for an individual species. The model uses life-history information and survival parameters provided by the user to estimate the volume of harvest. The model can then be used to explore how the amount of harvest (and, optionally, value) responds to changes in the amount of habitat (e.g., seagrass, mangrove, coral reef), environmental conditions (e.g., temperature, salinity), and/or fishing pressure. It is best to compare outputs from multiple runs of the model, where each run represents different scenarios of habitat extent, environmental conditions, and/or fishing pressure. Fish population dynamics are notoriously variable and difficult to predict. This model is not intended to give a precise prediction of harvest amounts, but rather to be used as a tool to explore the consequences of different decisions which could impact fisheries production.

Parameter sets for four sample models are provided, representing the following species and geographies: (1) Caribbean spiny lobster (Panulirus argus) in Belize; (2) Dungeness crab (Metacarcinus magister) in Hood Canal, Washington; (3) blue crab (Callinectes sapidus) in Galveston Bay, Texas; and (4) white shrimp (Litopenaeus setiferus) in Galveston Bay, Texas. We chose these combinations of species and geographies because they were of interest to our partners in different NatCap application sites. The existing models, and others that will be added as they are developed, capture a range of life history types and exploitation patterns such that users can choose an existing model and modify it for their own region and species (e.g., modify the Galveston Bay white shrimp model for brown shrimp in the South Atlantic). Alternatively, the model is formulated such that a user with more advanced knowledge of fisheries science and modeling techniques can start from scratch and parameterize the generic model to suit any species (or guild) of interest.

Model Domain
{{domain}}

Principles
{{principle}}
Incorporated Models
{{incorporatedModel}}
Model part of larger framework: {{metadata.design.framework}}
Incorporated Models
{{process}}

Inputs
{{input}}
Outputs
{{output}}

#### How to Cite

Natural Capital Project (2019). InVEST Fisheries Production Model, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/32aea340-0b7b-469d-a4ea-ada2a9e53ebf

#### History

Last modifier :
zhangshuo
Last modify time :
2021-01-11
Modify times :
View History

#### Comment(s)

{{comment.date}}
{{comment.content}}
{{subComm.date}}
{{subComm.content}}

#### Authorship

Affiliation:
Stanford University
Homepage:
View
Is authorship not correct? Feedback

#### History

Last modifier :
zhangshuo
Last modify time :
2021-01-11
Modify times :
View History

#### QR Code

• {{curRelation.name}}
{{curRelation.name}}

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}

You can link related {{typeName}} from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

Related Items
{{ props.row.description }}
{{ props.row.description }}
Related Items
{{props.row.name}}

You can link resource from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

{{ props.row.description }}
{{ props.row.description }}
Drop the file here, orclick to upload.
File size should not exceed 10m.
Select From My Space

These authorship information will be submitted to the contributor to review.

Cancel Submit
Model Classifications
Cancel Submit
{{ item.label }} {{ item.value }}
{{props.row.localName}}
Model Name :
Cancel Submit
Name:
Version:
Model Type:
Model Domain:
Scale:
Purpose:
Principles:
Incorporated models:

Model part of

larger framework

Process:
Information:
Initialization:
Hardware Requirements:
Software Requirements:
Inputs:
Outputs:
Cancel Submit
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
Cancel Submit

Yes, this is it Cancel

OK
Cancel Confirm
Model Classifications 1
Model Classifications 2
Title Author Date Journal Volume(Issue) Pages Links Doi Operation

#### NEW

Name:
Affiliation:
Email:
Homepage:

Yes, this is it Cancel

Confirm
path
:
/{{path.label}}
search results of '{{searchContentShown}}'

#### No content to show

{{item.label}}

.

{{item.suffix}}

.{{item.suffix}}

{{item.fileName}}.{{item.suffix}}

Copy
Delete
Rename
/{{path.label}}
Change
/{{path.label}}
Select File
Cancel Confirm
path
:
/{{path.label}}
/..
Cancel Confirm
{{ node.label }}
##### You have select  {{multipleSelection.length+multipleSelectionMyData.length}} data .
• Output Data
• {{item.computableName}}@{{formatDate(item.runTime)}}
{{scope.row.type}}
{{ scope.row.tag }}