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Identification of a spatially efficient portfolio of priority conservation sites in marine and estuarine areas of Florida.

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AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
Published online 25 November 2008 in Wiley InterScience
(www.interscience.wiley.com). DOI: 10.1002/aqc.992
Identification of a spatially efficient portfolio of priority
conservation sites in marine and estuarine areas of Florida
LAURA GESELBRACHTa,, ROBERTO TORRESa, GRAEME S. CUMMINGb,y, DANIEL DORFMANc,z
MICHAEL BECKc and DOUGLAS SHAWa
a
The Nature Conservancy, 222 S. Westmonte Drive, Suite 300, Altamonte Springs, Florida 32714 USA
b
Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida 32611
c
The Nature Conservancy, University of California Santa Cruz, Center for Ocean Health, Santa Cruz, California 95060 USA
ABSTRACT
1. A systematic conservation planning approach using benthic habitat and imperilled species data along with
the site prioritization algorithm, MARXAN, was used to identify a spatially efficient portfolio of marine and
estuarine sites around Florida with high biodiversity value.
2. Ensuring the persistence of an adequate geographic representation of conservation targets in a particular
area is a key goal of conservation. In this context, development and testing of different approaches to spatiallyexplicit marine conservation planning remains an important priority.
3. This detailed case study serves as a test of existing approaches while also demonstrating some novel ways in
which current methods can be tailored to fit the complexities of marine planning.
4. The paper reports on investigations of the influence of varying several algorithm inputs on resulting portfolio
scenarios including the conservation targets (species observations, habitat distribution, etc.) included,
conservation target goals, and socio-economic factors.
5. This study concluded that engaging stakeholders in the development of a site prioritization framework is a
valuable strategy for identifying broadly accepted selection criteria; universal target representation approaches
are more expedient to use as algorithm inputs, but may fall short in capturing the impact of historic exploitation
patterns for some conservation targets; socio-economic factors are best considered subsequent to the
identification of priority conservation sites when biodiversity value is the primary driver of site selection; and
the influence of surrogate targets on portfolio selection should be thoroughly investigated to ensure unintended
effects are avoided.
6. The priority sites identified in this analysis can be used to guide allocation of limited conservation and
management resources.
Copyright r 2008 John Wiley & Sons, Ltd.
Received 28 November 2007; Revised 16 April 2008; Accepted 26 May 2008
KEY WORDS:
systematic conservation planning; geographic representation; resilience; marine reserve
INTRODUCTION
Conservation efforts to protect biodiversity and maintain the
integrity of ecological systems are limited by time, funding and
staff resources. Often, conservation need is urgent. In the past,
decisions regarding the selection of priority marine
conservation areas have frequently been made on an ad hoc,
as needed, or opportunity driven basis (Roberts et al., 2003a).
While the designation of many managed areas has been
valuable for conservation goals, some of the most ecologically
important areas in a particular planning region may have been
missed and some resources may be under-represented. A range
*Correspondence to: Laura Geselbracht, The Nature Conservancy, 222 S. Westmonte Drive, Suite 300, Altamonte Springs, Florida 32714 USA.
E-mail: lgeselbracht@tnc.org
y
Present Address: Percy FitzPatrick Institute, DST/NRF Center of Excellence, University of Cape Town, Cape Town, South Africa.
z
Present Address: Intelligent Marine Planning, St Petersburg, Florida 33704 USA.
Copyright r 2008 John Wiley & Sons, Ltd.
IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA
of more systematic approaches, which may include the use of
site selection algorithms, have been pioneered in recent times
to facilitate the identification of priority marine and estuarine
sites (Pressey et al., 1994; Ball and Possingham, 2000;
Possingham et al., 2000; Salm et al., 2000; Beck, 2003;
Groves, 2003; Leslie, 2005). Despite their relevance, many of
these tools have had little application in real-world marine
conservation planning. Consequently there is a clear need for
further case studies that explore the strengths and weaknesses
of different systematic approaches and develop novel ways of
applying these methods in cases where conventional
applications are difficult or inappropriate. This article seeks
to contribute to the further development of systematic marine
conservation planning efforts through a detailed exploration
of their application in the state of Florida, USA.
Systematic conservation approaches generally require
identifying the best sites for concentrating conservation and
management activities in a particular planning region. Site
selection may be influenced by how much of the conservation
targets are represented at the sites, the redundancy of the
occurrences (to spread the risk in the face of stress), viability of
the resource occurrences represented, and connectivity between
occurrences to ensure replenishment and genetic diversity
(Shaffer and Stein, 2000; Groves, 2003). Economic and social
criteria may also be applied to either select or avoid areas of high
importance (Hockey and Branch, 1997; Roberts et al., 2003b).
Which selection criteria to use and how their use is applied will be
driven by the specific goals of each planning effort.
Site selection algorithms, such as MARXAN, have been
developed to accept multiple selection criteria as inputs and
have been used to identify priority conservation areas (e.g.
marine reserve networks) for numerous seascapes over the last
several years (Beck and Odaya, 2001; Floberg et al., 2004;
DeBlieu et al., 2005). The basic aim of these optimization
algorithms is to achieve conservation target goals in a spatially
efficient manner. Such algorithms allow for the evaluation of
dozens of conservation targets and hundreds of possible
conservation sites in a practically limitless arrangement. In
the past, such efforts have relied solely on expert opinion to
identify priority sites, but the vast number of configurations to
evaluate makes this a daunting task (Leslie et al., 2003). The
use of site selection algorithms to identify priority conservation
sites does not replace the need to engage resource experts, but
rather simplifies the task and provides a tool for explicit
identification of selection criteria and trade-offs.
In areas such as Florida where an extensive system of existing
marine and estuarine managed sites already exists, systematic
conservation planning using site selection algorithms can be used
to investigate the adequacy of the existing management regime
and provide a means for prioritizing the allocation of conservation
and management resources (funding and staff) around the
planning area. This study explored the influence of a number of
selection criteria on the development of a portfolio of priority
marine and estuarine conservation sites based on spatially efficient
representation of conservation features. It utilized existing biotic
resources and socio-economic datasets along with the site selection
algorithm, MARXAN, to identify several alternative portfolios of
priority sites. The results were vetted through a series of expert
workshops and utilized the collective advice of reviewers to
develop a preferred portfolio. The determination of the most
appropriate conservation actions to take at any particular site is
left to more detailed site planning processes.
Copyright r 2008 John Wiley & Sons, Ltd.
409
METHODS
MARXAN and existing geospatial datasets of marine and
estuarine resources were utilized to develop several potential
conservation portfolios for the marine and estuarine areas
surrounding Florida. To accomplish this, planning area
boundaries and subregions, conservation targets, appropriate
target distribution and socio-economic use datasets, and
alternative approaches for setting representation goals were
identified. An index for spatially representing socio-economic
activities likely to have an ‘irreversible’ adverse impact on
biodiversity and/or resource viability was also created.
Evaluation of the efficiency of the portfolio scenarios in
terms of spatial representation and attainment of conservation
target goals was carried out. Expert review workshops were
held throughout the priority site identification process, and the
comments from these were utilized, including those on a
number of alternative portfolios developed to help shape
selection of a preferred alternative.
Planning area and geographic stratification
This study encompassed marine and estuarine habitats
surrounding the state of Florida in the USA. The 1350 mile
Florida coastline supports a diverse and productive
assemblage of marine and estuarine systems. Owing to its
position (i.e. jutting from the North American continent south
towards the Caribbean Sea), Florida’s marine systems range
from temperate in the north to tropical in the south. Extensive
salt marsh and mangrove systems grace Florida’s low energy
coastlines. Beach and barrier island complexes are found in
higher energy areas. Vast, globally significant seagrass
meadows are present along portions of the Gulf Coast and
in Florida Bay. Large expanses of mangrove forests dominate
coastal areas of South Florida, which is also home to the third
largest barrier reef tract in the world. Large and small estuaries
and coastal rivers occur all along the Florida coast. These
highly productive systems support key life stages of many
commercially and recreationally important species such as
pink shrimp, stone crab, lobster, scallops, clams, groupers,
snappers, mullet and numerous other species of game fish.
The Florida planning area for the project was broadly
defined by setting the offshore boundary at the 500 m isobath
which comfortably encompasses the continental margin. The
inland boundary was defined by the zone of saltwater influence
as identified by the National Wetlands Inventory marine and
estuarine classifications (USFWS, 1979). The total area
described by these boundaries is approximately 28.4 million
hectares. To capture biogeophysical differences over this large
planning area, it was divided into eight subregions based
primarily on coastal geomorphology and faunal assemblages
(Davis, 1997; Randazzo and Halley, 1997; Lodge, 2005)
(Figure 1).
Conservation targets and data sources
For the priority areas analysis, benthic habitats and vulnerable
species were selected as conservation targets to represent
biodiversity. A hierarchical marine and estuarine habitat
classification scheme developed by the State of Florida
(System for Classification of Habitats in Estuarine and
Marine Environments) was used to categorize the habitat
types used in this analysis (Madley et al., 2002). The most
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
410
L. GESELBRACHT ET AL.
Figure 1. Planning area and subregions designated for this study. Outer planning area boundary is the 500 m isobath. Inland boundary is the zone of
saltwater influence as identified by the National Wetlands Inventory (USFWS, 1979). Subregions are named as follows: 1 — Northeast Florida, 2 —
East-Central Florida, 3 — Southeast Florida, 4 — Florida Keys/Florida Bay, 5 — Southwest Florida/Ten Thousand Islands, 6 — West-Central
Florida, 7 — Big Bend, and 8 — Northwest Florida/Panhandle.
recent geospatial datasets available were assembled for each
broad marine and estuarine benthic habitat category. Selected
datasets were limited to those that covered at minimum an
entire subregion. Datasets were primarily obtained from state,
federal and local government agencies and mostly covered only
areas within state waters. In three cases datasets were derived.
The coastal tidal river or stream dataset was derived from a
data set representing the inland limit of tide available through
the State of Florida and the National Hydrography Dataset.
The fish spawning aggregation dataset was derived from
interviewing fishermen and comparing with similar data
collected by Environmental Defense (K. Lindeman, personal
communication, 2005). The third derived dataset, benthic
complexity, was created from bathymetry data. It was utilized
to provide some means for predicting ecological importance
where little or no geospatial data were available on benthic
habitats in the planning area. Benthic complexity was the only
dataset used in the study that covered almost the entire study
area. Benthic complexity was estimated using an ArcInfo GIS
model developed by Duke University Marine Geospatial
Ecology Laboratory (2005). The model used bathymetry
data (90 m grid-scale resolution) and four geophysical
features: depth, topographic variety, amplitude of
topographic change and substrate type. The model is based
on the strong correlation between benthic complexity and
Copyright r 2008 John Wiley & Sons, Ltd.
species richness (Ardron, 2002). Topographic variety was
classified as flat, slope, ridge and canyon. Sediment classes
were extrapolated from data in the Atlantic and Gulf States
Marine Fisheries Commissions’ Southeast Area Monitoring
and Assessment Program (SEAMAP) and the USGS
usSEABED Project. Application of the resulting model
identified areas of relatively more complex bottom
topography. Benthic complexity could not be calculated for
a few areas where bathymetry data were unavailable, including
some shallow areas (o50 m) of the Big Bend Subregion and
the Florida Bay/Ten Thousand Islands area.
Ecologically vulnerable species targets and/or aggregations
were also included in the site selection process to ensure the
inclusion of ecologically important sites that might not
otherwise be identified from benthic habitat information
alone. The number of species targets included was limited to
ensure balance with the habitat targets, and to avoid a
situation in which species targets alone drove algorithm
results. The number of species targets included in this
analysis was limited by using the objective selection criteria
identified below:
Globally, regionally or state imperilled species (G1-G2/
G3), S1–S3, State Species of Special Concern — SSC),
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA
IUCN red-listed, federally listed/candidate species and
American Fisheries Society threatened or endangered
distinct population segments; and
Vulnerable breeding aggregations subject to harvest or
other forms of direct human disturbance (e.g. marine
dependent bird nesting aggregations and known fish
spawning aggregations).
In all, 12 benthic habitat and 35 species/aggregation targets
were included in the study. Species targets were only included
in subregions where they are known to occur, and where data
on their distribution were uniformly collected for most of the
subregion, or data had been collected over a long enough
period that the discovery of a significant number of additional
occurrence sites is not anticipated. The set of conservation
targets used in the analysis is listed in Table 1. Most of the
conservation targets that were used in this study only covered
areas within state waters (3 nautical miles on the Atlantic
Coast and 9 nautical miles on the Gulf of Mexico coast) with
the exception of benthic complexity, marine hardbottom, inwater sea turtles and the well-documented deep Oculina banks
(Reed, 2004).
Selection of priority sites
The optimization algorithm, MARXAN was used to aid in
identification of priority sites. MARXAN was developed by
Hugh Possingham and Ian Ball of the University of
Queensland, Australia, and along with related site selection
411
algorithms has been used for a variety of marine applications
(Ball and Possingham, 2000; Possingham et al., 2000).
MARXAN enabled the specified conservation target goals to
be achieved while minimizing the total area of all sites selected.
While biodiversity and ecosystem conservation objectives may
direct us towards maximizing the size and number of
conservation areas, practical considerations of funding and
staffing levels lead towards the identification of focal areas in
which to concentrate efforts. MARXAN seeks to minimize
total cost of the selected sites using the following objective
function:
X
X
Total Cost ¼
Cost site i þ
Penalty cost for element j
i
þ wb
X
j
boundary length
Cost may be represented as total area of the sites, but may also
include socio-economic costs. Socio-economic costs may be
thought of in terms of existing human activities or structures
that may render conservation more expensive. Penalty cost,
also known as the species penalty factor, represents the penalty
given for not adequately representing a conservation feature
and is summed for all conservation features. Boundary length
is the total perimeter of all the sites. Its inclusion in the
objective function controls fragmentation. Fragmentation of
the solution can be reduced (i.e. clumpiness of the solution
increased) by setting the constant Wb to a value greater than 0.
If the constant is given a value of 0, then boundary length has
no effect on the solution. Three optimization methods are used
for the objective function: the iterative improvement
Table 1. Conservation targets represented in site prioritization framework
Benthic habitat
Species and vulnerable aggregations
Coral reef (incl. deep Oculina Banks)
Mangrove forest
Beach/surf zone
Salt marsh
Submerged aquatic vegetation
Coastal tidal river or stream
Tide flats
Marine hardbottom
Bivalve reef (Oyster)
Annelid worm reef (Sabellariidae)
Ocean inlets and passes
Benthic complexity
Florida manatee, Trichechus manatus latirostris
Northern right whale, Eubalaena glacialis
American oystercatcher, Haematopus palliatus
Black skimmer, Rynchops niger
Brown pelican, Pelecanus occidentalis
Least tern, Sterna antillarum
Piping plover, Charadrius melodus
Reddish egret, Egretta rufescens
Roseate spoonbill, Ajaia ajaia
Roseate tern, Sterna dougallii
Snowy egret, Egretta thula
Snowy plover, Charadrius alexandrinus tenuirostris
Waterbird nesting sites
American crocodile, Crocodylus acutus
Sea turtle nesting sites
Sea turtles, in-water surveys
Diamondback terrapin (Ornate, Mississippi and Carolina), Malaclemys terrapin
macrospilota, M.t. pileata and M.t. centrata
Smalltooth sawfish, Pristis pectinata
Slashcheek goby, Ctenogobius pseudofasciatus
River goby, Awaous banana
Bigmouth sleeper, Gobiomorus dormitor
Mangrove Rivulus, Rivulus marmoratus
Opossum pipefish, Microphis brachyurus lineatus
Striped croaker, Bairdiella sanctaeluciae
Gulf sturgeon, Acipenser oxyrhynchus desotoi
Atlantic sturgeon, Acipenser oxyrhynchus oxyrhynchus
Shortnose sturgeon, Acipenser brevirostrum
Saltmarsh topminnow, Fundulus jenkinsi
Alabama shad, Alosa alabamae
Key silverside, Menidia conchorum
Elkhorn coral, Acropora palmata
Johnson’s seagrass, Halophila johnsonii
Spawning aggregations, harvested species
Copyright r 2008 John Wiley & Sons, Ltd.
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
412
L. GESELBRACHT ET AL.
algorithm, simulated annealing algorithm and greedy heuristic.
MARXAN starts with an initial random solution, compares
sequential random changes to the original solution and selects
the better of the two for the next iteration. As this iterative
process continues, MARXAN becomes increasingly selective
as it considers improved alternative solutions. A full
description of MARXAN can be found at the University of
Queensland, Ecology Centre website (www.ecology.uq.edu.au/
marxan.htm).
The parameter values selected for this analysis and the
rationale for doing so are described below:
Planning units
The planning area was divided into 1500 ha hexagonal
planning units resulting in a total of 18 943 units. The
1500 ha planning unit size was selected to provide fine
enough detail to resolve habitat within coastal features
(especially within bays and estuaries) for statewide analysis
while not overwhelming processing capabilities or exceeding
the resolution of the habitat data.
Conservation target goals
The amount of each target required to meet conservation goals
is a key planning parameter and input to MARXAN. In
priority conservation site identification, the purpose may be to
identify the minimum amount of each habitat type required to
maintain fully viable populations and communities into the
future. So far, there is no universal agreement on what this
representation should be or how representation should be
derived. Several publications on the topic suggest that target
representation for such purposes be set between 20% and 40%
of the habitat’s historic distribution (Roberts and Hawkins,
1999; Ward et al., 1999; Turpie et al., 2000; Beck, 2003;
Groves, 2003).
This study aimed to identify a set of priority sites on which
to focus scarce management resources or conservation dollars.
While it was recognized that all areas are potentially important
for natural resource conservation, the goal in this study was to
identify areas to direct limited financial resources, staff and
time with the hope that conservation successes in these areas
could be used as leverage to achieve conservation in adjacent
areas or at a larger scale. As a means of identifying an
appropriate target representation approach, several
representation scenarios were explored, including two
universal representation scenarios (20% and 40%
representation for all conservation targets) and a variable
representation approach developed by The Nature
Conservancy (2003). In the variable target representation
approach, individual target representation was based on four
attributes: degree of rarity, vulnerability to human activities,
current status compared with historic and whether the target
represents a breeding site such as nesting colony or spawning
aggregation, etc. Three of the attributes (degree of rarity,
vulnerability to human activities, and current status compared
with historic) were rated on a scale of 1 to 3, with 1 being less
rare, vulnerable or compromised and 3 being more rare,
vulnerable or compromised. The breeding site attribute was
rated as either a ‘1’ for not a breeding site or a ‘3’ for breeding
site. All scores were based on information available in the
scientific literature. Representation for each target was then
assigned, ranging from 20% to 100%, based on the total
attribute score (Figure 2).
Boundary length
Boundary length or perimeter of the MARXAN solution
(boundary of all solution sites added together) can be modified
using the boundary length modifier (Wb), a constraint entered
into the MARXAN algorithm. Adjustment of this variable
affects the spatial arrangement and efficiency of the results.
MARXAN allows Wb to be set at values between 0 and 1. A
Wb of 0 returns the most spatially efficient result with no
consideration given to arrangement of the selected sites which
are typically small (single planning unit) and scattered
throughout the planning area. As the Wb value is increased,
overall site number is reduced and sites become more
aggregated (multiple planning unit sites). Adjustment of this
variable allows for the development of solutions that make
more sense from a practical conservation or resource
management perspective as attempting to manage thousands
of small sites scattered throughout the planning area would be
difficult and expensive. A Wb value of 0.05 was used in the
analyses presented here.
Planning unit cost
MARXAN requires that each planning unit be assigned a base
cost as the algorithm seeks to minimize cost in its optimum
solution. The cost function in the algorithm may also be used
to vary the relative value of each planning unit depending on
their attributes. The effect of varying planning unit cost is that
those with a lower cost have a greater likelihood of being
selected in the MARXAN output all other factors being equal.
Two approaches for assigning values to planning units were
Figure 2. Assignment of variable representation factors. Representation of each target in each subregion was scored based on four attributes: rarity
vulnerability, current status as compared to historic condition, and whether the distribution coverage represents a breeding aggregation.
Representation factors ranging from 20% to 100% were assigned based on the frequency distribution of target scores.
Copyright r 2008 John Wiley & Sons, Ltd.
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
413
IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA
Table 2. Socio-economic use index - factors and scoring for each planning unit
Socio-economic factors
Most Concentrated Impacts:
Major NPDES discharges
Superfund sites (range, 1–2)
Offshore dredge disposal sites (range, 1–2)
More Dispersed Impacts:
Major shipping lanes (uptonnageX272,155
metric tons)
Most Dispersed Impacts:
Hardened shoreline (range, 0.003–115 km)
Dredged shipping channels (range, 1–15)
Port facilities (range, 1–31 facilities)
Population density (range, 0.000218/km2
–18998/km2)
Road density (range, 0.004–246 km)
Marine facilities/boat ramps (range: 1–33)
Unit
Index Points
Data source
Presence in planning unit
For each site
For each site
500
500
500
NOAA-OPIS
NOAA-OPIS
NOAA-OPIS
Presence in planning unit
250
USACE - Navigation
Data Center (NDC)
For every 2.5 kilometres
For each dredging project
For each facility
For each 38.6 people per
square kilometre
100
25
20
10
FWC/FWRI
USACE- NDC
USACE- NDC
U.S. Census
Census 2000
For each kilometre
10
For each facility/boat ramp
10
U.S. Census
Census 2000
FWC/FWRI
Note: Socio-economic index points were added to the planning unit base cost of 85 to derive the total planning unit cost. NPDES: National
Pollutant Discharge Elimination System (http://cfpub.epa.gov/npdes/).
examined. In the first approach, all planning units were valued
equally, and assigned a base cost of 85 points. In this type of
approach, where all planning units are valued equally, the
value of the planning unit cost selected is not important as long
as it is greater than 0 and is in balance with the species penalty
factor (discussed below).
The second approach to assigning costs to planning units,
involved the development of a spatial index of socio-economic
factors. The purpose of this approach was to better inform
marine resource managers and conservation practitioners
where priority attention could be focused while minimizing
conflicts with existing resource users and avoiding more
impacted locations. The spatial socio-economic index was
composed of activities not likely to be reversible or reversible
only at a high to very high cost. Only socio-economic uses that
have a demonstrated adverse impact on native habitats and
species (e.g. development, high intensity use and pollution) and
may be described as structures, facilities or activities were
considered for inclusion. Eleven such socio-economic uses
impacting marine and estuarine habitats in Florida where
geospatial information was available were identified. The 10
socio-economic factors selected and the scoring used to
describe the inlevel of impact are listed in Table 2 and
include population and road density, port facilities, major
shipping lanes, hardened shorelines, Superfund sites, major
National Pollutant Discharge Elimination System permitted
point source (NPDES) discharges, marine facilities and boat
ramps, offshore dredged disposal sites, and dredged shipping
channels.
Relative scoring of the socio-economic factors was
developed and refined through an iterative expert review
process that included marine resource managers, scientists and
conservationists working in Florida. Point locations of the
most concentrated adverse impacts (e.g. major municipal
discharges and Superfund sites) were assigned the highest score
of 500 points. More dispersed, but still intensive impacts (i.e.
shipping lanes with greater than 272 155 metric tons annual
uptonnage) were assigned 250 points along the entire shipping
lane length. The most dispersed socio-economic factors (e.g.
population, roads, hardened shoreline) were assigned scores
ranging from 10 to 100 points per unit of measure. Scores for
Copyright r 2008 John Wiley & Sons, Ltd.
these most dispersed socio-economic factors are lower because
multiple factor units may occur within a single planning unit
(see Table 2 for the range of units for each socio-economic
factor). For example, of the planning units with the highest
socio-economic index scores (total score ranging from 4000 to
6212), none contain a Superfund site, major wastewater
discharge or offshore dredged disposal site. The high socioeconomic scores for these planning units are derived primarily
from population density, road density, and hardened
shoreline. A map depicting the socio-economic index is
shown in Figure 3. There are other threat factors that would
have been advantageous to include in this analysis if the time
and resources had been available, including trawling intensity,
observed seagrass propeller scarring and coastal watershed
impacts, etc. Where the described socio-economic uses
were present, the cost points outlined in the socio-economic
index were added to the base cost of 85 points for each
planning unit.
Species penalty factor
A species penalty factor is set for each conservation target and
represents the cost added to the total portfolio cost if the
conservation target goal is not met. Setting a high species
penalty helps to ensure that MARXAN will meet conservation
target goals. A species penalty factor of 500 was applied to all
conservation targets for all scenarios presented here.
Algorithm application
For each MARXAN run, the number of iterations was set at
10 million to ensure a high degree of confidence in optimizing
the conservation goals. Each run was repeated 100 times and
MARXAN selected the best of these configurations as the
optimal solution.
RESULTS
Two aspects of MARXAN output were evaluated, total
portfolio area selected and efficiency at meeting target goals,
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
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L. GESELBRACHT ET AL.
Figure 3. Spatial Index of socio-economic factors. The spatial index is a relative scoring of the following socio-economic factors not likely to be
reversible or reversible only at a high to very high cost: population and road density, port facilities, major shipping lanes, hardened shorelines,
Superfund sites, major permitted point source discharges, marine facilities and boat ramps, offshore dredged material disposal sites, and dredged
shipping channels. The darkest areas represent the highest concentration of these socio-economic factors. Table 2 provides the scoring methodology.
to assess the influence of the algorithm parameters that were
varied (conservation targets, their representation and planning
unit cost). The efficiency of meeting target goals for each
scenario was calculated by averaging the percentage of target
goals met in each subregion, then averaged across all
subregions. Table 3 illustrates how this was done for
MARXAN scenario 2. The conservation targets listed in
Table 1 were included in all of the MARXAN runs presented
in this section with the exception of benthic complexity, which
was included in only one of the scenarios to illustrate its
influence on the MARXAN output. Likewise, the socioeconomic index was only included in one scenario, scenario 5,
to illustrate its influence on results. A preferred alternative was
selected from among the scenarios based on analysis of the
MARXAN results and expert review comments, before
conducting a spatial comparison of the preferred alternative
with existing managed areas.
Target goals
Under the universal target goal approach, the total amount of
priority area selected in the optimal solution increased as
target goals were increased. When conservation targets were
universally represented at 20% and 40%, 2.9% versus 5.6% of
the total planning area was selected (Table 4). The portfolio
resulting from using a 20% target goal is for the most part a
subset of the 40% goal portfolio. The only exception to this is
Copyright r 2008 John Wiley & Sons, Ltd.
the southern tip of the Florida peninsula. The 20% target goal
scenario selected all of Biscayne Bay whereas the 40% goal
scenario selected northern and southern areas of Biscayne Bay
and a large area of Florida Bay. The variable target goal
approach delivered an intermediate result of 5.0% of the total
planning area selected. Differences between areas selected in all
three scenarios are modest (Figure 4(a)–(c)). When benthic
complexity is not included as a conservation target, most
target distribution included in this study are nearshore and
coastal. If the planning area was limited to state waters (3
nautical miles off the Atlantic Coast and 9 nm off the Gulf of
Mexico coastline) where target data density is high, the total
amount of planning area selected for the 20%, 40% and
variable target representation scenarios is as follows: 18.6%,
36.3% and 31.6%, respectively.
Regarding efficiency of meeting the target goals, goals were
achieved for all of the conservation targets in all of the
scenarios presented in this study. Efficiency of meeting target
goals, however, differed among the scenarios. Thirty percent
over target goals was selected as an arbitrary limit for
acceptable representation and anything over this 130% value
was considered as excessive over-representation. Under the
variable target goal approach, 51% of the targets exceeded
their goals by 30% or more. Under the universal goal
approaches of 20% and 40%, the portion of targets
exceeding 30% of their goals was 62% and 46%, respectively
(Table 4). Therefore, the 40% universal target goal approach
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
415
IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA
Table 3. Calculation of efficiency at meeting target goals for marxan scenario 2 (40% universal target goal; benthic complexity excluded)
Percentage target goals captured by subregion:
Conservation targets
1
2
3
4
5
6
7
8
Patch coral reef
na
na
250%
250%
102%
102%
102%
127%
113%
114%
116%
250%
225%
112%
160%
213%
113%
100%
179%
138%
156%
na
188%
167%
na
115%
na
167%
na
113%
103%
158%
na
na
106%
na
na
na
125%
200%
188%
150%
109%
132%
250%
na
250%
na
na
na
na
110%
na
54%
na
114%
103%
103%
117%
123%
142%
216%
108%
245%
na
155%
122%
116%
137%
na
na
250%
na
125%
250%
250%
250%
na
167%
na
250%
129%
131%
177%
214%
na
na
129%
na
na
na
250%
250%
na
250%
125%
250%
na
na
na
na
na
na
167%
126%
250%
63%
103%
117%
100%
103%
128%
203%
174%
100%
119%
101%
na
na
131%
125%
120%
na
na
250%
105%
125%
125%
160%
201%
100%
155%
250%
104%
154%
135%
100%
na
250%
na
100%
100%
na
na
109%
na
na
na
167%
na
na
na
na
na
na
120%
237%
na
103%
38%
na
na
na
na
100%
138%
131%
120%
165%
103%
208%
na
na
na
141%
na
208%
200%
125%
208%
125%
na
na
na
125%
250%
167%
262%
na
113%
na
na
na
na
108%
na
na
118%
na
na
na
na
na
na
na
na
na
na
na
na
na
na
55%
na
na
na
na
121%
100%
126%
101%
112%
100%
173%
na
na
101%
188%
na
100%
104%
110%
125%
103%
117%
107%
250%
128%
113%
104%
120%
239%
102%
na
na
na
na
119%
na
na
103%
na
na
na
167%
250%
na
125%
na
na
na
na
na
na
na
21%
na
na
na
na
115%
133%
105%
100%
115%
101%
134%
na
na
104%
250%
na
116%
200%
125%
208%
125%
250%
na
na
167%
114%
167%
na
166%
100%
154%
na
na
na
122%
na
na
111%
na
na
na
na
na
na
250%
na
na
161%
na
na
na
na
48%
na
na
na
na
na
103%
106%
145%
103%
101%
na
na
na
107%
na
na
250%
133%
250%
117%
150%
250%
na
na
150%
145%
na
na
100%
108%
na
na
na
na
123%
116%
na
na
na
250%
na
na
na
na
125%
na
107%
231%
na
na
na
na
45%
Shallow bank coral reef
Deep bank coral reef
Deep reef resources
Mangrove swamp
Beach/Surf zone
Salt marsh
Submerged aquatic vegetation
Coastal tidal river or stream
Tide flat
Bivalve (oyster) reef
Annelid (worm) reef
Hardbottom
Inlets and passes
Florida manatee
Right whale calving grounds
American oystercatcher
Black skimmer
Brown pelican
Least tern
Piping plover
Reddish egret
Roseate spoonbill
Roseate tern
Snowy egret
Snowy plover
Wading bird colony
American crocodile
Green turtle nesting beaches
Loggerhead turtle nesting beaches
Leatherback turtle nesting beaches
Hawksbill turtle nesting beaches
Kemp’s Ridley nesting beaches
Turtles in-water
Ornate diamondback terrapin
Mississippi diamondback terrapin
Carolina diamondback terrapin
Smalltooth sawfish
Slashsheek goby
River goby
Bigmouth sleeper
Mangrove Rivulus
Opossum pipefish
Striped croaker
Gulf sturgeon
Atlantic sturgeon
Saltmarsh topminnow
Alabama shad
Key silverside
Acropora palmata
Johnson’s seagrass
Spawning aggregations
Percentage targets with goals 4130% in subregion:
Percentage targets with goals 4130% statewide:
na
na
239%
100%
104%
148%
128%
100%
157%
159%
na
na
190%
202%
100%
159%
208%
103%
111%
100%
125%
150%
na
125%
na
125%
na
188%
166%
101%
na
129%
na
na
na
242%
125%
na
na
na
na
125%
na
na
250%
na
na
na
na
na
na
46%
46%
The coral reef conservation target was split into the reef types listed above. na 5 not applicable. Target was not present in subregion.
delivered the most efficient results in terms of meeting target
goals among the three approaches examined. The variable
target goal approach was viewed less favourably by the expert
review panel owing to the necessity to employ some
subjectivity in rating criteria for some conservation targets.
For several targets, quantitative information on pre-settlement
extent or population size is limited or unavailable (Geselbracht
et al., 2005).
Copyright r 2008 John Wiley & Sons, Ltd.
Influence of including benthic complexity
The effect of including the surrogate target, benthic complexity
was examined by comparing scenarios that were identical
except for their inclusion/omission of benthic complexity,
using the scenario with a 40% universal target goal. When
benthic complexity was included, results for the offshore
portions of the planning area (here defined as greater than
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
416
L. GESELBRACHT ET AL.
50 m) differed substantially, as anticipated, because the benthic
complexity dataset was the only dataset used in this study that
covered the entire offshore portions of the planning area
(Figures 4(b) and (d)). Many of the offshore areas gained, as a
result of including benthic complexity, coincide with known
deep reef features of high biodiversity value including Pulley
Ridge, Pourtales Terrace, Miami Terrace, Steamboat Lumps and
DeSoto Canyon (Koenig et al., 2000; Scanlon, 2000; Scanlon et
al., 2001; Sheridan and Caldwell, 2002; Gardner et al., 2003;
Jarrett et al., 2004; Reed, 2004). Differences in results between the
nearshore and coastal portions of these two scenarios were
modest with four exceptions. The scenario excluding benthic
complexity (4b) includes a large site in Charlotte Harbor. The
scenario with benthic complexity (4d) includes two large sites
along the Florida Panhandle and one large site in Tampa Bay. In
addition, the scenario with benthic complexity favoured Biscayne
Bay over Florida Bay and split the large Panhandle sites into
several small sites. Closer examination of why including benthic
complexity may have influenced some of the results in this way
revealed that in the shallower portions of the planning area,
benthic complexity highlighted man-made features such as
shipping lanes and other dredged areas.
The scenario that included benthic complexity (4d) selected
more than twice the area of the scenario without benthic
complexity, reflecting greater spatial representation in the
offshore area. In terms of efficiency of meeting conservation
target goals, all goals were met with 54% of targets exceeding the
target goal by 30% or more compared with 46% for the scenario
without benthic complexity (Table 4). Therefore, the scenario
with benthic complexity was less efficient at meeting target goals
than the identical scenario without benthic complexity.
Effect of including socio-economic use index as a cost
surface
The effect of including a spatial index of socio-economic activities
or threats was explored to examine its influence on selection of
priority areas. A comparison of the optimal portfolio of sites,
both with and without the socio-economic index included, shows
many similarities (Figures 4(b) and (e)). A large site off extreme
north-east Florida was selected by both approaches as were sites
in the Mosquito, Banana and Indian River lagoons (subregions 1
and 2). The scenario including the socio-economic index (4e)
differs from the scenario excluding it (4b) in the following ways:
the Florida Panhandle sites are more fragmented, there is a gap in
the selected areas at Crystal River in the southern Big Bend
Subregion, and smaller sites in Florida Bay and the St Lucie
Estuary were selected. In the portfolio with the socio-economic
index included, results appear to avoid more developed or higher
cost areas.
In terms of efficiency, the scenario including the socioeconomic index exhibits improved efficiency both spatially and
at meeting target goals. This scenario selected 4.8% of the total
planning area, all conservation target goals were met and 38%
(versus 46%) of targets exceeded the 40% target goal by 30%
or more (Table 4). While the expert review panel recognized
the value of a socio-economic index for informing
conservation and resource management activities, they
expressed a distinct preference for selecting priority sites
based solely on biodiversity importance rather than giving
preference to sites less subject to human use/disturbance. Their
rational was that important biodiversity sites should not be deCopyright r 2008 John Wiley & Sons, Ltd.
Table 4. Summary of MARXAN output statistics
MARXAN scenario
Percentage of
planning area
selected
1. 20% universal target goal,
benthic complexity
excluded, BLM 5 0.05
2. 40% universal target goal,
benthic complexity
excluded, BLM 5 0.05
3. Variable target goal,
benthic complexity
excluded, BLM 5 0.05
4. 40% universal target goal,
benthic complexity
included, BLM 5 0.05
5. 40% universal target goal,
benthic complexity
excluded, BLM 5 0.05,
socio-economic index
included.
Preferred alternative.
40% universal target goal,
benthic complexity only in
areas 450 m, BLM 5 0.05;
socio-economic index
excluded
Percentage
of targets overrepresented by
430% of goal
2.9%
62%
5.6%
46%
5.0%
51%
13.4%
54%
4.8%
38%
9.7%
48%
emphasized if they were also important for socio-economic
uses. Rather the expert review panel suggested that sites
important for both biodiversity values and socio-economic
uses may warrant more concentrated management attention
rather than less. They recommended using the socio-economic
index to guide resource management and conservation
activities following identification of priority sites.
Identification of a preferred portfolio and comparison with
existing managed areas
Based on the analysis of the MARXAN output and the general
advice of our expert review panel, a preferred portfolio was
selected that represented all targets at the 40% level, included
benthic complexity as a target only in deeper reaches of the
planning area (450 m depth) and did not include the socioeconomic index in site selection. The results of this scenario are
presented in Figure 4(f). The preferred scenario selected 9.7%
of the planning area, more than the scenarios that completely
excluded benthic complexity, but less than the scenario that
included it throughout the planning area. Efficiency at meeting
target goals was intermediate (48%) compared with the other
scenarios presented in this study.
A spatial comparison of the preferred alternative with
existing managed areas was undertaken to evaluate the extent
to which preferred alternative sites fall within some type of
management regime (Figure 5). Approximately 39% of the
preferred alternative falls into some form of existing managed
area. The overlapping managed areas range from seasonal
fishing closure areas to no take marine reserves.
DISCUSSION
The site selection algorithm, MARXAN, greatly simplifies
identification of a spatially efficient portfolio of priority areas
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA
417
Figure 4. Results of site prioritization analyses using MARXAN with several different scenarios. Expert review of the various scenario outputs
resulted in the creation of the preferred scenario, which can be used to guide future investment in conservation and management activates. All runs
used a boundary length modifier of 0.05 and a species penalty factor of 500.
using spatial information on conservation targets and objective
criteria on desired levels of representation. It is an excellent
tool that informs a complex process and enables those
interested in identifying priority sites to observe how changes
in selection criteria influence portfolio design. A good
knowledge of how input parameters influence portfolio
design will greatly enhance the ability of MARXAN users to
satisfactorily integrate the expert advice of scientists, resource
managers and the sometimes competing concerns of other
stakeholders. Identification of priority sites using only expert
input has been described as less defensible and scientifically
credible (Lourie and Vincent, 2004). However, development of
priority sites using biogeographic data and a site selection
algorithm without stakeholder input may be equally
problematic. Employing quantitative information and
decision-support tools such as MARXAN, while soliciting
expert and other stakeholder input throughout the process, is
likely the best approach to priority site identification. Broader
support of the process and results are built concurrently with
development of the priority site portfolio.
Setting target goals can be a difficult exercise due to the
varying opinions on how it should be done and the lack of well
established guidance. Employing universal target goals is an
expedient method. Scenario results can be compared on the
basis of both spatial efficiency and efficiency at meeting the
target goals. Although the effort to set goals individually for
each conservation target was not popular among the expert
Copyright r 2008 John Wiley & Sons, Ltd.
reviewers, refining this approach has merit. Setting all target
goals at the same level does not account for differences in
historical exploitation levels or the shifting of baseline
conditions. Refining an approach that sets representation
goals individually for each target will require advancing our
understanding of historical distribution of conservation
targets. Pauly (1995) supports improving our understanding
of historical distributions as a means for setting more
appropriate conservation target goals.
Inclusion of surrogate conservation targets in MARXAN
analyses may return some unexpected results. In shallower
portions of the planning area, benthic complexity highlighted
areas where shipping channels and dredged sites were
present. These high impact sites are not typically considered
of high biodiversity value, so the benthic complexity target was
excluded from areas with depths less than 50 m in the
preferred alternative. This result in shallow portions of
Florida marine and estuarine areas may also be a function of
Florida’s predominantly low relief environment with the
exception of coral reef areas. Benthic complexity does,
however, appear to be a valuable surrogate target to
include in the deeper portions of the planning area and
may aid in the identification of areas of potential conservation
interest that have not been subject to extensive
scientific investigations. Use of benthic complexity as a
target in site selection may best be confined to deeper
portions of marine planning areas where conservation target
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
418
L. GESELBRACHT ET AL.
Figure 5. Preferred alternative is compared with existing managed areas in the study area. Dark shading indicates permanently designated managed
areas. Light shading indicates areas only subject to temporary or seasonal fishing closures.
distribution data are limited, or to areas where man-made
features are not likely to influence the results.
The purpose of conducting the site prioritization effort will
drive the extent to which socio-economic information is
incorporated. The emphasis of this effort was to identify a
portfolio of sites representing high biodiversity value.
Participants in the expert review sessions were heavily
weighted towards marine scientists, government resource
managers and members of conservation organizations. There
was little representation from user groups. Representation by
user groups under these conditions was not essential as the
process did not result in any actual proposals for new marine
reserve sites, which would have resulted in limiting the access
of some user groups. The use of socio-economic data to
identify sites of high use was much less an issue here than in
other similar reserve design efforts. Subsequent efforts to
identify and implement appropriate conservation strategies for
priority sites will require the use of socio-economic
information and the involvement of user groups, especially if
any restrictions on site use are proposed.
While it was found that approximately 39% of the
preferred alternative falls within the boundaries of existing
managed areas, this should not be construed as 39% successful
conservation of the preferred portfolio. The existing managed
areas overlapping the preferred alternative sites vary greatly in
regulatory purview, staffing, enforcement, etc. Only the notake marine reserves, which represent less than 1% of preferred
Copyright r 2008 John Wiley & Sons, Ltd.
alternative area, provide protection from most of the direct
human use impacts. The majority of the managed areas
overlapping the preferred portfolio provide incomplete
protection, which may include seasonal or permanent
prohibitions on some but not all types of fishing and/or
limited protection from habitat destruction. Establishing an
appropriate managed areas framework from which to launch
effective, long-term conservation strategies for the marine
systems in Florida’s and surrounding waters will likely require
expanding some existing managed areas, creating new
managed areas and strengthening regulatory protection so
that at the very least direct site impacts are minimized.
Identifying priority areas will always be controversial, but
avoiding this kind of analysis risks losing the systems we seek
to protect (Lourie and Vincent, 2004). Marine conservation in
Florida has reached a crossroads. While there is an extensive
system of state, federal, local and private marine managed
areas in state and adjacent marine waters, many of these sites
operate independently of one another and most offer only
limited protection to marine resources. The goals and
strategies of these managed area programmes should be
aligned, quantitatively articulated and their regulatory
authorities strengthened to ensure the long-term health of
marine systems in and around Florida’s waters for the use,
enjoyment and benefit of future generations. More generally,
quantitative explorations of the effectiveness of tools like
MARXAN in specific case studies have represented a gap in
Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009)
DOI: 10.1002/aqc
IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA
our current body of knowledge. The methodologies and results
presented in this paper can be used by resource managers,
conservation professionals and other stakeholders to help
define how marine resource management can be strengthened
and to identify where scarce conservation and management
funds may best be focused in a particular planning area.
ACKNOWLEDGEMENTS
Funding for this project was provided by the US Department
of Interior, State Wildlife Grants Program T-4 Grant
administered by the State of Florida and by The Nature
Conservancy. We are grateful for the contributions of the
many participants and reviewers of this effort which are too
numerous to list here. A portion of the funding for this project
was provided by the US Fish and Wildlife Service State
Wildlife Grants Program T-4 Grant administered by the
Florida Fish and Wildlife Conservation Commission (FWC
Contract No. 04122).
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DOI: 10.1002/aqc
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site, efficiency, estuarine, conservative, area, portfolio, identification, priority, spatially, florida, marina
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