ArticlePDF Available

Abstract and Figures

Climate change and land use conversion are global threats to biodiversity. Protected areas and biological corridors have been historically implemented as biodiversity conservation measures and suggested as tools within planning frameworks to respond to climate change. However, few applications to national protected areas systems considering climate change in tropical countries exist. Our goal is to define new priority areas for biodiversity conservation and biological corridors within an existing protected areas network. We aim at preserving samples of all biodiversity under climate change and facilitate species dispersal to reduce the vulnerability of biodiversity. The analysis was based on a three step strategy: i) protect representative samples of various levels of terrestrial biodiversity across protected area systems given future redistributions under climate change, ii) identify and protect areas with reduced climate velocities where populations could persist for relatively longer periods, and iii) ensure species dispersal between conservation areas through climatic connectivity pathways. The study was integrated into a participatory planning approach for biodiversity conservation in Costa Rica. Results showed that there should be an increase of 11 % and 5 % on new conservation areas and biological corridors respectively. Our approach integrates climate change into the design of a network of protected areas for tropical ecosystems and can be applied to other biodiversity rich areas to reduce the vulnerability of biodiversity to global warming.
This content is subject to copyright. Terms and conditions apply.
Mapping conservation priorities and connectivity
pathways under climate change for tropical ecosystems
Emily Fung
1
&Pablo Imbach
1
&Lenin Corrales
1,2
&
Sergio Vilchez
3
&Nelson Zamora
4
&Freddy Argotty
1
&
Lee Hannah
5
&Zayra Ramos
6,7
Received: 23 September 2015 /Accepted: 26 August 2016 /Published online: 28 September 2016
#The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract Climate change and land use conversion are global threats to biodiversity. Protected
areas and biological corridors have been historically implemented as biodiversity conservation
measures and suggested as tools within planning frameworks to respond to climate change.
However, few applications to national protected areas systems considering climate change in
tropical countries exist. Our goal is to define new priority areas for biodiversity conservation
Climatic Change (2017) 141:7792
DOI 10.1007/s10584-016-1789-8
This article is part of a Special Issue on BClimate change impacts on ecosystems, agriculture and smallholder
farmers in Central America^edited by Camila I. Donatti and Lee Hannah.
Electronic supplementary material The online version of this article (doi:10.1007/s10584-016-1789-8)
contains supplementary material, which is available to authorized users.
*Emily Fung
efung@catie.ac.cr
Nelson Zamora
zamoravn@gmail.com
Lee Hannah
l.hannah@conservation.org
Zayra Ramos
zramos@catie.ac.cr
1
Environmental Modelling Laboratory, Climate Change Program, CATIE 7170, Turrialba 30501,
Costa Rica
2
Latin University of Costa Rica, Heredia, Costa Rica
3
Biostatistics Unit, Graduate School, CATIE 7170, Turrialba 30501, Costa Rica
4
Botanical Department, National Biodiversity Institute (INBio), Heredia, Costa Rica
5
Center for Applied Biodiversity Science, Conservation International, Washington, DC 20037, USA
6
Climate Change Program, CATIE 7170, Turrialba 30501, Costa Rica
7
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
and biological corridors within an existing protected areas network. We aim at preserving
samples of all biodiversity under climate change and facilitate species dispersal to reduce the
vulnerability of biodiversity. The analysis was based on a three step strategy: i) protect
representative samples of various levels of terrestrial biodiversity across protected area systems
given future redistributions under climate change, ii) identify and protect areas with reduced
climate velocities where populations could persist for relatively longer periods, and iii) ensure
species dispersal between conservation areas through climatic connectivity pathways. The
study was integrated into a participatory planning approach for biodiversity conservation in
Costa Rica. Results showed that there should be an increase of 11 % and 5 % on new
conservation areas and biological corridors respectively. Our approach integrates climate
change into the design of a network of protected areas for tropical ecosystems and can be
applied to other biodiversity rich areas to reduce the vulnerability of biodiversity to global
warming.
1 Introduction
Biodiversity supports the provision of ecosystem services contributing to human well-being
(MEA 2005). Conserving samples of biodiversity are important for community and ecosys-
tems structure and function and therefore to achieve environmental and development goals.
Climate and land use change have been identified as the main global challenges to biodiversity
conservation (Sala et al. 2000) due to effects on species distribution changes and extinctions,
populations, structure and function of communities and ecosystems (Yang and Rudolf 2010).
Protected areas (PA) and biological corridors (BC) have been proposed as conservation
measures for biodiversity with particular relevance to face the impacts of climate change
(Heller and Zavaleta 2009) accompanied by conservation planning frameworks and tools.
However, few applications to national protected areas systems in tropical countries exist
(Phillips et al. 2008;Gameetal.2011).
With over 50 % of tropical forest already converted into agricultural lands or other uses
(Hansen et al. 2013), the time-window for designing PA that can respond to the threat of
climate change is shortening. The general principle, however, is clear: protection added in
places that conserve species in both their present and future ranges can help meet current and
future conservation targets (Araújo et al. 2004).
PA systems designed based on climate change effects on biodiversity may help respond
to climate stressors. Multiple criteria have been used to identify climate-driven conserva-
tion gaps under the premise that biodiversity viability under future climate change can be
achieved by preserving: i) terrestrial environmental gradients (Game et al. 2011), ii)
climate refugia or holdouts to provide extended time for in-situ adaptation (Hannah
et al. 2014), and iii) connectivity pathways for species dispersal movements (Game
et al. 2011).
Coarse pattern characterization (i.e. ecosystems or biomes) of terrestrial biodiversity as well
as environmental units defined by climatic and topographic variables have been used as
surrogates of all biodiversity levels (communities, populations, species, genes) for conserva-
tion planning (Arponen et al. 2008;Gameetal.2011). Others have proposed the use of land
systems or land facets, defined by a combination of homogeneous topography and soil
variables, which are more stable factors over time (Beier et al. 2015). The assumption is that
by covering a wide range of environmental conditions within a conservation network, the
78 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
range of suitable living conditions is maximized, and therefore a comprehensive representation
of species is protected (Arponen et al. 2008; Beier et al. 2015). Modeling of terrestrial
vegetation redistribution under climate change has been conducted across the globe (Weltzin
et al. 2003) but has been seldom used in conservation planning of protected areas (Hannah
et al. 2007).
Spatial planning of protected areas for climate change may require to identify areas
in which biodiversity responses to climate change may be minor (Hannah et al. 2014).
The term refugia (or microrefugia) has been used referring to locations where species or
populations survived the last glacial period (Ashcroft 2010; Dobrowski 2011). Recently,
the concept has been applied to select areas that should be protected due to reduced
impacts of climate change on biodiversity (Saxon 2008;Rull2009). Tropical studies
have focused on understanding late quaternary climate fluctuations and refugia dynam-
ics of wet tropical forests (VanDerwal et al. 2009), to support conservation of amphib-
ians (Puschendorf et al. 2009), and the role of riparian forests (Aide and Rivera 1998);
although the Pleistocene refugia hypothesis of Haffer (1969) now seems thoroughly
rejected (Carnaval et al. 2009). In contemporary human-induced climate change, con-
tinual warming will make return to a pre-existing climate rare or nonexistent. Refugia
and microrefugia will therefore become vanishingly rare as climate change progresses
(Hannah et al. 2014). Climate change holdouts refer to populations that may persist for
longer but limited periods in localized areas, for example due to changes in climate that
are smaller than regional trends (Dobrowski 2011;Keppeletal.2012)andcanimprove
the chances of successful species dispersal processes under future climate scenarios
(Hannah et al. 2014).
Increasing landscape connectivity is broadly recommended as a climate change adap-
tation strategy for biodiversity conservation (Heller and Zavaleta 2009). The current
design of the BC network in Mesoamerica is aimed at facilitating connectivity between
protected areas (Mesoamericano 2007). However, species needs for dispersal pathways as
a response to change in climate was not taken into account and current corridors have
limited use for species dispersal under climate change (Imbach et al. 2013). Tropical areas
pose a particular challenge for designing corridors given their species richness and limited
knowledge on their dispersal processes (Rouget et al. 2006), although corridors with large
areas that cover altitudinal gradients have been proposed as general design guidelines
(Imbach et al. 2013).
Our goal was to identify and map potential gaps under the existing protected area
system and design connectivity routes to facilitate species range shifts to reduce the
vulnerability of biodiversity to a changing climate. Site selection was based on results
from workshops with experts and modeling outputs. We modeled impacts on biodiversity
based on a three step strategy: i) protect representative samples of various levels of
terrestrial biodiversity (ecosystems, communities, populations and species) across
protected area systems given future redistributions under climate change, ii) identify and
protect areas with reduced climate velocities where populations could persist for relatively
longer periods (referred as climate holdouts), and iii) ensure species dispersal between
conservation areas through climatic connectivity pathways. Experts input was used to
validate methods and to identify opportunities and constraints for field implementation of
new conservation areas. The methods used here represents a participatory planning
approach for biodiversity conservation under climate change in tropical areas based on
CostaRicaasacasestudy.
Climatic Change (2017) 141:7792 79
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
2 Materials and methods
2.1 Study region
Costa Rica is located in the Mesoamerican region near the northern limit of the Neotropical
realm in a zone that begins the transition to the Nearctic realm of North America. Estimations
show than in only 0.0001 % of the earths surface, the country holds around 4 % (ca. 500,000)
of all worlds species (including all taxa) (ca. 14,000,000) (SINAC 2007). Costa Rica is part of
the Mesoamerican biodiversity hotspot (Myers et al. 2000) and part of the Mesoamerican
Biological Corridor, a network of PA and biological connectivity aimed at conserving large
mammals and biodiversity along the length of Central America (DeClerck et al. 2010).
In response to rapid land use change during the 19701990s (3.7 % deforestation rate
average for the period, Sanchez-Azofeifa et al. (2003)), the country started to consolidate the
National System of Conservation Areas (SINAC) (SINAC 2007), increasing the extent of PA
and BC from 3 to 26.5 % of the country area (Estado de la Nación 2013). However, a few
terrestrial environmental gradients (mountaintops of dry forests in Guanacaste and Osa
peninsula, tropical montane vegetation and middle lowlands of the Nicoya Peninsula) are still
not completely protected (Arias et al. 2008).
The first gap analysis of the PA system in Costa Rica aimed at ensuring that at least 90 % of
its biodiversity was under protection (García 1996). However, this assessment and the second
to come in 2007, did not account for future climate change scenarios (SINAC 2007).
The first gap analysis used macro-types as the biodiversity surrogate, defined as landscape
areas that share uniform physiognomic appearance and flora but did not consider vegetation
associations (examples of macro-types classes include paramo, oak forests, evergreen/
deciduous forests). The classification was based on forest types, dominant species, soil type,
elevation and geomorphology (1:250,000 scale, Gómez (1986)). Results suggested the need to
extend the national conservation areas system to protect nine under-represented macro-types
(García 1996). In 2007, the country repeated the gap analysis using an improved bio-
environmental gradient map describing phytogeographic units (PU) as a surrogate for biodi-
versity. The PU map consisted of 31 categories based on information from the earlier
vegetation macro-types and floristic regions (Zamora 2008). The floristic regions refer to
including floristic associations, based on dominant and indicator species (1:200,000 scale,
Hammel et al. (2003)).
The 2007 gap analysis established to protect 10 to 30 % (conservation targets) of each PU
unit within the current PA system. Results from reserve network selection and expert knowl-
edge, proposed to create 92 new conservation areas with approximately 712,000 ha and 128
new biological corridors between protected areas. The PU representativeness targets and
current conservation network (PA and BC) (SINAC 2009) are used as the basis for the analysis
presented here.
2.2 General approach
To design the PA and BC system for the long-term persistence of biodiversity under a
changing climate, we integrated a spatial component addressing the impacts of climate change
on biodiversity with considerations for field implementation of the proposed conservation
areas. We used modeling tools and climate change scenarios to assess impacts, and participa-
tory methods with experts for site selection. We addressed impacts on biodiversity by
80 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
modeling: i) PU redistribution under future climate scenarios to quantify the ability of the
current network of PA in protecting PUs under climate change, and ii) climate holdouts,
mapped as sites with expected small changes in climate, relatively to its surrounding,assuming
that their populations could then persist under climate change. We also mapped climatic
connectivity routes for species dispersal between conservation areas assuming that dispersal
pathways would follow climates relatively closer to their current ones. Modeling results were
combined with information from experts, gathered during workshops, regarding threats and
opportunities for field implementation of new conservation areas (Supplementary Material 1).
2.2.1 Climate change scenarios
We used climate data to model the current and future (under climate change scenarios)
potential PU distributions, climate connectivity and holdout areas. Historical climate data
was obtained from the WorldClim database at ~1km
2
(30 arc-seconds) spatial resolution for
the 19502000 period and used to model the current PU distribution using a statistical
approach. To model future PU distribution, we used future climate scenarios from 19
General Circulation Models (GCMs) under the representative concentration pathway 4.5
(RCP 4.5, corresponding to an intermediate level of global radiative forcing) from CMIP5
(Coupled Model InterComparison Project 5, (IPCC 2013)) for the 20412060 period (Hijmans
et al. 2005). To estimate both range distributions (current and future), 19 bioclimatic variables
were derived from temperature and precipitation maps for each GCM run (Hijmans et al.
2005). Downscaling of climate scenarios was estimated by aggregating future anomalies, from
GCM runs, to a high-resolution historical climatology (available at WorldClim.org).
2.2.2 Terrestrial vegetation modeling
We used the national PU map (1:500,000 scale) as a surrogate for biodiversity, defined as
geographic areas sharing particular floristic vegetation patterns characterized by climate
(temperature, precipitation and its seasonality), topography (relief), elevation and geol-
ogy (Zamora 2008). The floristic vegetation pattern was defined as a type of vegetation
were a group of indicator species coexist; given their abundancy, rarity or restricted
distribution. Identification of indicator species was based on Bkeystone species^: organ-
isms controlling potential dominants, resource providers, mutualists and ecosystem
engineers (Payton et al. 2002) based on available occurrence data. To develop the PU
map, a multidisciplinary expert group (comprising biogeography, botany, climate, con-
servation, statistics, ecology, geology, geography, information technology and modeling
scientists) worked on manually delineating each PU using indicator species, climate
maps, contour lines and/or geographical features, such as rivers or basins. The resulting
PU map was used during the 2007 (SINAC 2007) gap analysis and the one presented
here. The PU map provides information suitable for a biodiversity coarse-filter assess-
ment (Powell et al. 2000) that simplified experts and users familiarization, given its use
in previous assessments.
We used a Random Forest algorithm (Liaw and Wiener 2002) to generate classification
rules of the current distribution of the PU (Zamora 2008) using a combination of biophysical
variables as predictors: i) five principal components (PC) capturing 90 % of the variability
from the 19 bioclimatic variables and evapotranspiration (Supplementary Material 2, Table 1)
(Hijmans et al. 2005), ii) relief quantified as the topographic position index (Jenness 2006)
Climatic Change (2017) 141:7792 81
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
from a digital elevation model (1 km
2
pixel, Jarvis et al. (2008)) that classifies the landscape
into slope position and landforms (plain, undulating, mountain and valleys and depressions)
and iii) surface lithology describing age and origin of the rocks based on the national
geological map (1:500,000 scale, USGS (1987)). Bioclimatic variables describe regional
precipitation and temperature climatology at the annual, seasonal and monthly scales based
on monthly mean values. Real evapotranspiration was based on Imbach et al. (2012). We used
70/30 % of the pixels to train/validate the model that showed a misclassification rate of 11 %.
The most important variables in the classification according to the Mean Decrease Accuracy
were PC1, PC2, PC3 and surface lithology (Supplementary Material 2,Figure1).Arandom
forest model was used to predict the PU distribution under future climatic scenarios using the
PC maps derived for each one of the 19 GCMs (Supplementary Material 2,Table1).We
considered future potential ecosystems presence on any given pixel, when >66 % of the future
climate models (>13 out of 19 GCMs) showed agreement indicating a likely presence
(according to IPCC 2013 guidelines).
Conservation targets were defined as a sub-set of species, communities or ecological
systems that represent biodiversity, established for each PU during the 2007 gap analysis
by experts criteria, based on the area of each PU. Conservation targets were set of
>10,000 ha or between 10 and 30 % of the total area of each PU to be conserved to
maintain a representative sample (SINAC 2007). We quantified the likely future area of
each PU within protected areas. For those PU that did not meet conservation targets within
PA, additional sites were selected with the Marxan planning support tool. Marxan is
designed to solve problems when multiple complex solutions exist, by selecting sites that
meet a specific conservation target with the most cost-efficient solution for field imple-
mentation (i.e. lowest cost) (Watts et al. 2009). A 250 ha hexagon was assumed to provide
a minimum area useful for spatial planning and 21,180 planning units covered the country.
In order to account for future uncertainties on PU distribution from climate scenarios, we
estimated the contribution of each hexagon to achieve a PU conservation target as the
product of each PU mean pixel frequency across the 19 scenarios by the total hexagon
area. Therefore, our setup optimized the selection of additional conservation sites that
maximized higher likelihood of PU presence under future climate scenarios. We assumed
that conservation costs of a hexagon depended on its dominant land use (SINAC and
FONAFIFO 2014), with forests having the lower cost for implementation (0) of conser-
vation activities and non-forest areas (agriculture and urban areas) with maximum costs
(100). Pastures, forest plantations and secondary forests were assigned costs of 60, 40 and
20 respectively. We calculated solutions with different clumping levels of planning units
(Balletal.2009). PU without any significant likely future distribution (<10 km
2
), were not
included in the analysis and assumed to disappear.
2.2.3 Climate holdouts: velocity of climate change
We mapped velocity of climate change as a proxy for climate holdouts to identify areas
where populations may persist for longer periods. Velocity of climate change is
calculated as the distance (km) per year that a species would need to travel to maintain
its original climate conditions (Loarie et al. 2009; Ackerly et al. 2010; Dobrowski et al.
2013). We estimated the velocity of climate change based on future changes in mean
annual temperature and total annual precipitation following Loarie et al. (2009)and
Dobrowskietal.(2013). We calculated velocity as the ratio of temporal (year/km) and
82 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
spatialgradient(
°
C/km or mm/km) of changes in climate, as a proxy for the potential
speed at which species would need to disperse in the future. We mapped areas with
minor velocities (arbitrarily selected as <0.01 °C/km) assuming that species would need
to move shorter distances to maintain its current climate and be able to persist for a
relatively longer time. The resulting map was used to select conservation areas by
experts during the participatory workshops.
2.2.4 Climatic connectivity pathways: direction of climate change
Climate change direction was used to identify possible spatial trajectories for species
dispersal pursuing their suitable climate niche. We used the direction of future change in
temperature (year/km), following Burrows et al. (2014) formula, to map the direction of
potential trajectories. Burrows et al. 2014 combined trajectories to map corridor, sink,
source, convergence and divergence areas. Nevertheless, we used a simplified approach
based on each pixel direction of change. We assumed that all species have dispersal
capacities to keep pace with climate change without accounting for species dispersal
capacity nor potential barriers other than climatic. We assumed that continuous areas with
similar direction represented connectivity pathways if the direction followed the shifts in
the PU future redistribution, or a barrier otherwise. Clusters of vectors pointing at each
other were also assumed as barriers. We assumed that the first situation (i.e. opposite
direction) represented a larger barrier than the second (i.e. pointing to each other).
Direction of changes in precipitation was not considered given its inconsistent direction
pattern possibly resulting from uncertainty in future precipitation scenarios over this
region (IPCC 2013).
2.2.5 Conservation site and connectivity network selection: experts inputs
Selection of additional conservation sites and BC resulted from combining results of
experts input and modeling outputs during workshops. We systematized leading biolo-
gists and conservation planners opinion through participatory workshops. The first
workshop focused on validating the methodologies. We started with a description on
climate variability, trends and future climate scenarios as background to the analysis,
followed by an open discussion based on guided questions (Supplementary Material 3)
related to the methods, data availability and limitations. The second workshop aimed at
communicating modeling results and identification of new gaps and connectivity routes.
Working groups, comprising experts across regions of the country, were provided with
base maps (of PA, BC, current conservation gaps and forest cover) and model outputs
(PU future distribution, Marxan selected areas and climate holdouts). The climate change
direction map was also used to design connectivity networks (Section 2.2.3). Maps were
provided in transparent format allowing overlays to facilitate discussion. Experts pro-
vided criteria for site selection related to land use change threats (i.e., urban or cash crop
developments, local community conflicts, other barriers for species -i.e., hydro-power
reservoirs, stream contaminants) and opportunities for field implementation of new
conservation areas (i.e., resources for implementation and community involvement).
Site selection resulted from experts consensus for each group while a rapporteur
systematized implementation issues for each site discussed. The third workshop was
oriented towards a broader audience (including previous workshops participants) to
Climatic Change (2017) 141:7792 83
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
validate the final portfolio of new conservation areas and BC. Workshops attendance
ranged between 15 and 31 experts.
3Results
3.1 Terrestrial vegetation modeling
Future PUs distribution indicated that most will experience decrease in their likely distribution
range (Fig. 1). The northern dry region, the humid southeast Caribbean slope and Talamancas
mountainside towards the Pacific lowlands did not present likely future distributions of any PU
(grey areas of the map, Fig. 1). PU across the central mountain range (brown), Talamancas
Caribbean mountain range (green) and Pacific southernmost areas (light blue and orange)
showed the greatest persistence areas under future scenarios (Fig. 1). PU from dryer north-
western areas (purple PU) experienced an increase in their future distribution range indicating
an expansion into currently more humid areas. North Caribbean lowland plains (dark blue),
Central Pacific lower slopes (red) and Talamancas Caribbean mountain range (green) undergo
a reduction of their current distribution combined with newly colonized areas (Fig. 1).
Fig. 1 Potential redistribution of phytogeographic units (PU) under future climate scenarios. Black lines
delineate current PU distribution areas. Within the limits of each PU, light and dark color tones indicate the
future loss and persistence areas respectively. Dark tones outside current PU boundaries indicate colonization
areas
84 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
3.2 Climate holdouts: velocity of climate change
The mean velocity for mean temperature and total precipitation for 20412060 across the
country was 0.17 C°/km and 0.88 mm/km respectively. The highest velocity rates were
observed in regions with low topographic relief, primarily found in the north and northeastern
areas (Guanacaste and Alajuela provinces) for precipitation, in addition to the northwestern
region (Caribbean floodplains in Tortuguero) for temperature. The lowest velocity rates were
found in mountainous regions of the country. These potential holdouts were found in the
central volcanic mountain range, central south mountains (La Amistad National Park) and
mountains near the Pacific coast (Fig. 2).
3.3 Climatic connectivity pathways
Directional patterns appeared as i) continuous areas following the same direction demonstrat-
ing clear pathways for species dispersal and ii) undistinguishable pathways due to vectors
pointing at each other. The countrys complex topography resulted in pathways usually having
a few arrows with opposite direction. Figure 3shows examples of regional directional analysis
from two sites in Costa Rica. Arrows indicate the direction of change in climate. Figure 3a-i
shows the potential location of corridors (routes following the blue arrows) in areas with
arrows pointing in the same direction as the range shift of a PU. Figure 3a-ii indicates a climate
Fig. 2 Climate holdouts (red), estimated as the lowest temperature (°C/km) and precipitation (mm/km) velocities
Climatic Change (2017) 141:7792 85
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
barrier for species dispersal, as future re-distribution of the PU follows the opposite direction
of climate change. Figure 3a-iii shows arrows in all directions exemplifying no clear pathways.
Connectivity pathways were also identified to facilitate the contraction of PU. Figure 3b-iv
shows the location of corridors that could favor PU range contractions (towards PU persistence
over dark red pixels) and facilitate species dispersal towards persistence areas. We selected
pathways benefiting the connectivity between biological corridors, protected areas and current
conservation gaps (not shown in the figure).
3.4 Conservation targets and gap analysis
Future biodiversity distribution showed that: (i) 7 PUs had likely future areas in current
protected areas that meet conservation targets, (ii) 10 PUs future distribution did not meet
conservation targets, (iii) 4 PUs did not have any likely distribution area in the future and (iii)
10 PUs disappear under future climates. For the 10 PUs not achieving their conservation
targets, we proposed 11 new conservation areas to maximize their representativeness under
future scenarios. These PUs are located in the north and northwestern lowlands of the country
(including one PU in the Pacific coast) and on the southern foothills of the Central Mountain
Range.
Our results show an increase of 151,000 ha (11 % of the current extent) of new conservation
areas and 237, 000 ha (5 % area increase from its current extent) in 15 new BC for Costa Rica,
in order to increase biodiversity representativeness under climate change (Supplementary
Material 4).
4 Discussion
Future expansions and contractions of PUs over the dry northwestern, Caribbean and
central southern areas agree with other studies indicating redistribution of potential life
zones (Bertrand et al. 2011; Khatun et al. 2013) and increased coverage of drier PU types
(Imbach et al. 2012). These redistributions will result from migration of species or
communities to track ideal climate conditions, with a range shift that generates coloniza-
tion and contraction areas; persistence over current distribution through adaptation or
extinction (Breshears et al. 2008). Resulting shifts in individual species responses will
ultimately define the future composition of the PUs, requiring studies on species range
redistributions, population and community dynamics.
Fig. 3 Direction of annual temperature change under future climate scenarios. Purple,blue,yellow and green
arrows indicate northeast, southeast, southwest and northwest directions of change respectively. Light and dark
red areas indicate the current and future (persistence and colonization areas) distribution of a phytogeographic
unit (PU). Black arrows indicate areas for proposed new climatic corridors and black circles show barriers. aThe
PU will likely experience a range reduction (dark red within its current distribution) and colonization areas (dark
red pixels to the right outside its current distribution): i) blue arrows specify the climate direction that species
might need to follow to colonize newly suitable areas, ii) yellow arrows exemplifies a climatic barrier for species
dispersal with climate change in opposite direction as the course that follows the future distribution of the PU
and, iii) all colored arrows cluster showing a barrier for dispersal. bthe PU suffers a range reduction to smaller
persistence areas within its current range (dark red) that require climate pathways to benefit species movement
towards the persistence core (iv). The country topography is highly complex resulting in pathways usually having
afewarrows contradicting the general landscape direction, therefore, we had to neglect single arrows barriers
86 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The Caribbean lowlands PUs (dark blue in Fig. 1), suggests new colonization areas over
flatlands (towards the south) while the center south and pacific PUs (Green and red PU in
Fig. 1) shift over mountain areas. Range shifts are expected to occur through pioneer species
Climatic Change (2017) 141:7792 87
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
that disperse long distances and colonize by exponential population growth in less favorable
climate conditions (Hampe and Petit 2005). Areas of the country without likely distribution of
any PU (grey areas in Fig. 1), mostly resulting from GCM uncertainty or arrangement of novel
climates, could lead to novel species assemblages (Bertrand et al. 2011).
Persistence areas were found in the central region of the country (brown and green PU,
Fig. 1). The literature suggest that these PUs will retain species covering the inner part of the
range distribution, as the margins suffer greater climate stress and experience a contraction
(Hannah et al. 2014).
Mean annual temperature velocity estimates found in this study (0.17 km/yr) are lower than
those presented by Imbach et al. (2013) (0.25 km/yr), Loarie et al. (0.33 km/yr) and Burrows
et al. (2014)(0.15 km/yr) for the Central American region, probably due to different emission
scenarios, future time frames and finer spatial resolution of the approach. Dobrowski et al.
(2013) calculated climate velocities at five different resolutions (between 30 arc-seconds and 1
degree) and found that at coarser spatial resolution climate velocity increased due to an
underestimation on the terrain capacity to buffer changes in climate. We found no reports on
precipitation velocities for this region. However, there is a general agreement pattern of higher
temperature velocities in flatter areas and lower estimates over mountainous regions. Relatively
small temperature changes over flat areas will require a species to disperse farther distances to
track its ideal climate under changing conditions, while in mountainous regions, a short
movement upslope or downslope will result in larger compensations, allowing the species to
rapidly keep pace with changes in climate (Loarie et al. 2009;Imbachetal.2013). Precipitation
holdouts were also found in mountainous areas (over high slope areas). However, the relatively
small anomalies of mean precipitation found might result from GCM positive and negative
signals canceling each other, given the uncertainty on trends of future precipitation (Imbach
et al. 2012). Its usefulness as a proxy for holdouts should be further explored.
The proposed BC should facilitate contractions or expansion of PU by allowing species to
disperse to a suitable climate and rescue small populations from local extinction (due to
demographic or environmental stochasticity) (Bull et al. 2007). Identification of holdout areas
to support the design of BC provides for places where species may persist for longer periods
and support species flow between the expansion and contraction areas. (Hannah et al. 2014).
Temperature directions are dominated by the altitudinal gradient, since altitude is the main
temperature controller over the region (Ackerly et al. 2010). Direction of precipitation showed
no clear patterns, with contradicting directions at very small scales, limiting its use to define
connectivity pathways.
Our conservation targets (10 to 30 % of the PU area) are similar to those used for other
studies (Langhammer et al. 2007), however, it would be valuable to explore the effect of
different values or inclusion species level conservation targets. Furthermore, the methodology
could benefit from improved representation of local species ecological characteristics includ-
ing dispersal capacities and effects of landscape barriers to identify climate pathways or their
sensitivity to changes in climate for improved holdouts mapping.
Experts and conservation managers were involved during all stages of the process to
complement the systematic planning approach (Cowling et al. 2003). Their feedback, contrib-
uted to the selection of new biodiversity conservation sites and validation of the final portfolio.
We found that expert knowledge provided for updated local level information hardly available
on maps at national scale. Information referred to opportunities and barriers for implementation,
socioeconomic aspects of the target area or micro areas of high endemism. Furthermore, their
involvement potentially enhanced users legitimacy of the conservation portfolio.
88 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Our results define new areas that could require landscape management for biodiversity
conservation depending on their current vegetation cover and connectivity needs. These
management activities can range from conservation or restoration of forests, agroforestry
systems and live fences to removing barriers for dispersal, among others (Heller and
Zavaleta 2009). Monitoring programs could prove useful to detect changes in areas of
PU expansions and contractions in response to climate fluctuations, therefore strength-
ening our understanding on biodiversity response rates (Plattner 2009) and affected
components (biomes, communities, populations, species and genes) (Bellard et al.
2012). Finally monitoring results could support future research and updates on the
conservation portfolio.
5 Conclusions
We presented a multi-criteria approach for redesigning a network of conservation areas for
enhancing long-term biodiversity viability under changing climate. The approach was based on
integrating information on biodiversity distribution patterns, climate holdouts and connectivity
pathways under future climate scenarios. A participatory planning process also accounted for
expert knowledge on local context, potentially facilitating future implementation and improving
legitimacy to the process. Results indicate the need for conservation activities on sites currently
outside protected areas and for improved landscape connectivity across selected landscapes.
However, assumptions regarding the use of phytogeographic units as surrogates for impacts on
biodiversity or disregarding species dispersal capacity could have important implications for our
findings. Future research and monitoring of changes in species, populations, communities
and ecosystems should help fill these gaps. Finally, the resulting portfolio can support
biodiversity conservation policies and the development of national adaptation strategies
coherent with cross-country conservation efforts (Hannah et al. 2002). This multi-criteria
approach, can be used in other regions to identify areas to ensure biodiversity conser-
vation in face of climate change.
Acknowledgments This work was done to support BCosta Ricas adaptation of the biodiversity sector to
climate change^project CR-T1081 ATN/OC-13260-CR coordinated by SINAC and partially financed by the
Inter-American Development Bank (IDB). We acknowledge SINAC for their support during the process and all
experts who provided their valuable input during workshops. We thank The Betty and Gordon Moore Center for
Science at Conservation International for providing funds for open access. We thank Peter Läderach for his
review of early versions of this manuscript.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
References
Ackerly DD, Loarie SR, Cornwell WK, et al. (2010) The geography of climate change: implications for
conservation biogeography. Divers Distrib 16:476487. doi:10.1111/j.1472-4642.2010.00654.x
Aide TM, Rivera E (1998) Geographic patterns of genetic diversity in Poulsena armata (Moraceae): implications
for teh theory of Pleistocene refugia and the importance of riparian forest. J Biogeogr 25:695705
Climatic Change (2017) 141:7792 89
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Araújo MB, Cabeza M, Thuiller W, et al. (2004) Would climate change drive species out of reserves? An
assessment of existing reserve-selection methods. Glob Chang Biol 10:16181626. doi:10.1111/j.1365-
2486.2004.00828.x
Arias E, Chacón O, Induni G, et al. (2008) Identificación de vacíos en la representatividad de ecosistemas
terrestres. Recur Nat y Ambient 54:2127
Arponen A, Moilanen A, Ferrier S (2008) A successful community-level strategy for conservation prioritization.
J Appl Ecol 45:14361445. doi:10.1111/j.1365-2664.2008.0 1513.x
Ashcroft MB (2010) Identifying refugia from climate change. J Biogeogr 37:14071413. doi:10.1111/j.1365-
2699.2010.02300.x
Ball I, Possingham H, Watts M (2009) Marxan and relatives: software for spatial conservation prioritization. In:
Spatial conservation prioritization: quatitative methods & computational tools. Oxford University Press, Oxford
Beier P, Hunter ML, Anderson M (2015) Special section: conserving natures stage. Conserv Biol 29:613617.
doi:10.1111/cobi.12511
Bellard C, Bertelsmeier C, Leadley P, et al. (2012) Impacts of climate change on the future of biodiversity. Ecol
Lett 15:365377. doi:10.1111/j.1461-0248.2011.01736.x
Bertrand R, Lenoir J, Piedallu C, et al. (2011) Changes in plant community composition lag behind climate
warming in lowland forests. Nature 479:517520. doi:10.1038/nature10548
Breshears DD, Huxman TE, AdamsHD, et al. (2008) Vegetation synchronously leans upslope as climate warms.
Proc Natl Acad Sci U S A 105:1159111592. doi:10.1073/pnas.0806579105
Bull JC, Pickup NJ, Pickett B, et al. (2007) Metapopulation extinction risk is increased by environmental
stochasticity and assemblage complexity. Proc Biol Sci 274:8796. doi:10.1098/rspb.2006.3691
Burrows MT, Schoeman DS, Richardson AJ, et al. (2014) Geographical limits to species-range shifts are
suggested by climate velocity. Nature 507:492506. doi:10.1038/nature12976
Carnaval ACOQ, Hickerson MJ, Haddad CFB, et al. (2009) Stability predicts genetic diversity in the Brazilian
Atlantic forest hotspot. Science 323:785789. doi:10.1126/science.1166955
Cowling RM, Pressey RL, Sims-Castley R, et al. (2003) The expert or the algorithm?- comparison of priority
conservation areas in the Cape Floristic region identified by park managers and reserve selection software.
Biol Conserv 112:147167. doi:10.1016/S0006-3207(02)00397-X
de la Nación E (2013) Vigésimo Informe Estado de la Nación en Desarrollo Humano Sostenible. San José, CR
DeClerck FAJ, Chazdon R, Holl KD, et al. (2010) Biodiversity conservation in human-modified landscapes of
Mesoamerica: past, present and future. Biol Conserv 143:23012313. doi:10.1016/j.biocon.2010.03.026
Dobrowski SZ (2011) A climatic basis for microrefugia: the influence of terrain on climate. Glob Chang Biol 17:
10221035. doi:10.1111/j.1365-2486.2010.02263.x
Dobrowski SZ, Abatzoglou J, Swanson AK, et al. (2013) The climate velocity of the contiguous United States
during the twentieth century. Glob Chang Biol 19:241251. doi:10.1111/gcb.12026
Game ET, Lipsett-Moore G, Saxon E, et al. (2011) Incorporating climate change adaptation into national
conservation assessments. Glob Chang Biol 17:31503160. doi:10.1111/j.1365-2486.2011.02457.x
García R (1996) Propuesta técnica de ordenamiento territorial con fines de conservación de biodiversidad. Costa
Rica. Costa Rica, Informe de País
Gómez L (1986) Mapa de lo macrotipos de vegetación de Costa Rica. Serie de 10 mapas. Escala 1:250000.
EUNED, San José, CR
Haffer J (1969) Speciation in Amazonian forest birds. Science 165:131137. doi:10.1126/science.165.3889.131
Hammel BE, Grayum MH, Herrera C, Zamora N (2003) Manual of plants of Costa Rica, Volume II:
gymnosperms and monocotyledons (Agavaceae-Musaceae). Missouri Botanical Garden Press, St. Louis
Hampe A, Petit RJ (2005) Conserving biodiversityunder climate change: the rear edge matters. Ecol Lett 8:461
467. doi:10.1111/j.1461-0248.2005.00739.x
Hannah L, Midgley GF, Lovejoy T, et al. (2002) Conservation of biodiversity in a changing climate. Conserv
Biol 16:264268. doi:10.1046/j.1523-1739.2002.00465.x
Hannah L, Midgley GF, Andelman S, et al. (2007) Protected area needs in a changing climate. Front Ecol
Environ 5:131138. doi:10.1890/1540-9295(2007)5[131:PANIAC]2.0.CO;2
Hannah L, Flint L, Syphard AD, et al. (2014) Fine-grain modeling of speciesresponse to climate
change: holdouts, stepping-stones, and microrefugia. Trends Ecol Evol 29:390397. doi:10.1016/j.
tree.2014.04.006
Hansen MC, Potapov PV, Moore R, et al. (2013) High-resolution global maps of 21st-century forest cover
change. Science 342:850853
Heller NE, Zavaleta ES (2009) Biodiversity management in the face of climate change: a review of 22 years of
recommendations. Biol Conserv 142:1432. doi:10.1016/j.biocon.2008.10.006
Hijmans RJ, Cameron SE, Parra JL, et al. (2005) Very high resolution interpolated climate surfaces for global
land areas. Int J Climatol 25:19651978. doi:10.1002/joc.1276
90 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Imbach P, Molina L, Locatelli B, et al. (2012) Modeling potential equilibrium states of vegetation and terrestrial
water cycle of Mesoamerica under climate change scenarios*. J Hydrometeorol 13:665680. doi:10.1175/
JHM-D-11-023.1
Imbach P, Locatelli B, Molina LG, et al. (2013) Climate change and plant dispersal along corridors in fragmented
landscapes of Mesoamerica. Ecol Evol 3:29172932. doi:10.1002/ece3.672
IPCC (2013) Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen
SK, Nauels JA, Xia Y, Bex V, Midgley PM (eds) The physical science basis, Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. University Press, Cambridge
Jarvis A, Reuter H, Nelson A, Guevara E (2008) Hole-filled seamless SRTM data V4. http://www.cgiar-csi.org/
data/srtm-90m-digital-elevation-database-v4-1.Accessed12Apr2014
Jenness J (2006) Topographic Position Index (tpi_jen.avx) extension for ArcView 3.x, v. 1.3a. Jenness
Enterprises. Available at: http://www.jennessent.com/arcview/tpi.htm
Keppel G, Van Niel KP, Wardell-Johnson GW, et al. (2012) Refugia: identifying and understanding safe havens
for biodiversity under climate change. Glob Ecol Biogeogr 21:393404. doi:10.1111/j.1466-8238.2011.
00686.x
Khatun K, Imbach P, Zamora J (2013) An assessment of climate change impacts on the tropical forests of Central
America using the Holdridge Life Zone (HLZ) land classification system. iForest - Biogeosciences For 6:
183189
Langhammer PF, Bakarr MI, Bennun LA, et al. (2007) Identification and gap analysis of key biodiversity areas:
targets for comprehensive protected area systems. Gland, Switzerland
Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:1822. doi:10.1177/
154405910408300516
Loarie SR, Duffy PB, Hamilton H, et al. (2009) The velocity of climate change. Nature 462:10521055. doi:10.
1038/nature08649
MEA (Millenium Ecosystem Assessment) (2005) Ecosystems and human well-being: synthesis. Island Press,
Washington, DC
Mesoamericano PCB (2007) Informe final proyecto establecimiento de un Programa para la consolidación del
Corredor Biológico Mesoamericano. Managua, Nicaragua
Myers N, Fonseca GAB, Mittermeier RA, et al. (2000) Biodiversity hotspots for conservation priorities. Nature
403:853858. doi:10.1038/35002501
Payton IJ, Fenner M, Lee WG (2002) Keystone species: the concept and its relevance for conservation
management in New Zealand. Sci Conserv 203:529. doi:10.1186/1472-6785-4-10
Phillips SJ, Williams P, Midgley G, Archer A (2008) Optimizing dispersalcorridors for the cape proteaceae using
network flow. Ecol Appl 18:12001211. doi:10.1890/07-0507.1
Plattner G-K (2009) Climate change: terrestrial ecosystem inertia. Nat Geosci 2:467468
Powell GVN, Barborak J, Rodriguez SM (2000) Assessing representativeness of protected natural areas in Costa
Rica for conserving biodiversity: a preliminary gap analysis. Biol Conserv 93:3541. doi:10.1016/S0006-
3207(99)00115-9
Puschendorf R, Carnaval AC, Vanderwal J, et al. (2009) Distribution models for the amphibian chytrid
Batrachochytrium dendrobatidis in Costa Rica: proposing climatic refuges as a conservation tool. Divers
Distrib 15:401408. doi:10.1111/j.1472-4642.2008.00548.x
Rouget M, Cowling RM, Lombard AT, et al. (2006) Designing large-scale conservation corridors for pattern and
process. Conserv Biol 20:549561. doi:10.1111/j.1523-1739.2006.00297.x
Rull V (2009) Microrefugia. J Biogeogr 36:481484. doi:10.1111/j.1365-2699.2008.02023.x
Sala OE, Chapin FS, Armesto JJ, et al. (2000) Global biodiversity scenarios for the year 2100. Science 287:
17701774. doi:10.1126/science.287.5459.1770
Sanchez-Azofeifa G, Daily G, Pfaff A, Busch C (2003) Integrity and isolation of Costa Ricas nationalparks and
biological reserves:examining the dynamics of land-cover change. Biol Conserv 109:123135
Saxon E (2008) Noahs parks: a partial antidote to the Anthropocene extinction event. Biodiversity 9:510. doi:
10.1080/14888386.2008.9712901
SINAC (Sistema Nacional de Áreas de Conservación Costa Rica) (2007) GRUAS II: Propuesta de Ordenamiento
Territorial para la conservación de la biodiversidad de Costa Rica, vol 1. SINAC, San José
SINAC (2009) Corredores Biológicos [mapa]. 1: 50 000. San José:Sistema Nacional de Áreas de Conservación.
Shapefile.
SINAC, FONAFIFO (2014) Tipos de bosque de Costa Rica, Inventario Nacional Forestal (database). Sistema
Nacional de Áreas de Conservación, Ministerio de Ambiente y Energía y Fondo Nacional de Financiamiento
Forestal, San José
USGS (1987) Geology and Resource Assessment of Costa Rica [map]. 1:500,000. USGS: U.S. Geological
Survey
Climatic Change (2017) 141:7792 91
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
VanDerwal J, Shoo LP, Williams SE (2009) New approaches to understanding late quaternary climate fluctua-
tions and refugial dynamics in Australian wet tropical rain forests. J Biogeogr 36:291301. doi:10.1111/j.
1365-2699.2008.01993.x
Watts ME, Ball IR, Stewart RS, et al. (2009) Marxan with zones: software for optimal conservation based land-
and sea-use zoning. Environ Model Softw 24:15131521. doi:10.1016/j.envsoft.2009.06.005
Weltzin JF, Loik ME, Schwinning S, et al. (2003) Assessing the response of terrestrial ecosystems to potential
changes in precipitation. Bioscience 53:941. doi:10.1641/0006-3568(2003)053[0941:ATROTE]2.0.CO;2
Yang LH, Rudolf VHW (2010) Phenology, ontogeny and the effects of climate change on the timing of species
interactions. Ecol Lett 13:110. doi:10.1111/j.1461-0248.2009.01402.x
Zamora N (2008) Unidades Fitogeográficas para la clasificación de ecosistemas terrestres en Costa Rica. Recur
Nat y Ambient 54:1420
92 Climatic Change (2017) 141:7792
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer
Nature at
onlineservice@springernature.com
... To maintain cost-effectiveness and prevent the loss of certain habitat types, the reserve network may therefore have to be adapted under climate change. There are a range of studies investigating how to expand existing reserves under climate change to maintain chosen habitat types in chosen case study areas (Pyke and Fischer, 2005;Fung et al., 2017;Graham et al., 2019;Lawler et al., 2020). However, most current research presents the additional, optimal reserve sites of a case study area necessary at some chosen point in the future. ...
... that do not consist of grid cells but vectors of any shape and size (Jafari and Hearne, 2013), time lags or "conservation credit" (Watts et al., 2020), and changing opportunity costs if the necessary information for the case study-specific parametrisation is available. Given the rapid loss of global biodiversity (Dasgupta, 2021) and the need to adapt existing reserve networks to climate change (Pyke and Fischer, 2005;Fung et al., 2017;Graham et al., 2019;Lawler et al., 2020), we believe 24/34 that further (economic) research in this field may provide important insights in how to conserve biodiversity cost-effectively under climate change. ...
Preprint
Full-text available
Climate change causes range shifts of species and habitats, thus making existing reserve networks less suitable in the future. In principle, reserve networks may be adapted to climate change in two ways: by providing additional funds and / or by allowing for the sale of sites to liquidate funds for new purchases. However, due to general negative ecological consequences, selling is often strongly regulated, thus rendering the optimal reserve design a problem of irreversible investment decisions. On the other hand, allowing for sale may be interpreted as an investment with costly reversibility, as involved transaction costs do not allow for full recovery of the initial investment. Whether allowing for the sale of sites to increase flexibility under climate change outweighs the costs of this increased flexibility remains an open question. We develop a conceptual climate-ecological-economic model to find the optimal solution for the reserve design problem under changing climatic conditions and different policy scenarios. These scenarios differ in terms of whether and when additional funds are provided, and whether selling of reserve sites is allowed. Our results show that the advantage of allowing for sales is large when no additional funds are available and decreases as the amount of additional capital provided for adaptation increases. Finally, providing a one-off payment initially instead of regular payments throughout the runtime of the model leads to higher habitat protection.
... Maintaining effective connectivity is particularly relevant in the scope of offsetting impacts of climate change and other localized disturbances, such as overexploitation or pollution. Well-connected networks of MPAs can reduce and reverse disturbances through the replenishment of the impacted populations from external connected sources, thus, enhancing resilience and decreasing the risk of local extinctions (Magris et al., 2014;Fung et al., 2017). For instance, climate-induced mass mortality events (e.g., extreme conditions during heatwaves; Garrabou et al., 2009) can be reversed by replenishing impacted populations from well-connected sources (Bonin et al., 2016). ...
... This information was unknown until now and may provide useful guidelines for future management of the West Africa network of MPA (Balbar and Metaxas, 2019). In particular, stepping-stone reserves for ecosystem structuring species are particularly relevant to ensure biodiversity persistence under future environmental changes and overexploitation, and increase the network resilience (Fung et al., 2017). For instance, massive mortality events (e.g., Garrabou et al., 2009) can be reversed through the replenishment of impacted populations from wellconnected neighboring locations (Bonin et al., 2016). ...
Article
Full-text available
Marine Protected Areas (MPAs) must function as networks with sufficient stepping-stone continuity between suitable habitats to ensure the conservation of naturally connected regional pools of biodiversity in the long-term. For most marine biodiversity, population connectivity is mediated by passively dispersed planktonic stages with contrasting dispersal periods, ranging from a few hours to hundreds of days. These processes exert a major influence on whether threatened populations should be conserved as either isolated units or linked metapopulations. However, the distance scales at which individual MPAs are connected are insufficiently understood. Here, we use a biophysical model integrating high-resolution ocean currents and contrasting dispersal periods to predict connectivity across the Network of MPAs in Western Africa. Our results revealed that connectivity differs sharply among distinct ecological groups, from highly connected (e.g., fish and crustacea) to predominantly isolated ecosystem structuring species (e.g., corals, macroalgae and seagrass) that might potentially undermine conservation efforts because they are the feeding or nursery habitats required by many other species. Regardless of their dispersal duration, all ecological groups showed a common connectivity gap in the Bijagós region of Guinea-Bissau, highlighting the important role of MPAs there and the need to further support and increase MPA coverage to ensure connectivity along the whole network. Our findings provide key insights for the future management of the Network of MPAs in Western Africa, highlighting the need to protect and ensure continuity of isolated ecosystem structuring species and identifying key regions that function as stepping-stone connectivity corridors.
... These findings are also consistent with previous studies, which have shown that the IAV of NPP or NEP exhibits a positive correlation with temporal variability in precipitation in monsoon areas such as China [42], India [43], and Australia [44]. This finding agrees with the understanding that carbon uptake could be mostly regulated by water availability, since the temperature is not a limiting factor [45]. This finding is also consistent with studies that emphasize the role of water-limited ecosystems on the global IAV of NEP [46]. ...
Article
Full-text available
Global climate change has significantly affected terrestrial carbon sinks. Net ecosystem production (NEP) plays a critical role in the global carbon cycle. However, interannual variability (IAV) of the NEP and its regional contributions and climate attributions are not well-understood on a global scale. This study used a diagnostic model driven by remote sensing leaf area index (LAI) to investigate the NEP IAV and analyze regional and climate contributions on a global scale from 1982 to 2016. We found large NEP IAV during the study period, with the NEP detrended anomaly ranging from −2.3 Pg C in 1998 to 1.6 Pg C in 2013 at a global scale. Furthermore, 63.7% and 34.1% of the areas showed positive and negative contributions to NEP IAVs globally, respectively. Evergreen broadleaf forest (EBF) contributed the most (31.1%) to NEP IAV, followed by cropland (21.7%) and grassland (20.8%). Temperature played the most critical roles in the global NEP IAV, with a contribution of 45.5%. However, the partial correlation between NEP and temperature was negative, and the correlation with precipitation was positive in most areas of the globe, indicating that global warming is not conducive to the global carbon sink, but abundant rainfall is important for the global carbon cycle. This study suggests that, to increase the global carbon sink, we should pay more attention to tropical forests (EBFs) and highlight the importance of water availability.
... Climate change affects the current and future distribution of many species, influencing ecosystems and biotas worldwide [11][12][13][14][15][16]. Global warming has caused significant changes in spatial and temporal environmental patterns, affecting efforts to conserve biodiversity [17][18][19][20][21]. Rapidly changing climates have continued to result in the serious degradation or loss of species habitats, causing declines in population sizes or even the extinction of endangered species [22][23][24][25][26][27][28][29]. ...
Article
Full-text available
Global climate change has become a major threat to biodiversity, posing severe challenges to species conservation. This is particularly true for species such as Horsfieldia tetratepala that have extremely small populations in the wild. Little is known about the species distribution of H. tetratepala in the current climate, as well as how that will change with potential future climates. The key environmental factors that influence its expansion, especially its habitat sustainability and its potential to adapt to climate change, are also unknown, though such information is vital for the protection of this endangered species. Based on six climate factors and 25 species distribution points, this study used the maximum entropy model (MaxEnt) to simulate the potential distribution for H. tetratepala in three periods (current, 2050s, and 2070s), and to investigate the changes in distribution patterns and the main environmental factors affecting species distribution. The modeling results show that the most important bioclimatic variables affecting H. tetratepala were precipitation of the warmest quarter (Bio_18) and temperature seasonality (Bio_4). The suitable areas for H. tetratepala will gradually be lost in Yunnan but will be generally offset in the northeastward direction, expanding in Hainan, Guangzhou, and Taiwan provinces under the future climate conditions. Therefore, we recommend protecting the habitats of H. tetratepala in Yunnan and strengthening the in-depth species investigation and monitoring in areas (Hainan, Guangzhou, and Taiwan) where no related reports of H. tetratepala have been reported. The results improve our understanding of this species’ response under the changing climate and benefit strategies for its conservation.
... Compared to the other types, wet and montane forests host the largest number of amphibians, birds, mammals and reptiles by far, many of them already under threat of extinction or suffering from habitat fragmentation 10 . To protect wildlife and maintain the integrity of these ecosystems, it is thus becoming increasingly important to delineate biological corridors and include them into land development plans (also compare Fung et al. 33 ). Equally, for species facing mountaintop extinction due to the lack of alternative habitats, there is an urgent need to create or extend protected areas to act as refugia. ...
Article
Full-text available
The tropical forests of Central America serve a pivotal role as biodiversity hotspots and provide ecosystem services securing human livelihood. However, climate change is expected to affect the species composition of forest ecosystems, lead to forest type transitions and trigger irrecoverable losses of habitat and biodiversity. Here, we investigate potential impacts of climate change on the environmental suitability of main plant functional types (PFTs) across Central America. Using a large database of occurrence records and physiological data, we classify tree species into trait-based groups and project their suitability under three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) with an ensemble of state-of-the-art correlative modelling methods. Our results forecast transitions from wet towards generalist or dry forest PFTs for large parts of the study region. Moreover, suitable area for wet-adapted PFTs is projected to latitudinally diverge and lose connectivity, while expected upslope shifts of montane species point to high risks of mountaintop extinction. These findings underline the urgent need to safeguard the connectivity of habitats through biological corridors and extend protected areas in the identified transition hotspots.
... According to Fahrig (2003), fragmentation is a landscape-scale process involving the loss and breaking apart of habitat independent from habitat loss. Actually the fragmentation process activities that have been exacerbated by climate change as a result of natural and anthropogenic (Fung et al., 2017). The creation of habitat edge through the fragmentation process affects many species with smaller home ranges and provokes different species responses where members of the community may be using resources across heterogeneous types of landscapes (Fletcher Jr et al., 2018). ...
Article
Full-text available
In recent years, ecological corridors have been proposed on a global scale as a response to the accelerated process of natural ecosystem fragmentation, mainly as a result of human impact. In accordance with this trend, Costa Rica has undergone a process of implementing ecological corridors as to promote ecological connectivity since the 1990s, with the establishment of 44 ecological corridors covering 38% of Costa Rica's territory. Nevertheless, there is no research evaluating these corridors on a national scale that takes into account their functions as conduits, barriers, and habitats. Thus, the objective of this research was to describe the process of biological corridor formation in Costa Rica, and to evaluate the potential effectiveness of corridors by considering aspects of landscape structure and ecological processes related to connectivity and fragmentation. We used the National Program of Ecological Corridors database along with coverage analysis from Landsat images from 2000 and 2015.The composition of the biological corridors was determined at the landscape scale and related to potential to maintain a specific population of wild mammals weighing more than 10 kg. The composition of the ecological corridors was highly variable in terms of total area, proportion of natural habitat, and fragmentation process. Most biological corridors are capable of maintaining viable populations of Pecari tajacu and Tapir bairdii, while none could maintain populations of Panthera onca and Tayassu pecari. Only 50% of the biological corridors had improved in their connectivity. Therefore, public policies, such as master plans focusing on ecosystem restoration must be established. In addition, only two biological corridors incorporate the majority of elevation ranges (Life Zones) present in the country, which reduces the potential of the corridor system as a tool for climate change adaptation. K E Y W O R D S connectivity, Costa Rica, ecological corridor, protected areas
Article
Full-text available
Connectivity plays a key role in the effectiveness of MPA networks ensuring metapopulation resilience through gene flow and recruitment effect. Yet, despite its recognized importance for proper MPA network functioning, connectivity is not often assessed and is very seldomly used in marine spatial planning. Here, we combined biophysical models with graph theory to identify Mediterranean marine reserves that contribute towards the connectivity between different ecoregions thus preventing further network fragmentation, and those that have an important role as propagule source areas contributing to the recruitment and rescue effects. The Côte D’Azur marine reserves play an important role both as stepping-stones and propagule source areas for several ecological groups. Also key are the Capo Rizzuto and Plemmirio marine reserves due to their role as stepping stones between different marine ecoregions, particularly for species with longer propagule duration (Pisces, Crustacea and Echinodermata). These results provide stakeholders and managers with crucial information for the implementation and management of an efficient marine reserve network in the Mediterranean.
Thesis
Full-text available
La dinámica del uso de la tierra y la variabilidad climática son precisamente eso, dinámicas; por lo tanto, son un fenómeno cambiante y cabe aclarar que no son procesos nuevos. Conocer estos datos ayuda a identificar las posibles implicaciones socioambientales y económicas para el área de estudio. Para este caso, el área de estudio es la subcuenca del río Panajachel, cuenca del lago de Atitlán, Sololá, de donde se conocen pocos estudios y datos relacionados con la variabilidad climática y su interacción con el uso de la tierra en el país y en el área específica de estudio, a pesar de la importancia que reviste en cuanto a la gestión integral del recurso hídrico; sobre todo porque esta área geográfica es de interés económico, social, turístico y político. En cuanto a la dinámica del uso de la tierra y la variabilidad climática en la subcuenca del rio Panajachel en Sololá; como antecedente se menciona en Ochoa (2009) que, en el área bajo estudio, para el año 2009 ya existía un 48.54 % del territorio de sobreutilización, y únicamente un 27.48 % en uso correcto. Siendo este uso correcto únicamente en algunas de las áreas montañosas y de ladera, mismas que cuentan con cobertura vegetal y otras muy pocas que cuentan con actividades agrícolas en partes de altiplanicie. Los anteriores hallazgos presentan una alarmante información para la actualidad y según los escenarios modelados para el futuro también, ya que devela el nivel de degradación que existe actualmente en el área analizada. En ese mismo sentido y para contextualizar la situación a nivel macro, se menciona en Netzer et. al. (2011) que, para América Latina, la degradación, el cambio del uso de la tierra y la deforestación, son algunos de los orígenes importantes de la concentración de gases de efecto invernadero; identificando que las emisiones de CO2 generadas por cambio de uso de la tierra y silvicultura, superan la cantidad que puede ser absorbida por los bosques tropicales. La transformación de las áreas de bosque muestra una superficie obtenida en el año 1980 de 49.14 km2 equivalente al 69.8 % del área total de la subcuenca; en el año 1990 se reduce a 43.62 km2 (61.96 %); en el año 2000 llega a 39.13 (55.58 %); 2010 con 36.62 km2 (52.01 %), y para el año 2030 se reduce a 31.26 km2 y al 2040 tan solo 28.31 km2, que representarían el 40.21 % de la subcuenca. En cuanto a la tendencia de la precipitación se identificó que de1994 al 2001 se observa una tendencia normal; sin embargo, de los años 2002 al 2010 las tendencias de la precipitación comienzan a variar y se normalizan del 2011 al 2017; luego, se observa una disminución en la precipitación promedio anual de 1,235 mm anual en los últimos 7 años. En cuanto al análisis de la erosión hídrica en la subcuenca en el 2018, utilizando la Ecuación Universal de Pérdida del Suelo Revisada (RUSLE) se determinó que existe muy alta erosión con pérdidas mayores a 200 Ton/ha/año y una cobertura del 39.82 % del área total de la subcuenca, el 17.79 % de erosión alta entre rangos de 50-200 Ton/ha/año y moderada, 21.44 %. Entre 10 a 50 Ton/ha/año y 22.26 % con una erosión nula o leve menor a 10 Ton/ha/año. Del 2001 al 2018 se observa un incremento del uso de la tierra para agricultura anual de 18.63 a 22.96 km2 y una disminución del área para bosques de 27.50 a 21.79 km2. Para el 2030 se determinaron los posibles valores de erosión siguientes: el 42.70 % muy alta y el 16.19 % alta; al combinar estos dos valores se obtiene un resultado de 58:89 % del área total en riesgo de erosión. Sin embargo, el 21.05 % tiene riesgo de erosión moderada y eso es significativo en el área, dada la dinámica del uso de la tierra hacia cultivos agrícolas y urbanización acelerada. Para el 2040 los posibles valores de erosión son: el 55.02 % muy alta y el 18.35 % alta; al combinar estos dos valores se obtiene un resultado de 73.37 % del área total en riesgo de erosión. Sin embargo, el 14.29 % de erosión moderada es significativa en el área, de seguir la dinámica del uso de la tierra tal como se proyecta actualmente. Por lo tanto; se concluye que la dinámica del uso de la tierra es variable y está relacionada con la variabilidad climática a través de la precipitación, al evidenciar que hay incremento de erosión hídrica; por lo que se recomienda implementar de manera inmediata una propuesta de gestión del uso de la tierra orientada hacia la conservación del suelo en las áreas de mayor riesgo a erosión, así como en las áreas con cobertura agrícola para adaptarse a la variabilidad climática. Asimismo, se recomienda profundizar en la explicación del fenómeno del cambio climático y su relación con los usos de la tierra en la subcuenca, e investigar sobre la relación del cambio del uso de la tierra y su influencia en la hidrología e hidrogeología de la subcuenca.
Technical Report
Full-text available
This monograph presents expert assessments of four different facets of Latin American and Caribbean (LAC) forests at the start of the 2020s. In Chapter 1, Dan Nepstad and coauthors distill lessons from case studies of the application of various approaches to forest conservation and restoration in four countries: Brazil, Costa Rica, Ecuador, and Peru. In Chapter 2, Carlos Nobre and coauthors examine the two-way links between forests and climate change. They summarize what we know about the effects of climate change on forests and human migration in LAC, and the effects of forest loss and degradation on global and regional climate change. In addition, they present case studies of some of these links for Brazil and Costa Rica. In Chapter 3, Brent Sohngen explores LAC forest management, including LAC trends in international trade in timber and bioenergy, sustainable forest management, nontimber forest products, illegal logging, property rights, and climate change as it affects managed forests. In addition, Dr. Sohngen summarizes an original analysis of future timber supply potential using the Global Timber Model. Finally, in Chapter 4, Simone Bauch presents an analysis of the IADBGs experience with forest projects over the past 13 years. Having reviewed IADBG documents on all 99 forest projects approved by bank during this period and interviewed 23 current and former bank staff, Dr. Bauch presents a brief recent history of IADBG forest projects, an overview of the major determinants of project development, and an analysis of trends in forest projects, including their number, funding, objectives, themes, and locations. An Introduction by the editor, Allen Blackman, discusses the broad issues these expert assessment address and summarizes their key findings.
Article
Gross primary production (GPP) quantifies the photosynthetic uptake of carbon by the terrestrial ecosystem. Positive GPP extremes represent the potential capacity of the terrestrial ecosystem to uptake carbon dioxide. Studying the positive GPP extreme is vital for the global carbon cycle and mitigation of global warming. With increasing climate extreme events, many kinds of research focus on studying negative GPP and the negative impact of climatic extremes on GPP. There is still a lack of research on positive GPP extremes and whether climatic extremes could be beneficial to global carbon uptake. In this study, we used daily Boreal Ecosystem Productivity Simulator (BEPS) to simulate GPP of the global terrestrial ecosystem during 1982-2016 and combined TRENDY models to detect positive GPP extremes and investigate the effects of climate extremes on GPP. We found the results of the TRENDY models have large differences in some areas of the globe, and the BEPS model driven by remote sensing data could be more suitable for simulating the long-term time series of global terrestrial GPP. Compared to other plant functional types, grasslands contributed the most to positive GPP extremes, accounting for approximately 41.6% (TRENDY) and 34.8% (BEPS) of the global positive GPP extremes. The probabilities of positive GPP extremes caused by positive precipitation extremes were significantly higher than those caused by temperature and radiation in most areas of the globe, indicating that sufficient precipitation (not a flood) would boost the carbon uptake ability of the global terrestrial ecosystem to form positive GPP extremes. On the contrary, the partial correlation coefficients between temperature and GPP were negative in most areas of globe, suggesting that global warming will not be conducive to carbon uptake of the terrestrial ecosystem. This study may provide new knowledge on the global positive GPP extremes.
Article
Full-text available
The keystone species concept has proved both promising and elusive in theoretical and applied ecology. The term has its origins in Robert Paine's studies of rocky shore communities in California. When the top predator (a starfish) was removed the species assemblage collapsed, prompting the architectural analogy with the keystone of an arch. By definition keystone species are those whose effect is large, and disproportionately large relative to their abundance. They include organisms that (i) control potential dominants, (ii) provide critical resources, (iii) act as mutualists, and (iv) modify the environment. Identifying keystone species can be problematic. Approaches used include experimental manipulations, comparative studies, natural history observations, and 'natural experiments', but no robust methodologies have been developed. Our inability to monitor and manage all aspects of biodiversity has led to the development of paradigms that focus on either single-species (e.g. indicators, umbrellas or flagships) or whole ecosystems (ecological processes and habitats). Not surprisingly, both have their advocates and detractors. The keystone species concept, which retains a species focus while avoiding the need to examine every species, and emphasises processes that directly (e.g. predation, competition) rather than indirectly (e.g. nutrient cycling) control biodiversity, may allow managers to combine the best features of both these paradigms. By itself, however, the concept is unlikely to provide a panacea for biodiversity managers.
Article
Full-text available
Ecological models have predicted shifts in forest biomes, yet there have been very few studies that have looked at the implications on carbon stocks due to these shifts. Carbon is closely correlated to biomass and constitutes an important characteristic of the forest ecosystem. It has implications for conservation and land use practices, especially for climate change mitigation strategies currently under discussion, such as the Reduced Emissions from Deforestation and Forest Degradation (REDD). This study couples the Holdridge Life Zone (HLZ) classification with the ECHAM5 model, to evaluate the impacts of climate change using the Special Report on Emissions Scenarios (SRES) A2, A1B and B1 for the Central American region. We utilize methodologies which combine biophysical variables with model output to assess the impacts on carbon stocks for two time periods, 2000 and 2100. Results show that overall the tropical category of the HLZ classification gains area as a consequence of one type of HLZ shifting to another forest type. In many cases the shifts lead to some categories of HLZ being lost in their entirety. Elevation-associated life zones are particularly vulnerable to future climatic changes. A strong point of our approach is that differences between disaggregate regional and aggregate country levels can be compared. We suggest that a critical focus of conservation and management efforts should be concentrated on where vulnerable biomes are at most risk, i.e., biomes that shift and/or reduce fall under the vulnerable category.
Article
Full-text available
Abstract Concern for climate change has not yet been integrated in protocols for reserve selection. However if climate changes as projected, there is a possibility that current reserve-selection methods might provide solutions that are inadequate to ensure species' long-term persistence within reserves. We assessed, for the first time, the ability of existing reserve-selection methods to secure species in a climate-change context. Six methods using a different combination of criteria (representation, suitability and reserve clustering) are compared. The assessment is carried out using European distributions of 1200 plant species and considering two extreme scenarios of response to climate change: no dispersal and universal dispersal. With our data, 6–11% of species modelled would be potentially lost from selected reserves in a 50-year period. Measured uncertainties varied in 6% being 1–3% attributed to dispersal assumptions and 2–5% to the choice of reserve-selection method. Suitability approaches to reserve selection performed best, while reserve clustering performed poorly. We also found that 5% of species modelled would lose their entire climatic envelope in the studied area; 2% of the species modelled would have nonoverlapping distributions; 93% of the species modelled would maintain varying levels of overlapping distributions. We conclude there are opportunities to minimize species' extinctions within reserves but new approaches are needed to account for impacts of climate change on species; especially for those projected to have temporally nonoverlapping distributions.
Article
Full-text available
Microclimates have played a critical role in past species range shifts, suggesting that they could be important in biological response to future change. Terms are needed to discuss these future effects. We propose that populations occupying microclimates be referred to as holdouts, stepping stones and microrefugia. A holdout is a population that persists in a microclimate for a limited period of time under deteriorating climatic conditions. Stepping stones successively occupy microclimates in a way that facilitates species' range shifts. Microrefugia refer to populations that persist in microclimates through a period of unfavorable climate. Because climate projections show that return to present climate is highly unlikely, conservation strategies need to be built around holdouts and stepping stones, rather than low-probability microrefugia.
Article
Full-text available
The reorganization of patterns of species diversity driven by anthropogenic climate change, and the consequences for humans, are not yet fully understood or appreciated. Nevertheless, changes in climate conditions are useful for predicting shifts in species distributions at global and local scales. Here we use the velocity of climate change to derive spatial trajectories for climatic niches from 1960 to 2009 (ref. 7) and from 2006 to 2100, and use the properties of these trajectories to infer changes in species distributions. Coastlines act as barriers and locally cooler areas act as attractors for trajectories, creating source and sink areas for local climatic conditions. Climate source areas indicate where locally novel conditions are not connected to areas where similar climates previously occurred, and are thereby inaccessible to climate migrants tracking isotherms: 16% of global surface area for 1960 to 2009, and 34% of ocean for the 'business as usual' climate scenario (representative concentration pathway (RCP) 8.5) representing continued use of fossil fuels without mitigation. Climate sink areas are where climate conditions locally disappear, potentially blocking the movement of climate migrants. Sink areas comprise 1.0% of ocean area and 3.6% of land and are prevalent on coasts and high ground. Using this approach to infer shifts in species distributions gives global and regional maps of the expected direction and rate of shifts of climate migrants, and suggests areas of potential loss of species richness.