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RESEARCH ARTICLE
Widespread local chronic stressors in
Caribbean coastal habitats
Iliana Chollett
1
*, Rachel Collin
2
, Carolina Bastidas
3,4
, Aldo Cro
´quer
5
, Peter M. H. Gayle
6
,
Eric Jorda
´n-Dahlgren
7
, Karen Koltes
8
, Hazel Oxenford
9
, Alberto Rodriguez-Ramirez
10
,
Ernesto Weil
11
, Jahson Alemu
12
, David Bone
13
, Kenneth C. Buchan
14
, Marcia Creary
Ford
15
, Edgar Escalante-Mancera
7
, Jaime Garzo
´n-Ferreira
16
, Hector M. Guzma
´n
2
,
Bjo
¨rn Kjerfve
17
, Eduardo Klein
5
, Croy McCoy
18,19
, Arthur C. Potts
20
, Francisco Ruı
´z-
Renterı
´a
7
, Struan R. Smith
21
, John Tschirky
22
, Jorge Corte
´s
23
1Smithsonian Marine Station, Smithsonian Institution, Fort Pierce, Florida, United States of America,
2Smithsonian Tropical Research Institute, Smithsonian Institution, Panama City, Panama, 3Departamento
de Biologı
´a de Organismos, Universidad Simo
´n Bolı
´var, Caracas, Venezuela, 4Massachusetts Institute of
Technology, Sea Grant Program, Cambridge, Massachusetts, United States of America,5Departamento de
Estudios Ambientales, Universidad Simo
´n Bolı
´var, Caracas, Venezuela, 6Discovery Bay Marine Laboratory,
Centre for Marine Sciences, University of the West Indies, St. Ann, Jamaica, 7Instituto de Ciencias del Mar y
Limnologı
´a, Universidad Nacional Auto
´noma de Mexico, Puerto Morelos, Mexico, 8Office of Insular Affairs,
US Department of the Interior, Washington DC, United States of America, 9Centre for Resource
Management and Environmental Studies, University of the West Indies, Cave Hill, Barbados, 10 Global
Change Institute, The University of Queensland, Brisbane, Queensland, Australia, 11 University of Puerto
Rico, Mayagu¨ez, Puerto Rico, 12 University of the West Indies, Port of Spain, Trinidad and Tobago,
13 Instituto de Tecnologı
´a y Ciencias Marinas, Universidad Simo
´n Bolı
´var, Caracas, Venezuela,
14 Environment and Economy Directorate, Dorset County Council, Dorchester, Dorset, United Kingdom,
15 Centre for Marine Sciences, University of West Indies, St. Ann, Jamaica, 16 Brewster Academy,
Wolfeboro, New Hampshire, United States of America, 17 American University of Sharjah, Sharja, United
Arab Emirates, 18 Department of Environment, Cayman Islands Government, Georgetown, Grand Cayman,
19 School of Ocean Sciences, Bangor University, Gwyneth, United Kingdom, 20 University of Trinidad and
Tobago, Chaguaramas, Trinidad and Tobago, 21 Bermuda Aquarium Museum and Zoo, Flatt’s, Bermuda,
22 American Bird Conservancy, International Program, Washington DC, United States of America,
23 Centro de Investigacio
´n en Ciencias del Mar y Limnologı
´a, Universidad de Costa Rica, San Jose
´, Costa
Rica
*iliana.chollett@gmail.com
Abstract
Coastal ecosystems and the livelihoods they support are threatened by stressors acting at
global and local scales. Here we used the data produced by the Caribbean Coastal Marine
Productivity program (CARICOMP), the longest, largest monitoring program in the wider
Caribbean, to evidence local-scale (decreases in water quality) and global-scale (increases
in temperature) stressors across the basin. Trend analyses showed that visibility decreased
at 42% of the stations, indicating that local-scale chronic stressors are widespread. On the
other hand, only 18% of the stations showed increases in water temperature that would be
expected from global warming, partially reflecting the limits in detecting trends due to inher-
ent natural variability of temperature data. Decreases in visibility were associated with
increased human density. However, this link can be decoupled by environmental factors,
with conditions that increase the flush of water, dampening the effects of human influence.
Besides documenting environmental stressors throughout the basin, our results can be
used to inform future monitoring programs, if the desire is to identify stations that provide
early warning signals of anthropogenic impacts. All CARICOMP environmental data are
PLOS ONE | https://doi.org/10.1371/journal.pone.0188564 December 20, 2017 1 / 19
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OPEN ACCESS
Citation: Chollett I, Collin R, Bastidas C, Cro
´quer A,
Gayle PMH, Jorda
´n-Dahlgren E, et al. (2017)
Widespread local chronic stressors in Caribbean
coastal habitats. PLoS ONE 12(12): e0188564.
https://doi.org/10.1371/journal.pone.0188564
Editor: Heather M. Patterson, Department of
Agriculture and Water Resources, AUSTRALIA
Received: January 27, 2017
Accepted: November 9, 2017
Published: December 20, 2017
Copyright: This is an open access article, free of all
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: Through the years the CARICOMP
program was supported by the MacArthur
Foundation, the Coral Reef Initiative of the US
Department of State, UNESCO’s Environment and
Development in Coastal Regions and Small Islands
and the US National Science Foundation. Each
participating institution and national agency from
each CARICOMP country have also provided
individual financial and logistical support. IC was
now available, providing an invaluable baseline that can be used to strengthen research,
conservation, and management of coastal ecosystems in the Caribbean basin.
Introduction
Changes at local and global scales are influencing our oceans, altering their health and the ben-
efits we receive from them. Here we use the terms global and local to define scales of action of
anthropogenic stressors, ranging from disturbances acting on broad spatial scales, such as
ocean warming, to those acting at very localized scales, such as dredging [1,2]. These changes
have affected the health of marine ecosystems and the services they provide [3] and may
threaten coastal livelihoods and food security [4]. Long-term measurements of environmental
parameters over wide geographic regions are necessary to understand the rate of change at
global and local scales. Such a strategy provides information that informs identification of
threatened areas and provides potential explanations for and predictions of ecosystem
responses. A long-term approach also allows the assessment of progress towards management
objectives and planning for mitigation or adaptation accordingly [5].
Increases in temperature and decreases in water quality are common indicators of changes
in the oceans at global and local scales, respectively [1,6]. Increases in greenhouse gases
released by human activities have altered ocean temperature, generally by warming [7]. In the
Caribbean, analyses of remote sensing data indicate that most areas have warmed at rates that
range from 0.2 to 0.5˚C dec
-1
during the last three decades [8]. These increases in temperature
have been positively correlated with increases in the frequency and prevalence of coral bleach-
ing and, in some cases, diseases affecting coral reef species across the region [9–11]. The local-
ized influence of human stressors, on the other hand, has been manifested as decreases in
water quality driven by increased pollution resulting from rapid development and habitat con-
version [1]. Decreases in water quality have also been mapped using satellite information, but
only at regional scales, showing increases in turbidity in several localized areas in the Carib-
bean (e.g. [12,13]).
Optical remote sensing has been a pivotal tool in quantifying changes in the oceans at global
and regional scales [14], however, this tool is not well suited to study patterns and processes at
the land-sea interface [15]. While this technology can sample the globe cheaply and repeatedly
over a large area, it can be inaccurate in coastal areas. The inaccuracy of optical remotely-
sensed data close to the coast is related to two main issues: high cloud coverage in coastal areas
that blocks the view from satellites, and the presence of land that contaminates the signal
received by the sensor [15,16]. Additionally, the complex optical signal of coastal waters hin-
ders the quantification of water quality along the coast; the complex mixture of components in
coastal waters makes the quantification of the separate constituents very difficult, and shallow
bottoms can look very similar to heavily turbid regions. As a result, water quality can be mea-
sured using remote sensing only in particular locations using algorithms that are heavily reli-
ant on in situ data [15,16]. Thus in situ measurements from monitoring programs may play an
important role in quantifying patterns in coastal areas.
Long-term in situ datasets documenting temporal changes in the environment of coastal
areas, where most economically valuable ecosystems are located, are limited [17,18]. Most in
situ datasets that record ocean conditions focus on open-ocean areas (e.g. SeaBASS: [19]), and
do not provide repeated measurements that allow for the quantification of changes at fine spa-
tial scales (e.g. the World Ocean Database: [20]). First of its kind in the wider Caribbean, the
Local stressors in the Caribbean
PLOS ONE | https://doi.org/10.1371/journal.pone.0188564 December 20, 2017 2 / 19
supported by the Summit Foundation. JC thanks
the Vicerrectorı
´a de Investigacio
´n, Universidad de
Costa Rica and UNEP for funding the monitoring in
Costa Rica. FRR, EEM and EJD thank the
Universidad Nacional Auto
´noma de Me
´xico for
funding the monitoring in Me
´xico.
Competing interests: The authors have declared
that no competing interests exist.
international Caribbean Coastal Marine Productivity program (CARICOMP) was established
almost 30 years ago to fill this gap [21]. The CARICOMP long-term program was developed to
study processes at the land-sea interface and understand productivity, structure and function
of the three main coastal habitats (mangroves, seagrass meadows, and coral reefs) across the
region [21,22]. Together with biological monitoring, the CARICOMP network has collected
environmental data since 1992 using simple, standardized methods [21–23].
Here we used the environmental data collected by CARICOMP’s monitoring network to
quantify long-term changes in oceanographic conditions in coastal habitats in the wider Carib-
bean. We focused our analyses on temperature and visibility, two proxies of global and local
chronic stressors in marine environments. We had two aims. First, quantifying significant
changes in these environmental variables over time. Second, understanding if these stressors
are influencing the entire basin in a homogeneous way, and if not, what factors (i.e. water
movement, rainfall, and human influence) could explain differences among sites. In this study
we not only synthesize the information in this unparalleled dataset (which is made available
with this publication), but provide guidelines for the better selection of monitoring sites if
future aims include identifying early warning signals of change.
Materials and methods
CARICOMP dataset
Beginning in 1992, CARICOMP established permanent monitoring stations in mangrove,
seagrass, and coral reef habitats. Effort was made to select stations that specifically avoided
anthropogenic sources of disturbance, particularly coastal development and pollution [21].
Weekly (whenever possible) physical measurements were taken at each station between 10:00
and 12:00 local standard time. Measurements consisted of water temperature (˚C), salinity,
and visibility (m). Temperature and salinity were measured with a field thermometer and a
refractometer at 0.5 m depth at all habitats. Visibility was measured with a Secchi disk in sea-
grass (measured horizontally 0.5 m below the surface, as these habitats are often too shallow
for a standard vertical measurement) and reef habitats (typically measured vertically over the
drop-off), and can be assumed to indicate water quality at the surface. Secchi depth is strongly
correlated to the amount of particulate material in the water column and it has been used as a
cheap, fast, and simple proxy for visibility and water quality [24]. We are aware, however, that
this is only one of the multiple environmental variables that characterize water quality at a site,
and that a full assessment of this component would require the measurement of other variables
(e.g. concentration of nutrients, pollutants, dissolved matter).
Data from previously published CARICOMP databases and updates provided directly from
individual researchers at CARICOMP stations were compiled into a uniform format. All envi-
ronmental CARICOMP data are available in the Supporting Information (a description of all
stations is in Tables A and B in S1 File and the data are in S2 File). Although information from
all three variables is included in the appendix, to address the aims of this research only temper-
ature and Secchi data were analyzed.
Simple mixed effect models for the assessment of differences among habitats (fixed factor)
including all stations (as random factor) were fitted with the R package lmerTest [25], which
provides additional F statistics and p-values for factors calculated based on Satterthwaite’s
approximations. Satterthwaite’s method allows for the calculation of the denominator degrees
of freedom as a function of the variance of the parameter estimate [26], therefore estimating
significance in mixed effect models which is generally problematic [27].
Monthly averages were calculated from the weekly data for each station. To ensure mean-
ingful quantification of a linear trend, only stations with data for at least three years and a
Local stressors in the Caribbean
PLOS ONE | https://doi.org/10.1371/journal.pone.0188564 December 20, 2017 3 / 19
minimum of 30 monthly records were included in subsequent analyses (60% of the sites:
Table 1,Fig 1).
Global and local-scale changes across the Caribbean
To assess global and local-scale changes across the Caribbean, we focused our analyses on
changes in temperature and visibility, which as previously noted, are common proxies for
change at each scale. Long-term trends and significance were calculated considering serial cor-
relation, a characteristic of the data that, if not taken into account, violates the assumption of
independence of most regression analyses and influences the magnitude and significance of
trends [27].
Following Weatherhead et al. [27], for temperature (T), we fitted a non-linear model with
the form:
T¼mþStþot
12 þNtð1Þ
Table 1. Description of sites. CARICOMP stations with long-term data (at least three years and 30 monthly records).
Country Site Habitat Station acronym Latitude Longitude Year range
Barbados Bellairs Coral Reef BARr 13.192 -59.642 11/1992-12/1999
Barbados Bellairs Seagrass Beds BARs 13.068 -59.578 11/1992-09/1996
Belize Carrie Bow Cay Coral Reef BELr 16.800 -88.067 01-1993/07-2015
Belize Carrie Bow Cay Seagrass Beds BELs 16.825 -88.099 01-1993/07-2015
Bermuda Hog Breaker Reef Coral Reef BERr 32.344 -64.865 09-1992/12-2002
Bermuda North Seagrass Seagrass Beds BERs 32.401 -64.799 09-1992/12-2002
Bonaire, N.A. Barcadera Reef Coral Reef BONr 12.195 -68.301 08/1994-12/1997
Colombia Chengue Bay Coral Reef COLr 11.328 -74.128 09-1992/06-2011
Colombia Chengue Bay Mangrove COLm 11.317 -74.128 09-1992/06-2011
Colombia Chengue Bay Seagrass Beds COLs 11.321 -74.127 09-1992/06-2011
Costa Rica Rio Perezoso Seagrass Beds CRIs 9.737 -82.807 03-1999/05-2015
Jamaica Discovery Bay Coral Reef JAMr 18.472 -77.414 09-1992/02-2002
Jamaica Discovery Bay Mangrove JAMm 18.469 -77.415 09-1992/02-2002
Jamaica Discovery Bay Seagrass Beds JAMs 18.471 -77.414 09-1992/02-2002
Mexico Puerto Morelos Coral Reef MEXr 20.878 -86.845 10-1992/10-2005
Mexico Puerto Morelos Seagrass Beds MEXs 20.868 -86.867 09-1992/10-2005
Panama STRI_colo Coral Reef PANr 9.349 -82.266 06-1999/05-2015
Panama STRI_colo Mangrove PANm 9.352 -82.259 02-1999/05-2015
Panama STRI_colo Seagrass Beds PANs 9.352 -82.258 06-1999/05-2015
Puerto Rico La Parguera Coral Reef PURr 17.935 -67.049 01-1993/12-2014
Puerto Rico La Parguera Seagrass Beds PURs 17.955 -67.043 01-1993/12-2014
Saba, N.A. Ladder Labyrinth Coral Reef SABr 17.626 -63.260 09-1992/04-1997
USA Long Key Seagrass Beds USAs 24.800 -80.717 07-1996/06-2004
Venezuela P.N. Morrocoy—Caiman Coral Reef VENr1 10.852 -68.232 09-1992/11-1999
Venezuela P.N. Morrocoy—Cayo Sombrero Coral Reef VENr2 10.881 -68.213 02-2000/11-2012
Venezuela P.N. Morrocoy Mangrove VENm 10.836 -68.261 01-1993/11-2012
Venezuela P.N. Morrocoy Seagrass Beds VENs 10.858 -68.291 09-1992/11-2012
Venezuela Punta de Mangle Mangrove VEN2m 10.864 -64.058 01-1993/12-2003
Venezuela Punta de Mangle Seagrass Beds VEN2s 10.864 -64.058 01-1993/12-2003
https://doi.org/10.1371/journal.pone.0188564.t001
Local stressors in the Caribbean
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Fig 1. Changes in temperature and visibility throughout the CARICOMP network. Map of CARICOMP
stations showing significant increases, decreases, or non-significant trends for temperature (A) and visibility
(B). Labels as in Table 1, with upper case letters indicating the location and lower case the habitat.
https://doi.org/10.1371/journal.pone.0188564.g001
Local stressors in the Caribbean
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Where the temperature at time tin months is a function of a constant term μ, a seasonal
component with sinusoidal form S
t
, a linear trend of rate ˚C year
-1
, and residuals N
t
. In this
model, the seasonal component is allowed to include up to two cycles, and is described by the
formula:
St¼X4
j¼1b1;jsin 2pjt
12 þb2;jcos 2pjt
12 ð2Þ
Where tis the number of months, and βare parameters to be estimated. And the residuals
have an AR-1 autocorrelation form, the simplest form of autocorrelation (i.e., the similarity
between a time series and a lagged version of itself). That is, the residuals at time tare a func-
tion of the residuals at time t-1 (i.e. the temporal “memory” of the time series has a one month
lag), depending on the station-specific autocorrelation parameter νF, along with the noise (
t,
[27]):
Nt¼Nt1þtð3Þ
For visibility (V), we fitted a non-linear model that follows the approach described above
but without the seasonal component:
V¼mþot
12 þNtð4Þ
In this model, Vat a given time tin months is a function of a constant term μ, a linear trend
of rate m year
-1
, and residuals, N
t
also assumed to have a AR-1 autocorrelation form (Eq 3)
The models were fitted using generalized squares and the package nlme in R[28]. Initial
estimates for μand were obtained through simple linear regression, and initial values of 1 were
used for all β’s.
Correlates of global and local-scale changes
Global and local-scale stressors can be exacerbated or dampened by local conditions related to
water movement, with circumstances that increase the flush of water potentially less conducive
to warming and decreases in visibility [29,30]. We examined the effects of water movement
through the inclusion of two variables: wave exposure and current speed. Additionally, trends
in visibility can be driven by human influence (with areas of rapid population increases
expected to lose visibility), and could also be influenced by trends in rainfall (with stations that
are getting wetter anticipated to show increased turbidity); therefore these two variables were
also included to explain trends for this response variable. This way, we characterized each sta-
tion with the explanatory variables: (1) average wave exposure; (2) average current speed; (3)
changes in human population density; (4) trend in rainfall. Due to the lack of consistent in situ
datasets for all stations, modelled or remote sensing sources were used to derive explanatory
variables. Below we briefly describe each dataset.
Wind-driven wave exposure for each station is dependent on the wind patterns and the
configuration of the coastline, which defines the fetch, or the length of water over which a
given wind has blown to generate waves. To calculate wave exposure, wind speed and direction
data at each location were acquired from the QuickSCAT (NASA) satellite scatterometer from
1999 to 2008 at 25 km spatial resolution [31]. Coastline data were obtained from the Global
Self-consistent, Hierarchical, High-resolution, Shoreline (GSHHS v 2.2) database which pro-
vides global coastline at 1:250,000 scale [32]. From these datasets wave exposure was calculated
using the methods based on wave theory described in Chollett et al. [33] for 32 fetch directions
Local stressors in the Caribbean
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and the coastline data at full resolution. Average wave exposure at each station was calculated
in Rwith the aid of the packages maptools,raster,rgeos, and sp [34–37].
Average surface current speed was extracted from the ocean model HYCOM [38]. We
used global data-assimilative runs at 1/12˚ of spatial resolution for the period 2008–2011. The
HYCOM model is forced by wind stress, wind speed, heat flux, and precipitation and the sys-
tem uses in situ temperature and salinity profiles to improve estimates, providing the most
detailed and comprehensive global dataset of ocean currents available to date [39].
Gridded human population density data for the years 1990 and 2000 (the most recent data-
set available at that spatial detail) were obtained from the Global Rural-Urban Mapping Proj-
ect, Version 1 (GRUMPv1: [40]). These years coincide with the period when most of the
CARICOMP took place, with the time series beginning on average in February 1994 and fin-
ishing on average in September 2007 (Table 1). We used the adjusted population density grids
as inputs, which provide population density in persons per square kilometre using census
information but also observations of night lights to delineate the extent of urban areas. From
these datasets we extracted the number of people within a buffer of 1-degree diameter around
each station, and then calculated the difference in population between the years 2000 and
1990, which captures a proxy for broad impacts of human population expansion on coastal
ecosystems. A one degree buffer was considered a reasonable range at which many human
impacts might affect coastal ecosystems, as demonstrated in previous studies [41].
Satellite rainfall data were extracted from the GPCP v2.2 combined precipitation dataset,
which merges satellite and gauge precipitation values in monthly estimates of total precipita-
tion from 1986 to 2016 (i.e. 37 years of data) at 2.5˚ spatial resolution. This is the longest, most
accurate global dataset of rainfall available to date [42,43]). For each station, trends were calcu-
lated from these monthly means taking into account the temporal autocorrelation of the data
(Eq 4).
When trends are non-significant their value is uninformative (e.g. a trend in temperature of
2˚C year
-1
with a p value of 0.8 is meaningless), hindering the use of the actual trend values as
a response variable in quantitative analyses. We therefore transformed the continuous data
(i.e. trend values in temperature and visibility) into nominal data (i.e. trend categories) by clas-
sifying trends as non-significant, significantly increasing or significantly decreasing. We then
used multinomial regression models to identify what factors were relevant at explaining the
observed trend categories in temperature and visibility. Multinomial regression is a method
used to generalize logistic regression where the response variable is nominal and has more
than two classes, in which the log odds of the outcomes are modelled as a linear combination
of predictor variables. Here, we modelled trends in temperature as a function of wave exposure
and currents, and trends in visibility as a function of wave exposure, currents, changes in
human population, and trends in rainfall. Multinomial regression was carried out using the
package nnet in R [44]. All figures were produced using the package ggplot2 in R [45].
Results
CARICOMP dataset
CARICOMP collected data at 48 stations in 18 countries/territories across the wider Caribbean
(Tables A and B in S1 File). Participants in the network have sampled environmental data
from 20 reefs, 19 seagrass meadows, and 9 mangrove forests since 1992. Data collection is
ongoing at some stations.
Water temperature and visibility were variable throughout the region (Figs 2and 3). Aver-
age temperature ranged from about 22˚C in Bermuda to almost 30˚C in Cuba, but many sta-
tions showed relatively similar values (Fig 2). There were no clear differences in temperature
Local stressors in the Caribbean
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Fig 2. Sea temperature throughout the CARICOMP network. Sea temperature in each site and habitat in the CARICOMP network, all data are
presented, including all years (i.e. since 1992) and all stations, with and without long-term (>3 years) data: (A) coral reefs; (B) seagrass meadows; and (C)
mangroves. In boxplots, lines represent means, boxes 25 and 75% quantiles, whiskers 1.5 inter-quartile ranges and dots outliers. Sites are: Costa Rica
(CRI), Panama (PAN), western Venezuela (VEN), eastern Venezuela (VEN2), Colombia (COL), Trinidad y Tobago (TAT), Bonaire (BON), northern
Local stressors in the Caribbean
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among seagrass, mangroves, and coral reefs (mixed effect model with location as random
effect, F = 0.74, p = 0.48). Visibility, only measured in reef and seagrass habitats, also showed
large variability among stations, with a minimum of about 3 m at the seagrass meadow off
eastern Venezuela, and a maximum of 37 m at the reef in the Bahamas (Fig 3). Locations with
lower values of visibility also showed the greatest variability. As expected, there were clear dif-
ferences in visibility between habitats, with higher values in coral reefs (mixed effect model
with location as random effect, F = 18.22, p <0.001). Sixty percent of the CARICOMP stations
(described in Table 1) included long-term records and were therefore suitable candidates for
the estimation of long-term trends in subsequent analyses.
Colombia (COL2), Curac¸ao (CUR), Barbados (BAR), Belize (BEL), Puerto Rico (PUR), Saba (SAB), Dominican Republic (DRE), Jamaica (JAM), Mexico
(MEX), Cuba (CUB), the Bahamas (BAH), United States (USA), and Bermuda (BER). Sites with an asterisk were included in subsequent analyses.
https://doi.org/10.1371/journal.pone.0188564.g002
0
20
40
60
Visibility (m)
Coral reefs
0
20
40
60
CRI PAN VEN VEN2 COL TAT BON COL2 CUR BAR BEL PRI SAB DRE JAM MEX CUB BAH USA BER
Location
Visibility (m)
Seagrass meadows
A
B
A
B
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Fig 3. Visibility throughout the CARICOMP network. Visibility in each site and habitat in the CARICOMP network, all data are presented,
including all years (i.e. since 1992) and all stations, with and without long-term (>3 years) data: (A) Coral reefs; and (B) Seagrass meadows.
In boxplots, lines represent means, boxes 25 and 75% quantiles, whiskers 1.5 inter-quartile ranges and dots outliers. Sites are: Costa Rica
(CRI), Panama (PAN), western Venezuela (VEN), eastern Venezuela (VEN2), Colombia (COL), Trinidad y Tobago (TAT), Bonaire (BON),
northern Colombia (COL2), Curac¸ao (CUR), Barbados (BAR), Belize (BEL), Puerto Rico (PUR), Saba (SAB), Dominican Republic (DRE),
Jamaica (JAM), Mexico (MEX), Cuba (CUB), the Bahamas (BAH), United States (USA), and Bermuda (BER). Sites with an asterisk were
included in subsequent analyses.
https://doi.org/10.1371/journal.pone.0188564.g003
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Global and local-scale changes across the Caribbean
Data collected by the CARICOMP network offered evidence of widespread local, but not
global-scale changes across the wider Caribbean using visibility and sea temperature as prox-
ies. While a few stations showed evidence of warming, about half the stations showed evidence
of decreased visibility (Fig 1). The mixed effects models represented the temporal variability in
the oceanographic variables well, capturing both the seasonality (for temperature) and long-
term linear trends (Fig 4, Tables A and B in S3 File).
There was large spatial variability in temperature and visibility trends across the CARI-
COMP network (Fig 1). Of the 28 reef, seagrass, and mangrove stations, 18% (1 mangrove, 2
seagrass meadow, and 2 coral reef stations) showed a significant increasing trend in tempera-
ture, and only one (Bonaire reef) showed a significant decrease (Fig 1A, Table A in S3 File).
On the other hand, of the 24 reef and seagrass stations, 42% (4 seagrass meadows and 6 reefs)
showed a significant decreasing trend in visibility, and two stations (Jamaica seagrass and Ber-
muda reef) showed a positive trend (Fig 1B, Table B in S3 File). Neither warming nor decreases
1995 2000 2005 2010
5101520
Time
Visibility (m)
A
B
trend = 0.51 oC dec-1
trend = 0.10 m dec-1
24 26 28 30
Temperature (oC)
Fig 4. Time series example. Time series for sea temperature (A) and visibility (B) for the reef at Chengue Bay (Colombia), showing
significant increases in temperature and significant decreases in visibility. For temperature, the model fit takes into account both seasonality
(sinusoidal line) and a linear trend (straight line).
https://doi.org/10.1371/journal.pone.0188564.g004
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in visibility were observed to be more common in any of the three habitats monitored (Chi-
squared tests, p >0.05).
Correlates of global and local-scale changes
The presence of negative, positive, or non-significant trends in temperature was not explained
by either of the two local factors assessed (wave exposure and currents, multinomial regres-
sion, p >0.05 for both variables). Trends in visibility were explained by all variables, that is,
changes in human population, wave exposure, current speed, and trend in rainfall (multino-
mial regression, p <0.01). Decreases in visibility were more likely to occur in areas where
human population (and associated coastal development) has increased the most (Fig 5A).
Oceanographic and atmospheric variables have the ability to modulate changes in visibility
(Fig 5B–5D). Long-term decreases in visibility were more likely to occur at stations with slow
water motion, characterized either by low exposure (Fig 5B, top panel) or low current speed
(Fig 5C, top panel). Conversely, long-term increases in visibility were more likely at stations
with high wave exposure and current speed, although these variables had a very small effect in
driving significant long-term increases in visibility (Fig 5B and 5C, bottom panels). Finally,
decreases in visibility were also more likely to occur in areas that were getting wetter, and
increases were more likely in areas that were getting drier (Fig 5D). The functional responses
to these explanatory variables were similar irrespective of habitat (Fig 5).
Discussion
The longest and most spatially comprehensive in situ monitoring effort in the wider Caribbean
provides evidence of widespread local changes within the basin. This is a relatively unexpected
result, given that CARICOMP stations were intended to be established in pristine areas under
minimal local impacts that could serve as a baseline against which to measure degradation
[21]. However, 15 years ago it was already suggested that some stations were being impacted
by human activities [22]. Results presented here support this statement, agree with results of
localized studies in some of these locations (e.g. [46–50]), and indicate that human impacts on
coastal habitats are ongoing and pervasive within the Caribbean basin.
CARICOMP’s time series do not show widespread evidence of long-term warming at
coastal stations in the wider Caribbean. These findings contrast with a global study that
showed prevalent warming along the world’s coasts using 30 years of satellite data [30], and a
regional study which showed significant warming throughout most of the Caribbean basin
using 25 years of satellite temperatures [8]. The lack of signal in the CARICOMP time series
can be attributed to two related issues: the larger variability of in situ temperature data and the
need for longer time series to detect significant trends. Satellites measure temperature at the
‘skin’ of the ocean surface, which is more stable [51], and ignores subsurface temperature pat-
terns that are more variable at multiple temporal scales (from minutes to decadal: [52]). There-
fore, in situ temperature data are more variable making trend estimation more difficult. Low
precision of in situ measurements due to external influences (such as changes in sampling
methodology, observers, instrumentation or gaps in the time series: [27,53]) could also
increase variability and limit the ability to detect trends. Besides the issue of increased variabil-
ity, the inability to detect trends might be related to the length of the CARICOMP time-series
(from 3 to 22 years). This timeframe may provide insufficient statistical power to assess long-
term changes in temperature due to intrinsic characteristics of the location, particularly in sta-
tions where the magnitude of the trend is small, the memory (i.e. temporal autocorrelation) is
high, or temperature is especially variable [27].
Local stressors in the Caribbean
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0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Decrease Increase
0 250000 500000 750000 1000000
Change in human population (2000-1990)
probabiliy
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Decrease Increase
02468
Wave exposure (Ln Jm-3)
probability
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Decrease Increase
0.0 0.1 0.2 0.3 0.4 0.5
Current speed (ms-1)
probability
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Decrease Increase
−1.0 −0.5 0.0 0.5 1.0
Trend in rainfall (mm yr-1)
probability
A
D
C
B
Coral Reef Seagrass Beds
Ecosystem
Fig 5. Explaining trends in visibility. Predicted probability of decreases and increases in visibility (as per
right-hand labels of the top and bottom panels, respectively) against changes in human population (A), wave
exposure (B), current speed (C), and trend in rainfall (D).
https://doi.org/10.1371/journal.pone.0188564.g005
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Site-specific information on the inherent characteristics of the time-series can be used to
aid in the identification of monitoring sites that are cost-effective in the sense that they have
the power to detect trends earlier [27], if the detection of early changes is the main objective of
the monitoring. Significant trends will be detected faster at sites characterized by low variabil-
ity and temporal autocorrelation of the noise, which is a measure of the ‘memory’ or inertia of
the time-series. For example, within the CARICOMP network, the time period to detect an
expected change varies greatly among stations (Fig 6, Table A in S3 File). Within this dataset,
given the variability and memory of the time-series, Puerto Morelos in Mexico would need the
shortest sampling to identify changes in temperature (about ten years), and it might be a good
location to identify trends in temperature early. In contrast the seagrass meadow and man-
grove stations in eastern Venezuela might need the longest time series to detect a significant
trend (Fig 6, Table A in S3 File). This result is not rare; research in atmospheric [27] and
oceanographic [54,55] science has shown that for most expected environmental changes, sev-
eral decades of high-quality data may be needed to detect significant trends. For example,
BONr
BARr
BARs
VENr1
BELr
BELs VENr2
COLr
COLm
COLs
JAMr
JAMm
JAMs
BERr
PURr
PURs
SABr
USAs
BERs
VENm
VENs
MEXr
M
EXs
VEN2m
VEN2s
PANr
PANm
PANs
0.00
0.25
0.50
10 20 30 40
Number of years needed
5
10
15
20
Actual number
of years available
Residuals
0.4
0.5
0.6
Φ
Fig 6. Explaining the lack of trends in temperature. Number of years needed to detect a trend in temperature of 0.05˚C year
-1
as a
function of the autocorrelation of the noise (φ) and the residuals of each station [27]. Also shown in color the actual number of years of data
available for each station. Note that to identify trends of different magnitudes, different number of years might be required.
https://doi.org/10.1371/journal.pone.0188564.g006
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many years of continuous data were needed to distinguish a climate change trend in pH and
sea surface temperature (about 15 years), chlorophyll concentration and primary production
(between 30 and 40 years) from the natural background variability [54,55]. The process of
deciding which sites may be useful for future detection of trends is very similar to conducting
power analysis to estimate the number of samples needed to detect a particular effect. This
type of analysis can be done if the data has already been collected (as in the example in Fig 6)
or before collecting the data, assuming a range of effect sizes, autocorrelation, and noise [27],
and taking into account any external forces that might affect the accuracy of the data (see pre-
vious paragraph; [53]). This information can be used to set realistic expectations on trend
detectability at different sites. It could also be used to identify sites for further monitoring of
chronic impacts [56] and early detection of trends, after accounting for important factors such
as the relevance of the site to answer the s scientific question at hand, or logistic factors such as
accessibility and maintenance of the monitoring site.
Decreases in visibility were related to changes in human density, which increased in all but
one (Bonaire) of the CARICOMP stations assessed. The effects of local anthropogenic impacts
can be modulated, however, by local hydrodynamic and weather conditions. Areas with high
flush of marine water and/or drier weather are less vulnerable to deteriorating visibility. Waves
and currents flush sediments, nutrients, and pollutants and determine the spatial variability in
visibility patterns [29,57]. Decreased rainfall, on the other hand, diminishes runoff reaching
the stations, thus improving visibility [58]. The Caribbean basin is getting drier [59] due to the
intensification of the Caribbean Low Level Jet [60] and warming in the Atlantic [61]. Because
rainfall is predicted to decrease further [60], we expect that rainfall and runoff will play dimin-
ished roles in exacerbating local stressors in the basin in the near future. Knowledge of the fac-
tors that modulate the detection of trends in visibility can also assist in the identification of the
best monitoring sites for early warning signal detection. In this sense, sites with vigorous water
movement should be avoided if the desire is the early detection of water quality degradation in
coastal areas.
Chronic decreases in coastal water quality can be linked to the increase in marine diseases
[62] and the demise of seagrass [63] and coral reef ecosystems [57]. Furthermore, declines in
water quality have been linked to economic losses such as decreases in property value and
tourism revenues (reviewed in [64]). Results presented here pinpoint areas that might require
management interventions. Such interventions may include identifying the cause of decreased
water quality, and implementing changes in management practices and long-term commit-
ments towards change. Improving water quality could also have the added benefit of improv-
ing resilience of coastal ecosystems to other disturbances, such as climate change [65,66].
CARICOMP’s environmental dataset provides an invaluable baseline that can be used to
strengthen research, conservation, and management of coastal ecosystems in the Caribbean
basin. First, the dataset provides context for other local studies, aiding comparisons and under-
standing of observations at single locations [67]. CARICOMP’s environmental measurements
also provide a powerful in situ dataset to help improve satellite observations in coastal areas,
where accuracy is currently limited [15]. In addition, in situ CARICOMP datasets can help
ground truth environmental reconstructions of coastal ecosystems based on geochemical anal-
yses of natural archives (e.g. massive corals). Particularly, calibrations of temperature and
salinity proxies can be achieved using CARICOMP data. Such calibrations and reconstructions
are indispensable to extend time scales prior to monitoring and instrumental records [68–69]
and to infer the magnitude of human-induced impacts within the context of natural variability.
Because CARICOMP sites are located in areas with contrasting environmental regimes (not
only in terms of oceanography but also human influence), the dataset could be used to assess
the impact of these potential controls in key physicochemical variables. For example, the
Local stressors in the Caribbean
PLOS ONE | https://doi.org/10.1371/journal.pone.0188564 December 20, 2017 14 / 19
CARICOMP data may be useful in identifying and assessing indicators of the long-term effects
of marine protected areas (MPA), by comparing sites outside and inside MPAs (e.g. Costa
Rica, Colombia, Venezuela: [46,49,70]). Furthermore, CARICOMP data can be used to assess
the impact of disturbances. For example, the dataset has been used to show a relationship
between high sea surface temperatures and coral bleaching (e.g. [71]). Finally, CARICOMP
environmental data may support models of marine ecosystem dynamics in the Caribbean
region that facilitate science-based decision-making relating to restoration or conservation
management practices.
The CARICOMP program aimed to relate environmental data to observed changes in man-
grove, seagrass meadow and coral reef communities over time [22], and this study serves as a
first step towards that goal. Long-term changes in seagrass biomass and productivity were
reported by van Tussenbroek et al. [72] and the documentation of the changes in mangrove
and reef communities are currently under preparation. Large heterogeneity in environmental
signals reported here could explain, for example, the variability in responses showed by sea-
grass meadows in the region [72], a hypothesis that could be tested now that both datasets are
available. CARICOMP represented the longest, broadest international effort to manually col-
lect data in coastal ecosystems using standard methodologies. By leveraging efforts of a large
group of collaborators from multiple institutions across large spatial scales, CARICOMP’s in
situ monitoring provides an invaluable source to document the spatial distribution of anthro-
pogenic impacts in the coastal Caribbean. Results from this unparalleled effort highlight the
limitations of highly variable coastal in situ data, but also the potential for documenting change
at regional scales.
Supporting information
S1 File. Site metadata. Word file including metadata for all CARICOMP stations included in
the database and mixed effect model fits for temperature and visibility.
(DOCX)
S2 File. CARICOMP environmental database. Text file including all CARICOMP’s weekly
environmental data.
(TXT)
S3 File. Mixed effect models results. Word file including non-linear mixed effect model fits
for temperature and visibility.
(DOCX)
Acknowledgments
Through the years the CARICOMP program was supported by the MacArthur Foundation,
the Coral Reef Initiative of the US Department of State, UNESCO’s Environment and Devel-
opment in Coastal Regions and Small Islands and the US National Science Foundation. Each
participating institution and national agency from each CARICOMP country have also pro-
vided individual financial and logistical support. IC was supported by the Summit Foundation.
JC thanks the Vicerrectorı
´a de Investigacio
´n, Universidad de Costa Rica and UNEP for fund-
ing the monitoring in Costa Rica. FRR, EEM and EJD thank the Universidad Nacional Auto
´n-
oma de Me
´xico for funding the monitoring in Me
´xico. The authors are thankful to all the
researchers, students and volunteers who are too many to name but who have participated
willingly and selflessly in collecting data at the CARICOMP stations. We are grateful to John
Ogden and his staff at FIO (USF) for his leadership and their dedication in support of the
CARICOMP program throughout the years. Thanks to Rosa Rodrı
´guez-Martı
´nez, site
Local stressors in the Caribbean
PLOS ONE | https://doi.org/10.1371/journal.pone.0188564 December 20, 2017 15 / 19
director, for running the station in Puerto Morelos. Monitoring activities in Colombia have
been possible thanks to the support of the Institute for Marine and Coastal Research (INVE-
MAR) and particularly Raul Navas. Thanks to the Bermuda Institute of Oceans Sciences for
access to the data. This is the contribution Number 1076 from the Smithsonian Marine Station
at Fort Pierce, Florida, and 990 of the Smithsonian Institution’s Caribbean Coral Reef Ecosys-
tem program.
Author Contributions
Conceptualization: Iliana Chollett, Kenneth C. Buchan.
Data curation: Iliana Chollett.
Formal analysis: Iliana Chollett.
Funding acquisition: Rachel Collin, Carolina Bastidas, Aldo Cro
´quer, Peter M. H. Gayle, Eric
Jorda
´n-Dahlgren, Karen Koltes, Hazel Oxenford, Alberto Rodriguez-Ramirez, Ernesto
Weil, Jahson Alemu, David Bone, Marcia Creary Ford, Edgar Escalante-Mancera, Jaime
Garzo
´n-Ferreira, Hector M. Guzma
´n, Bjo¨rn Kjerfve, Eduardo Klein, Croy McCoy, Arthur
C. Potts, Francisco Ruı
´z-Renterı
´a, Struan R. Smith, John Tschirky, Jorge Corte
´s.
Investigation: Iliana Chollett.
Methodology: Iliana Chollett, Rachel Collin, Carolina Bastidas, Aldo Cro
´quer, Peter M. H.
Gayle, Eric Jorda
´n-Dahlgren, Karen Koltes, Hazel Oxenford, Alberto Rodriguez-Ramirez,
Ernesto Weil, Jahson Alemu, David Bone, Kenneth C. Buchan, Marcia Creary Ford, Edgar
Escalante-Mancera, Jaime Garzo
´n-Ferreira, Hector M. Guzma
´n, Bjo¨rn Kjerfve, Eduardo
Klein, Croy McCoy, Arthur C. Potts, Francisco Ruı
´z-Renterı
´a, Struan R. Smith, John
Tschirky, Jorge Corte
´s.
Project administration: Jorge Corte
´s.
Writing – original draft: Iliana Chollett.
Writing – review & editing: Iliana Chollett, Rachel Collin, Carolina Bastidas, Aldo Cro
´quer,
Peter M. H. Gayle, Eric Jorda
´n-Dahlgren, Karen Koltes, Hazel Oxenford, Alberto Rodri-
guez-Ramirez, Ernesto Weil, Kenneth C. Buchan, Jorge Corte
´s.
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