Introduction

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Step 1: Projecting Future Fishery Conditions

The purpose of implementing a science-based, sustainable fishery management system is to improve the long-term wellbeing of the individuals and communities who depend on that fishery. This is likely why you are here exploring the FISHE process – you seek to improve the management of your marine resources so that your fishery and community can continue to thrive well into the future. In order to effectively reform our fisheries and guide our management decisions we must first have an understanding of what might be possible in our system so that we don’t set unrealistic goals and set ourselves up for failure.  Some systems can produce millions of tons of yield each year, while others are only capable of much smaller production levels, even if they are managed perfectly.

Traditionally, our understanding of what’s possible and our perspective on what success looks like in any given fishery system have been based on what we have experienced in the past. In other words, we seek to “restore” and “rebuild” our fisheries back to conditions that resemble what they looked like before we fished them too hard. But as climate change progresses, its impacts are altering the most fundamental aspects of marine species and ecosystem functioning. Things that we have historically taken for granted – such as the parts of the world a specific species can inhabit, the “equilibrium” sizes that a population will hover around if given the chance, and even the rate at which a population will reproduce and grow – may be changing. This all means that what was once possible in a given fishery may not be possible in that same fishery in the future.

Adding to the challenge, climate impacts are going to be very different from place to place. As marine species move in an effort to follow their preferred temperature and habitat conditions, impacts on fisheries across the globe may fall into three very general categories. In the highest and lowest latitude regions (e.g., the Arctic), the primary changes will probably involve new species moving in, and new areas opening up to fishing where fishing wasn’t possible before. In the mid-latitudes, primary impacts may involve changes to the mix of species found in any given place and the amount of species biomass available to particular fishing communities, regions, or countries. And, in the equatorial regions, unfortunately, many species are expected to decrease in abundance, or even to disappear completely. However, within these three large-scale generalized patterns there is a significant amount of regional and local variability. For example, upwelling regions will likely experience different patterns of fish abundance and distribution than will non-upwelling areas regardless of latitude, and some parts of the tropics may continue to be productive.

So in order to develop effective, climate-resilient, sustainable fishery management plans that balance conservation and utilization of marine resources over the short- and long-terms, we need to first reimagine what will be possible for our fisheries in the future. This should be based on an understanding of the likely climate change-driven impacts and challenges. Doing so can help us to develop reasonable goals and benchmarks for management, design data collection and scientific assessment regimes that can respond to increased uncertainty, track progress towards these updated benchmarks, and enact effective policies that address changes in fish stocks to facilitate adaptive progress toward our goals. In other words, projections of the likely future conditions in our fisheries should inform our thinking and decision-making at each subsequent step of the FISHE process.

What’s more, modeling different scenarios and projecting different outcomes under different management options can be a powerful motivator of action. Both in contexts where there is a need to garner buy-in and consensus around the need for management reforms, and where different management options are already under consideration, modeling and scenario planning have been used for years in fisheries all over the world to make better-informed decisions. FISHE references a variety of models for projecting fishery outcomes under different management scenarios.

As with stock and habitat status assessments, these types of tools have just recently begun to incorporate climate change data in a meaningful way. Options for data limited fisheries are also available, and we recommend using one or more of these in your fishery to inform your climate-resilient adaptive management plan.

Methods

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Climate Impact Profiling

There are some clear global patterns emerging around the expected (and observed) marine system changes stemming from climate impacts – namely that many species will move poleward to track their preferred temperature conditions, leading to overall increases in species abundance and diversity near the poles, changes in species mixes will occur in the mid-latitudes, and there will likely be overall losses in species abundance near the equator.  However, local and regional conditions as well as the behavior, physiology, and ecology of marine species will determine the actual distribution and abundance of fish stocks.  Hence, there will be a significant amount of variability in species response to climate change at the regional and local scales. This means that in order to make climate-informed decisions, fisheries managers and stakeholders need projections and guidance based on local and regional changes.  While regional projections of physical climate impacts such as temperature, sea level rise, and precipitation changes do exist, projections of fish abundance and distribution in response to these changes are often not available at the regional scale.  For local scale projections, a significant amount of extrapolation and local knowledge will be required, since local projections of climate change impacts are very rare.

The downloadable “Climate Impact Profile Template” is a document designed to direct users to a set of key resources, including literature and published databases of species’ climate vulnerabilities and observed and expected shift patterns, from which they can extract the available information on likely climate change impacts in their systems. The Template provides a format for capturing this information in a way that will be likely to be valuable to fisheries science and management decision-making.

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Comprehensive Assessment of Risk to Ecosystems (CARE) Model

Future ecosystem conditions will be contingent on cumulative impacts from multiple types of threats and stressors facing the system, including climate change impacts and interactions. The Comprehensive Assessment of Risk to Ecosystems (CARE) model is an Excel-based tool designed to systematically analyze the full suite of risks to selected “targets” (valued species and/ or ecosystems) within a spatially explicit site, from all of the potential “threats” that might impact that site. This tool quantitatively considers the interaction of all system threats and assesses the risk to the entire ecosystem through inclusion of a comprehensive suite of attributes to characterize system resilience. These attributes quantify intrinsic system recovery potential (i.e. “regeneration time” and “connectivity”) and sensitivity to impact (i.e. “removability of system components” and “functional redundancy and diversity”). In 2020 the tool was updated to include an explicit climate vulnerability assessment component that allows users in data limited systems to systematically evaluate the expected impact of climate change on their system in a user-selected future time period, as well as their system's vulnerability to that impact. The scoring process was also updated to allow for generation of climate-impacted relative threat risk scores for all threats present in a site in the same future time period.

The CARE model can be used to evaluate risks facing a single site; to compare multiple sites for suitability/necessity of different management options; or evaluate the effects of a proposed management action aimed at reducing one or more risks. Results of the CARE model can be used to identify which threats are the most important in a given site, and for a given target, both now and in a climate-impacted future. This information can help inform where limited management resources should be directed.

The assessments rely largely on local and expert knowledge, require minimal background research to complete, and can be implemented in the field in a matter of hours. These features make the CARE model singularly well-suited for use in Ecosystem Based Management in data-limited systems, for informing spatially explicit management decisions, and for prioritizing threats for efficient use of management resources.

In addition, a set of (Word Doc) questionnaires are provided to facilitate the collection of information necessary to complete a CARE analysis in the field, through interviews with fishers and other system experts. 

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Climate Vulnerability Analysis (CVA) for Marine Species

There are quantitative approaches that can help to assess climate impacts on productivity, abundance, and distribution of various marine fish and invertebrate species. However, it is difficult to apply these approaches to large numbers of species and in many regions of the world owing to the lack of understanding around the mechanisms through which climate-driven changes will impact given species, as well as the lack of scientific capacity to support these more detailed studies. Climate vulnerability assessments provide a framework for evaluating climate impacts over a broad range of species with existing information, thus making them more accessible in data-limited settings. These methods combine the exposure of a species to climate change and decadal variability and the sensitivity of species to these stressors. These two components are then combined to estimate an overall vulnerability for each species in the assessment. Quantitative data are used when available, but qualitative information and expert opinion are used when quantitative data is lacking. The US Northeast Climate Vulnerability Assessment (CVA) that was developed by scientists at the National Oceanographic and Atmospheric Administration (NOAA) serves as a useful example for how to conduct a regional climate vulnerability assessment.

In the original approach, there are four steps:

  1. Scoping and planning the assessment, which includes: i) identifying the spatial region, ii) the species to include, iii) the climate variables to include as exposure factors, iv) the biological and ecological traits to include as sensitivity attributes, and v) recruiting experts to participate in the assessment;
  2. Preparation of materials for the assessment, which includes: i) consolidating available information on each species, ii) obtaining information on the future state of exposure factors, and iii) providing the spatial overlap between climate exposure and species distributions in the region;
  3. Expert scoring of the different components of the assessment, which includes: i) climate exposure scoring, ii) sensitivity attribute scoring, iii) quantifying expert certainty in scoring, iv) scoring the directional effect of climate change on a species in the region, and v) data quality scoring, and
  4. Analysis of the scores, which includes: i) estimating overall climate vulnerability, ii) estimating potential for a distribution change using a subset of sensitivity attributes, iii) estimating certainty in overall climate vulnerability, potential for a distribution change, and the directional effect of climate change using bootstrapping; iv) identifying the importance of each exposure factor and sensitivity attribute to the overall climate vulnerability using a leave-one out sensitivity analysis, v) evaluating the results on a functional group basis, and vi) developing species-specific narratives that summarize the results for each species.

Steps 1iii and 1iv, the selection of exposure factors and biological and ecological traits to include as sensitivity attributes represent some of the main components of the analysis. Exposure is a measure of the projected magnitude of change in the physical environment due to climate. Exposure factors are those climate variables included in the assessment that could affect a species (e.g., temperature, salinity). The exposure score includes information about the magnitude of the expected climate change, but not in relation to each species’ tolerances, which are often unknown. Sensitivity attributes represent biological traits that are indicative of an ability or inability of a species to respond to environmental change. The table below illustrates the exposure factors and sensitivity attributes used in the approach designed for the U.S. Northeast Shelf region.

Step 1, Table 1: Climate Exposure Factors and Biological Sensitivity Attributes used in NOAA Climate Vulnerability Analysis

Climate Factor or Biological Attribute

Goal

Low Score

High Score

Climate Factors (Exposure)

Mean Ocean Surface Temperature

To determine if there are changes in mean ocean surface temperature between a current and future period.

Low magnitude of change

High magnitude of change

Mean Ocean Surface Salinity

To determine if there are changes in mean ocean surface salinity between a current and future period.

Low magnitude of change

High magnitude of change

Mean Air Temperature

To determine if there are changes in mean air temperature between a current and future period. Air temperature is a proxy for water temperatures in freshwater.

Low magnitude of change

High magnitude of change

Mean Precipitation

To determine if there are changes in mean precipitation between a current and future period. Precipitation is a proxy for streamflow.

Low magnitude of change

High magnitude of change

Mean Ocean pH

To determine if there are changes in mean ocean pH between a current and future period. pH represents ocean acidification.

Low magnitude of change

High magnitude of change

Variability in Ocean Surface Temperature

To determine if there are changes in variability of ocean surface temperature between a current and future period.

Low magnitude of change

High magnitude of change

Variability in Ocean Surface Salinity

To determine if there are changes in variability of ocean surface salinity between a current and future period.

Low magnitude of change

High magnitude of change

Variability in Air Temperature

To determine if there are changes in variability of air temperature between a current and future period. Air temperature is a proxy for water temperatures in freshwater.

Low magnitude of change

High magnitude of change

Variability in Precipitation

To determine if there are changes in variability of precipitation between a current and future period. Precipitation is a proxy for streamflow.

Low magnitude of change

High magnitude of change

Variability in pH

To determine if there are changes in variability of ocean pH between a current and future period. pH represents ocean acidification.

Low magnitude of change

High magnitude of change

Sea Level Rise

To evaluate the magnitude of sea level rise relative to the ability of nearshore habitats to change.

Low magnitude of change

High magnitude of change

Ocean Currents

To evaluate changes in large-scale circulation.

Low magnitude of change

High magnitude of change

Biological Attributes (Sensitivity)

Prey Specificity

To determine, on a relative scale, if the stock is a prey generalist or a prey specialist.

Prey generalist

Prey specialist

Habitat Specificity

To determine, on a relative scale, if the stock is a habitat generalist or a habitat specialist while incorporating information on the type and abundance of key habitats.

Habitat generalist

Habitat specialist

Sensitivity to Ocean

Acidification

To estimate a stocks sensitivity to ocean acidification based on its relationship with “shelled species.

Sensitive taxa

Insensitive taxa

Complexity in Reproductive

Strategy

To determine how complex the stocks reproductive strategy is and how dependent reproductive success is on specific environmental conditions.

Low complexity,

broadcast spawning

High complexity;

aggregate spawning

Sensitivity to Temperature

To use the distribution of the species as a proxy for its sensitivity to temperature. Note: that this attribute uses species (vs. stock) distributions as they better predict thermal requirements.

Broad thermal limits

Narrow thermal limits

Early Life History Survival and Settlement Requirements

To determine the relative importance of early life history requirements for a stock.

Generalist with few

requirements

Specialists with

specific requirements

Stock Size/Status

To estimate stock status to clarify how much stress from fishing the stock is experiencing and to determine if the stocks resilience or adaptive capacity are compromised due to low abundance.

High abundance

Low abundance

Other Stressors

To account for conditions that could increase the stress on a stock and thus decrease its ability to respond to changes.

Low level of other

stressors

High level of other

stressors

Population Growth Rate

To estimate the relative productivity of the stock.

High population

growth

Low population

growth

Dispersal of Early Life

Stages

To estimate the ability of the stock to colonize new habitats when/if their current habitat becomes less suitable.

High dispersal

Low dispersal

Adult Mobility

To estimate the ability of the stock to move to a new location if their current location changes and is no longer favorable for growth and/or survival.

High mobility

Low mobility

Spawning Cycle

To determine if the duration of the spawning cycle for the stock could limit the ability of the stock to successfully reproduce if necessary conditions are disrupted by climate change.

Year-round spawning

 

The NOAA CVA for the U.S. Northeast Shelf utilized a four-step process to generate climate vulnerability scores from these attributes:

  1. Component scores (low, moderate, high and very high) were assigned a numerical value (1, 2, 3, and 4).
  2. An average score for each climate exposure factor and sensitivity attribute was calculated as the weighted-mean of the experts’ tallies.
  3. An overall exposure and sensitivity score was calculated from the weighted means using a logic rule where a certain number of individual scores above a certain threshold are used to determine the overall climate exposure and overall biological sensitivity (see Table 2).

Step 1, Table 2: Qualitative and Quantitative Values for Overall Sensitivity and Exposure Scores based on Logic Rule used in NOAA CVA

Overall Sensitivity or Exposure Score

Numeric Score

Logic Rule

Very High

4

3 of more attributes or factors mean ≥ 3.5

High

3

2 of more attributes or factors mean ≥ 3.0

Moderate

2

2 of more attributes or factors mean ≥ 2.5

Low

1

All other scores

  1. An overall climate vulnerability score was calculated by multiplying the overall exposure and sensitivity scores. The product of the two component numeric scores results in a value between 1 and 16. The overall climate vulnerability rank is then classified as follows: 1–3 low, 4–6 moderate, 8–9 high, and 12–16 very high.

We are currently working on developing a simplified, “desktop” version of this CVA that can more easily be applied in data- and capacity-limited contexts. In the meantime, users can find more information on the NOAA analysis in the linked manuscript by Hare et al. (2016). The NOAA Climate Portal may be helpful to access information on five of the 12 climate exposure factors above: Mean Ocean Surface Temperature, Mean Ocean Surface Salinity, Mean Air Temperature, Mean Precipitation, and Mean Ocean pH. It is recommended that the user try to score each of the 12 sensitivity attributes listed in the table. A combination of published studies, information from databases such as www.Fishbase.org or www.SeaLifeBase.org, and local knowledge could be used to provide information for the sensitivity attributes.