Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
Role of African protected areas
in maintaining connectivity
for large mammals
rstb.royalsocietypublishing.org
Martin Wegmann1, Luca Santini2, Benjamin Leutner1, Kamran Safi3,4,
Duccio Rocchini5, Mirjana Bevanda6, Hooman Latifi1, Stefan Dech1,7
and Carlo Rondinini2
1
Research
Cite this article: Wegmann M, Santini L,
Leutner B, Safi K, Rocchini D, Bevanda M, Latifi
H, Dech S, Rondinini C. 2014 Role of African
protected areas in maintaining connectivity
for large mammals. Phil. Trans. R. Soc. B 369:
20130193.
http://dx.doi.org/10.1098/rstb.2013.0193
One contribution of 9 to a Theme Issue
‘Satellite remote sensing for biodiversity
research and conservation applications’.
Subject Areas:
ecology, environmental science
Keywords:
AVHRR, GIMMS, conservation, dispersal,
NDVI, protected area network
Author for correspondence:
Martin Wegmann
e-mail: [email protected]
Department of Remote Sensing, Remote Sensing and Biodiversity Research Group, University of Wuerzburg,
Wuerzburg, Germany
2
Global Mammal Asssessment Program, Department of Biology and Biotechnologies,
Sapienza University of Rome, Rome, Italy
3
Department of Migration and Immuno-ecology, Max Planck Institute for Ornithologe, Radolfzell, Germany
4
Department of Biology, University of Konstanz, Konstanz, Germany
5
Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Fondazione Edmund Mach,
Research and Innovation Centre, San Michele all’Adige, Trentino, Italy
6
Biogeographical Modelling, BayCEER, University of Bayreuth, Bayreuth, Germany
7
German Aerospace Center, Earth Observation Center, DLR-DFD, Oberpfaffenhofen, Germany
The African protected area (PA) network has the potential to act as a set
of functionally interconnected patches that conserve meta-populations of
mammal species, but individual PAs are vulnerable to habitat change which
may disrupt connectivity and increase extinction risk. Individual PAs have
different roles in maintaining connectivity, depending on their size and location.
We measured their contribution to network connectivity (irreplaceability) for
carnivores and ungulates and combined it with a measure of vulnerability
based on a 30-year trend in remotely sensed vegetation cover (Normalized
Difference Vegetation Index). Highly irreplaceable PAs occurred mainly in
southern and eastern Africa. Vegetation cover change was generally faster outside than inside PAs and particularly so in southern Africa. The extent of change
increased with the distance from PAs. About 5% of highly irreplaceable PAs
experienced a faster vegetation cover loss than their surroundings, thus requiring particular conservation attention. Our analysis identified PAs at risk whose
isolation would disrupt the connectivity of the PA network for large mammals.
This is an example of how ecological spatial modelling can be combined with
large-scale remote sensing data to investigate how land cover change may
affect ecological processes and species conservation.
1. Introduction
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rstb.2013.0193 or
via http://rstb.royalsocietypublishing.org.
As suitable habitat declines, species’ distribution ranges become fragmented and
disjointed in sparsely distributed, small populations which are characterized by
an increased extinction risk [1–5]. Range fragmentation has already been experienced worldwide in densely populated areas, for example Europe [6]. In Africa,
the socio-economic development of the past 30 years has led to severe habitat
loss and fragmentation, resulting in an increased and unprecedented threat to wildlife [7–9]. Many large mammal populations in Africa have been declining over the
same period [10]. According to future biodiversity scenarios, Africa is expected to
be among the continents with the largest predicted habitat loss by 2050 [11].
In this scenario, the network of African protected areas (PAs) is expected to play
a fundamental role in future biodiversity conservation. PAs can in fact host subpopulations that form the basis for meta-population dynamics [12,13], where
immigrating individuals from source populations can come to the ‘rescue’ of declining sink populations (sink–source dynamics). According to the meta-population
theory, the two fundamental processes, extinction and (self-)colonization rates,
act in concert with patch area and distances between patches to determine the
& 2014 The Author(s) Published by the Royal Society. All rights reserved.
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
The study region encompasses 469 PAs on the African continent
that intersect with at least one carnivore or ungulate geographical
range. The analysis was conducted on a spatial resolution of 10 km.
(a) Protected areas and species distribution data
In total, 76 species of carnivores and ungulates occurring in
Africa were used for this study (electronic supplementary
material, table S1). Species distributions were derived from habitat suitability models inside species’ geographical ranges
according to Rondinini et al. [33]. These models were developed
through expert-based species – habitat relationships. They
include three suitability levels: high (corresponding with the primary habitat of the species), medium (where the species can be
found but is not persisting in the absence of primary habitat)
and low suitability (where the species is expected to be rarely
found) [33]. The models are clipped to geographical ranges
obtained from IUCN.1 Median and maximum dispersal dis-
tances were calculated from the allometric equations in
Santini et al. [34]. The PA data were obtained through the
World Database on Protected Areas [35].
(b) Irreplaceability analysis
An individual-based stepwise model that simulates species-specific
dispersal among PAs was used to measure the irreplaceability
value of each PA in maintaining the network connectivity. This
model aims to analyse connectivity by incorporating ecologically
(c) Land-cover information from remote sensing
time-series
Deriving land-cover characteristics and their changes over time to
account for the PA status requires a long time-series, which was provided by the Global Inventory Modelling and Mapping Studies
(GIMMS) from 1982 to 2006. This dataset provides NDVI values
every 10 days based on US National Oceanic and Atmospheric
Administration AVHRR datasets on a spatial resolution of 8 km
[36]. NDVI is a proxy for vegetation amount and its condition
(photosynthetic activity) within a pixel. The NDVI ranges from
2
Phil. Trans. R. Soc. B 369: 20130193
2. Material and methods
relevant information into a landscape configuration analysis. This
model is related to meta-population model approaches by incorporating dispersal capabilities but aims to provide a simplistic
approach for range connectivity analysis. This model counts the
average number of steps from emigration of the source PA to immigration in new PAs by each individual within the geographical
range of the species. Immigrations into the source PA are ignored
by the model. The suitability of the landscape and the species’ perceptual range was also considered in the analysis. We assigned the
above-mentioned categories of habitat suitability with the following values: the ‘low suitability’ category was given high dispersal
cost (100 cost units), the ‘medium suitability’ moderate cost (50)
and the ‘high suitability’ was given no costs (0). In order to account
for a possible impact of the habitat suitability settings, a sensitivity
analysis was conducted on the dispersal costs. Therefore, two
additional costing schemes were applied which reflect the relative
magnitude of the landscape suitability to each other. To analyse
the sensitivity concerning the differences within the chosen
values, we set the values to 75, 50, 25 and 51, 50, 49 for low,
medium and high suitability, respectively. The habitat suitability
matrix was used to select the likelihood of the next step location
for individuals to move through the landscape between two PAs.
This likelihood of selecting a pixel for the next step increases with
increasing suitability, owing to the preference of the animals to
stay in highly suitable habitat. The default step direction was
sampled from a 1808 forward angle using the suitability values
for the next step movement probability. Owing to the design of
the model, only the connectivity is analysed and no dispersal success or post-breeding dispersal probabilities are included. To
scale perceptual abilities across different species, we arbitrarily set
the perceptual range to be equal to 50% of the species’ median dispersal distance. Higher differentiation of the perceptual range was
not feasible because of the spatial resolution used. Note that we
did not consider this as the real perceptual ability of the species.
Moreover, the presence of PAs within the perceptual range was
translated into a doubled probability of a step towards this direction. Individual movements were constrained in a buffer around
the source PA, equal to the species’ maximum dispersal distance.
The irreplaceability value of a PA was given by the average
difference in search time between the model considering all PAs
and the model excluding that particular PA (when a PA does not
affect search time because it is isolated, the difference is equal to
zero). This analysis was iterated for each PA and computed for
each species. For each PA, 100 individuals were released and the
simulations were conducted until 80% of the released individuals
had immigrated successfully into a PA other than the source. This
percentage was set as an internal parameter for the model to terminate in order to account for individuals not emigrating into any PA.
For each species, the search times of all PAs were standardized
by the maximum search time per species. The standardized values
for all species were summed and assigned to each PA as its irreplaceability value. This is a proxy for the importance of PAs to
support the dispersal connectivity for all studied species. The irreplaceability values were standardized for all species and summed
up for all PAs. This is reflecting the importance of PAs to support
the connectivity of the network.
rstb.royalsocietypublishing.org
capacity of a fragmented habitat to support a viable metapopulation structure [14,15]. Maximizing and maintaining
connectivity between PAs in a fragmented landscape is a fundamental element of the overall population persistence [1,2,16].
Individual PAs play different roles in the maintenance of connectivity in a network, depending on their size and
geographical location [17–20], and this can be translated in a
measure of their irreplaceability for maintaining the overall
level of connectivity of the PA network.
Continuous long-term remote-sensing series, such as those
provided by the Advanced Very High-Resolution Radiometer
(AVHRR), are valuable tools to analyse land-cover change over
time [21,22], making it possible to monitor biodiversity trends
[23,24]. In particular, they can be used to infer the land-cover
dynamics of PAs [25–28], which are highly relevant for informing management and assessing PA isolation [29,30]. Though
widely used for ecological applications, remote-sensing
data and methods are still underexploited for conservation
monitoring purposes [10,31,32].
In this paper, we combine ecological data on a large set of
species of carnivores and ungulate within Africa with Earth
observations, to assess the habitat change within PAs in
relation to their relative importance within the network. We
use a measure of connectivity among PAs based on the
spatial arrangement of suitable habitat within each species’
range, to quantify individual PA contribution to the overall
connectivity of the network, and hence a value for the irreplaceability of each PA. We couple this measure with an
estimated rate of vegetation change inside and around PAs
over the past 30 years based on the Normalized Difference
Vegetation Index (NDVI). The combination of these variables
identifies the African PAs at high risk of loss and high irreplaceability for the connectivity of large mammal populations
that should be prioritized for conservation.
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
3
5 000 000
rstb.royalsocietypublishing.org
2 500 000
0.75
0.50
0
0.25
0
–2 500 000
0
–2 × 106
1000 km
0
2 × 106
4 × 106
Figure 1. Irreplaceability of African PAs to maintain connectivity of the PA network for large mammals. High values indicate a high irreplaceability of the PA and are
therefore highly important for the connectivity in this area. (Online version in colour.)
21 to 1, with vegetation values being larger than zero. Bare soil and
water show values of close to zero and below, respectively. For vegetation, the value increases with the amount of vegetation present
and high photosynthetic activity. We computed the mean NDVI
value per year for the whole of Africa and resampled the data to
10 km to match the spatial resolution of the irreplaceability analysis.
In order to compare changes of vegetation cover inside and
around PAs, we calculated the difference between the annual
mean NDVI within each PA and in buffers around each PA.
We used three buffers of increasing size, corresponding to the
median, and first and fourth quartile of maximum species dispersal distances across all analysed species. This leads to
buffers of 17, 31 and 84 km around each PA. The differences
were then linearly regressed against time. A positive trend indicates growing difference of vegetation cover inside PAs when
compared with their surroundings. A positive trend is therefore
interpreted as a higher rate of vegetation cover change around
the PA than inside; a flat trend has no change of vegetation
cover inside or outside (or an identical trend inside and outside);
and a negative trend has a faster change of vegetation cover
inside the PA than outside or a faster increase outside the PA.
The analysis was performed in R v. 2.15.2 [37] and GRASS 6.4.2
[38,39], using the following R packages for spatial data manipulation, conversion and exchange as well as general statistical
techniques: ‘spgrass6’ [40], ‘raster’ [41], ‘rgdal’ [42], ‘rgeos’ [43],
‘reshape2’ [44] and ‘rsm’ [45] as well as the ‘r.pi.searchtime.iter’
package [46] in the GRASS add-on repository for the irreplaceability
and the connectivity analysis.
3. Results
The irreplaceability values ranged from zero to unity in a unimodal distribution, with a median of 0.41 (s.d. 0.15, see the
electronic supplementary material, Appendix S1, for histogram). PAs with a high irreplaceability and therefore a high
importance for the connectivity or acting as stepping stones
(above overall median values) occurred mainly in southern
and eastern Africa, namely Namibia, Botswana, Zimbabwe,
South Africa, Mozambique, Zambia, Tanzania and Kenya
(figure 1). In southern Africa, Etosha (0.49), Kafue (0.53),
Phil. Trans. R. Soc. B 369: 20130193
irreplaceability
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
4
buffer size: 17 km dispersal range
buffer size: 32 km dispersal range
buffer size: 84 km dispersal range
2 500 000
significant slope in
NDVI differences
20
0
0
–20
(b)
5 000 000
buffer size: 17 km dispersal range
buffer size: 32 km dispersal range
buffer size: 84 km dispersal range
2 500 000
r2 of significant slopes
in NDVI differences
0.8
0.6
0
0.4
0.2
−2 500 000
−2 × 106
0
2 × 106 4 × 106
−2 × 106 0
2 × 106 4 × 106
−2 × 106 0
2 × 106 4 × 106
Figure 2. Differences of NDVI values inside and outside PAs between 1982 and 2006 (slope values are displayed in the top row and r2 values are displayed in the
bottom row; values are derived from a linear regression of time against the difference in values). Grey areas experienced no significant change in differences across
the time span 1982– 2006. Row (a): red areas showed a decrease in the slope of the linear regression of NDVI outside, and blue areas a difference of increasing
NDVI outside. Row (b): the r2 values are showing high values of the linear regression fit in red, and low values in blue. (Online version in colour.)
Kalagadi (0.85) and Kalahari (0.72) feature high irreplaceability
values. In East Africa, high irreplaceability values are found for
Ruaha (0.54) and Selous (0.63) in Tanzania and a slightly lower
value for Serengeti (0.46).
Low to moderate irreplaceability (equal and below
median values) was assigned to many PAs in Central and
West Africa. In Central Africa, the Dzanga-Ndoki (Central
African Republic) features a higher irreplaceability than surrounding areas with 0.47, which is comparable to Tai (0.44)
in Ivory Coast. Areas with low irreplaceability values are
Kabore-Tambie (0.17) in Burkina Faso and Marahoue (0.19)
in Ivory Coast. A total of 19% of all PAs have an irreplaceability value higher than 0.5. The values for all PAs are listed in
the electronic supplementary material, table S2.
The sensitivity analysis of changing land-cover suitability
values did result in highly correlated results (Kendall’s tau
0.76– 0.87). The results differed by PAs but showed a linear
pattern between each of the sensitivity models. The values
of all PAs for the different suitability settings are plotted in
the electronic supplementary material, figure S2. The map
of overall species richness of the data used can be found in
the electronic supplementary material, appendix S3.
PAs with high importance in southern Africa showed
similar trends in vegetation cover compared to the surrounding areas (figure 2). Several PAs in eastern Africa showed a
lower rate of vegetation cover change inside PAs than
outside. Some PAs lost vegetation cover at a significantly
faster rate than the surroundings (21 –26% of the PAs,
depending on the buffer size, figure 2). In addition, 4–5%
of the PAs with high irreplaceability experienced a significantly faster change of vegetation cover inside than that
around them.
The Serengeti National Park lost vegetation cover faster
than its surrounding 84 km buffer, but not within its smaller
buffers (17 km: n.s.; 32 km: n.s.; 84 km: p ¼ 0.05). Selous or
Ruaha in Tanzania showed no differences in either direction.
High increase in differences for single PAs can be seen in
Uganda and Kenya where the value outside the PAs
decreased compared with the ones inside PAs. This is true
for Mount Kenya (17 km: p ¼ 0.02; 32 km: p ¼ 0.004; 84 km:
p , 0.008) and Matheniko (17 km: p ¼ 0.001; 32 km: p ¼
0.001; 84 km: p ¼ 0.001) in Uganda. This pattern can also be
found for PAs in Central and West Africa, e.g. for the Park
W complex part of Benin, the Kabore-Tambi in Burkina
Faso and for the Marahoue area in Ivory Coast. Other areas
such as the Tai and Comoé National Park in Ivory Coast
showed no changes across this time span, as well as areas
in the Sahel and Sahara or the Congo Basin.
PAs in Botswana, such as Kalahari and Kgalagadi, are
shown in green (figure 2), which indicates low NDVI differences and high importance. Other PAs, for example the
Matheniko in Uganda, are depicted in purple, which
Phil. Trans. R. Soc. B 369: 20130193
−2 500 000
rstb.royalsocietypublishing.org
(a)
5 000 000
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
4. Discussion
(a) Sensitivity and assumptions of the analysis
As in any model, the results of our analysis depend on the
available species distribution data, their accuracy and on the
model settings [49]. The former have been evaluated, and perform better than random for ca 95% of the species for which
validation data were available [33]. Our sensitivity analysis
tested the effect of model parameters on the results, and the
model proved robust to changes in their values. The changes
between models were linear with a fit of 0.76–0.86; hence the
values for the majority of PAs did not experience major differences. The connectivity of the PA network on a local scale
depends on the permeability of their surroundings, which
may influence population persistence [50]. The dispersal distances we used in our analysis were derived from allometric
equations, fitted on dispersal distances obtained from a variety
of studies [34]. Thus, they already incorporate the variable
permeability of the landscape.
Remote sensing is particularly suitable for detecting
land-cover changes because it allows a rapid mapping over
wide regions and represents a robust and reproducible tool.
This is especially useful when the objects composing land
cover maps have been generated by agglomerative methods
based on objective algorithms, for example image segmentation. Reviews of this issue can be found in Hay et al. [51] and
Blaschke [52], while Karl & Maurer [53] provide empirical
examples of their application in land-cover mapping. Various
studies have used remotely sensed data successfully for landscape or animal ecology [27] and for PA effectiveness analysis
[54]. Remote sensing usually monitors some threats to biodiversity, for example vegetation cover change, including
localized ones such as oil spill in the Sahara ([55], this
issue). While this may be a limitation for local-scale analyses,
pristine vegetation cover change (and deforestation in particular) remains the biggest threat to mammals worldwide
[56]. Our analysis implicitly assumes that local effects on vegetation cover change are negligible.
The observed differences inside and outside PAs in vegetation cover change relate to the accuracy in detecting land
cover modifications. Loss of rainforest, for example, is
easier to detect compared with habitat loss in deserts, even
if differences and accuracy differ between approaches
[57]. Yet, we detected positive and negative differences
(b) Protected area irreplaceability
Our results show that southern and eastern African PAs are
the most irreplaceable for maintaining network connectivity.
This finding complements and builds upon previous studies
that report higher conservation effectiveness of southern
African PAs [10]. This result is likely driven by the size of
the PAs (on average larger than in West and North Africa)
and additionally the higher number of large mammals in
the savannah biome [59].
Our measure of irreplaceability is new to our knowledge.
It is based on one specific property of the PA network (connectivity) that is not usually accounted for explicitly in
conservation planning. Geldmann et al. [54] acknowledged
a general shortage of understanding on PA effectiveness,
and we believe that our measure can help to fill one of the
existing theoretical gaps that prevent a better understanding.
Alternative measures of irreplaceability generally estimate
the importance of an area for the achievement of a conservation target, which is expressed in terms of number of
individuals or area occupied by a species to be included in
PAs [60– 62]. Complementing our irreplaceability estimates
with traditional irreplaceability analysis would provide a
more comprehensive picture of the conservation value of
African PAs for large mammals.
Mapping irreplaceable areas for connectivity is highly
relevant not only for dispersing individuals, whose movements
are directly related to population dynamics, but also for
migratory species and to estimate the importance of PAs in
climate change adaptation [63], as they could function as corridors. Thus, in addition to regional migration (sensu Thirgood
et al. [64]) considered here, further analyses should also take
large-scale migration into consideration, a task that will soon
be feasible at the spatial and temporal extents required [65].
(c) Combining irreplaceability with vulnerability
Our analysis showed that the rate of change in vegetation
change inside most African PAs was similar or lower than
that in their surroundings. Across the whole African continent, an annual forest decline of 0.14 –0.28% (depending on
the decade) has been reported [26]. This is likely to be
detected by NDVI if occurring in or around forest PAs. By
contrast, desert ecosystems are rather difficult to analyse
5
Phil. Trans. R. Soc. B 369: 20130193
Our analysis highlights the capabilities of coupling spatial
modelling with large-scale ecological and remote-sensing
data for a better understanding of PA status and value in
broad-scale geographical domains. In this case, we used
NDVI continuous data, which demonstrated an intrinsic
power in discriminating among trends in vegetation cover
change. Our analytical design accounted for the regional variation in vegetation cover (e.g. forest, savannah, desert), which
is indeed the main source of variation among PAs [47,48].
throughout the study area, and we assumed an equal
probability to detect change unimportant for the biome.
Differences inside and outside PAs may not necessarily
depend solely on cover loss outside PAs, but also on cover
gain inside PAs. As an example, an increased herbivore
population density inside PAs might result in a changing
vegetation cover [58] while the surrounding area remains
stable. Hence, an increasing difference might not in all
cases be the result of human-induced land-cover conversion
around PAs. Also the land-cover inside and outside the PAs
can change in the same manner, and therefore no differences
are detected even though the PA is in a different state than
before. Many PAs might have already experienced a
change in their surroundings before the start of remote sensing time-series, and therefore these changes cannot be
detected. Although the dynamics described above may
apply to some PAs, we assumed that all differences detected
could be attributed to a decrease of vegetation cover outside
PAs (figure 3).
rstb.royalsocietypublishing.org
indicates a low importance and a high increase in NDVI
differences inside and outside the PA. The overall pattern
reveals that in southern and eastern Africa generally more
PAs exist which have a high importance for maintaining
the connectivity between species ranges, while at the same
time experiencing a low difference in land-cover change
within and without.
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
6
5 000 000
rstb.royalsocietypublishing.org
Phil. Trans. R. Soc. B 369: 20130193
2 500 000
0
legend
−2 500 000
irreplaceability
0.8
0.6
0.4
0.2
0
–25.4 –13.5 –1.6 10.3 22.3
NDVI trend
−2 × 106
0
2 × 106
4 × 106
Figure 3. Illustration of the combination of PA irreplaceability values and differences in NDVI trends inside and outside PAs. Green to yellow areas show high
importance of PAs to maintain the connectivity of species ranges, blue to red colouring depicts low importance. Horizontal colouring represents the rate of vegetation cover change inside versus outside and around PAs. Blue indicates faster vegetation loss inside PAs than outside or a stronger increase outside, red indicates
faster vegetation loss outside or increase inside PAs (isolation). (Online version in colour.)
with respect to human impacts or land-cover change (see [55],
this issue). Moreover, the spatial location of the PAs and their
socio-economic status influence the differences. For example,
many PAs in Tanzania are surrounded by conservancies
( private land managed similarly to PAs) and hence have a
land-cover condition and history comparable to the PA
itself, while such land planning is uncommon in West
Africa. Correspondingly, PAs that are located within an
adjacent PA network, like the W –Arli–Pendjari complex in
Benin, Burkina Faso and Niger, are less likely to report a
change in NDVI differences than isolated PAs such as the
Tai National Park in Ivory Coast.
Our analyses revealed that in past decades 4–5% (depending on the buffer used) of all highly irreplaceable PAs
experienced an increasing difference of NDVI values compared with their surroundings, which can be interpreted as an
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
Acknowledgements. We are very grateful to the anonymous reviewers;
their comments improved the manuscript significantly. This study
has been initialized by the CEOS Biodiversity Workshop at the
German Aerospace Center in Munich, October 2012. We are grateful
to N. Pettorelli and W. Turner for constructive comments on
the analysis.
Funding statement. We are grateful to GEO-D and DLR for financial
support of this workshop.
Endnote
1
IUCN spatial data download: http://www.iucnredlist.org/technicaldocuments/spatial-data (accessed 2012).
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Fahrig L, Merriam G. 1985 Habitat patch
connectivity and population survival. Ecology 66,
1762–1768. (doi:10.2307/2937372)
Fahrig L, Merriam G. 1994 Conservation of
fragmented populations. Conserv. Biol. 8, 50 –59.
(doi:10.1046/j.1523-1739.1994.08010050.x)
Reed DH. 2004 Extinction risk in fragmented
habitats. Anim. Conserv. 7, 181 – 191. (doi:10.
1017/S1367943004001313)
Caughley G. 1994 Directions in conservation biology.
J. Anim. Ecol. 63, 215 –244. (doi:10.2307/5542)
MacArthur RH, Wilson E. 1967 The theory of island
biogeography. Princeton, NJ: Princeton University
Press.
Bruinderink GG, van der Sluis T, Lammertsma D,
Opdam P, Pouwels R. 2003 Designing a coherent
ecological network for large mammals in
northwestern Europe. Conserv. Biol. 17, 549– 557.
(doi:10.1046/j.1523-1739.2003.01137.x)
Brooks T et al. 2002 Habitat loss and extinction in
the hotspots of biodiversity. Conserv. Biol. 16,
909–923. (doi:10.1046/j.1523-1739.2002.00530.x)
Newmark WD, Hough JL. 2000 Conserving wildlife
in Africa: integrated conservation and development
projects and beyond. BioScience 50, 585–592.
(doi:10.1641/0006-3568(2000)050[0585:CWIAIC]
2.0.CO;2)
Newmark WD. 2008 Isolation of African protected
areas. Front. Ecol. Environ. 6, 321 –328. (doi:10.
1890/070003)
Craigie ID, Baillie JEM, Balmford A, Carbone C,
Collen B, Green RE, Hutton JM. 2010 Large mammal
population declines in Africa’s protected areas. Biol.
Conserv. 143, 2221 –2228. (doi:10.1016/j.biocon.
2010.06.007)
Visconti P et al. 2011 Future hotspots of terrestrial
mammal loss. Phil. Trans. R. Soc. B 366,
2693–2702. (doi:10.1098/rstb.2011.0105)
Hanski I. 1998 Metapopulation dynamics. Nature
396, 41 –49. (doi:10.1038/23876)
Hanski I. 1999 Metapopulation ecology. Oxford, UK:
Oxford University Press.
Hanski I, Ovaskainen O. 2000 The metapopulation
capacity of a fragmented landscape. Nature 404,
755–758. (doi:10.1038/35008063)
Schnell JK, Harris GM, Pimm SL, Russell GJ. 2013
Estimating extinction risk with metapopulation
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
models of large-scale fragmenation. Conserv. Biol.
27, 520– 530. (doi:10.1111/cobi.12047)
Kool JT, Moilanen A, Treml EA. 2012 Population
connectivity: recent advances and new perspectives.
Landscape Ecol. 28, 165–185. (doi:10.1007/s10980012-9819-z)
Baranyi G, Saura S, Podani J, Jordan F. 2011
Contribution to habitat patch network connectivity:
redundancy and uniqueness of topological indices.
Ecol. Indic. 11, 1301– 1310. (doi:10.1016/j.ecolind.
2011.02.003)
Bodin Ö, Saura S. 2010 Ranking individual habitat
patches as connectivity providers: integrating
network analysis and patch removal experiments.
Ecol. Model. 221, 2393 –2405. (doi:10.1016/j.
ecolmodel.2010.06.017)
Saura S, Rubio L. 2010 A common currency for
the different ways in which patches and links
can contribute to habitat availability and
connectivity in the landscape. Ecography 33,
523 – 537.
Rubio L, Saura S. 2011 Assessing the importance
of individual habitat patches as irreplaceable
connecting elements: an analysis of simulated
and real landscape data. Ecol. Complexity
11, 28 – 37. (doi:10.1016/j.ecocom.2012.01.003)
Alcaraz-Segura D, Cabello J, Paruelo JM,
Delibes M. 2009 Use of descriptor ecosystem
functioning for monitoring a national park
network: a remote sensing approach. Environ.
Manage. 43, 38 – 48. (doi:10.1007/s00267008-9154-y)
Pettorelli N, Vik JO, Mysterud A, Gaillard J-M, Tucker CJ,
Stenseth NC. 2005 Using the satellite-derived NDVI to
assess ecological responses to environmental change.
Trends Ecol Evol. 20, 503–510. (doi:10.1016/j.tree.
2005.05.011)
Pereira HM, Cooper HD. 2005 Towards the
global monitoring of biodiversity change. Trends
Ecol. Evol. 21, 123 –129. (doi:10.1016/j.tree.
2005.10.015)
Pereira HM et al. 2013 Essential biodiversity
variables. Science 339, 277 – 278. (doi:10.
1126/science.1229931)
Buchanan G, Nelson A, Mayaux P, Hartley A, Donald
PF. 2009 Delivering a global, terrestrial, biodiversity
observation system through remote sensing. Conserv.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
Biol. 23, 499 –502. (doi:10.1111/j.1523-1739.2008.
01083.x)
Mayaux P et al. 2013 State and evolution of the
African rainforests between 1990 and 2010.
Phil. Trans. R. Soc. B 368, 20120300. (doi:10.1098/
rstb.2012.0300)
Pettorelli N, Ryan S, Mueller T, Bunnefeld N,
Jedrzejewska B, Lima M, Kausrud K. 2011 The
Normalized Difference Vegetation Index (NDVI):
unforeseen success in animal ecology. Clim. Res. 46,
15– 27. (doi:10.3354/cr00936)
Wiens J, Sutter R, Anderson M, Blanchard J, Barnett
A, Aguilar-Amuchastegui N, Avery C, Laine S. 2009
Selecting and conserving lands for biodiversity: the
role of remote sensing. Remote Sens. Environ. 113,
1370– 1381. (doi:10.1016/j.rse.2008.06.020)
deFries R, Hansen A, Newton AC, Hansen MC. 2005
Increasing isolation of protected areas in tropical
forests over the past twenty years. Ecol. Appl. 15,
19– 26. (doi:10.1890/03-5258)
deFries R, Hansen A, Turner BL, Reid R, Liu J. 2007
Land use change around protected areas:
management to balance human needs and
ecological function. Ecol. Appl. 17, 1031–1038.
(doi:10.1890/05-1111)
Nagendra H, Rocchini D. 2008 High resolution
satellite imagery for tropical biodiversity studies: the
devil is in the detail. Biodivers. Conserv. 17,
3431– 3442. (doi:10.1007/s10531-008-9479-0)
Nagendra H, Lucas R, Honrado JP, Jongman RHG,
Tarantino C, Adamo M, Mariota P. 2013 Remote
sensing for conservation monitoring: assessing
protected areas, habitat extent, habitat condition,
species diversity, and threats. Ecol. Indic. 33,
45– 59. (doi:10.1016/j.ecolind.2012.09.014)
Rondinini C et al. 2011 Global habitat suitability
models of terrestrial mammals. Phil. Trans. R. Soc. B
366, 2633– 2641. (doi:10.1098/rstb.2011.0113)
Santini L, Di Marco M, Visconti P, Baisero D, Boitani L,
Rondinini C. 2013 Ecological correlates of dispersal
distance in terrestrial mammals. Hystrix 24, 1– 6.
IUCN and UNEP. 2010 The world database on
protected areas (WDPA). Cambridge, UK: UNEPWCMC. See http://www.protectedplanet.net.
Tucker CJ, Pinzon JE, Brown ME, Slayback D, Pak
EW, Mahoney R, Vermote E, El Saleous N. 2005 An
extended AVHRR 8 km NDVI data set compatible
Phil. Trans. R. Soc. B 369: 20130193
References
7
rstb.royalsocietypublishing.org
increasing isolation of the natural habitats of these PAs from
those of their surroundings. The interaction of habitat status
and anthropogenic-induced changes on mammal populations,
which is also discussed in detail in [66] and [67], outlines the
importance of strengthening the collective contribution for
biodiversity conservation. The combination of ecological and
remotely sensed information proved to be a highly valuable
tool. With increasing access and availability of remote sensing
data, it is expected to provide further relevant information
[68], with particular importance for the development of
standardized measures for biodiversity monitoring [24].
Downloaded from http://rstb.royalsocietypublishing.org/ on February 18, 2015
38.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
planning for African vertebrates. Conserv. Biol. 19,
1488 – 1497. (doi:10.1111/j.1523-1739.2005.
00204.x)
Pressey RL, Johnson IR, Wilson PD. 1994 Shades
of irreplaceability: towards a measure of the
contribution of sites to a reservation goal.
Biodivers. Conserv. 3, 242– 262. (doi:10.1007/
BF00055941)
Ferrier S, Pressey RL, Barret TW. 2000 A new
predictor of the irreplaceability of areas for
achieving a conservation goal, its application to
real-world planning, and a research agenda for
further refinement. Biol. Conserv. 93, 303 –325.
(doi:10.1016/S0006-3207(99)00149-4)
Margules CR, Pressey RL. 2000 Systematic
conservation planning. Nature 405, 243 –253.
(doi:10.1038/35012251)
Singh NJ, Milner-Gulland EJ. 2011 Conserving a
moving target: planning protection for a
migratory species as its distribution changes.
J. Appl. Ecol. 48, 35 – 46. (doi:10.1111/j.1365-2664.
2010.01905.x)
Thirgood S et al. 2004 Can parks protect migratory
ungulates? The case of the Serengeti wildebeest.
Anim. Conserv. 7, 113 –120. (doi:10.1017/
S1367943004001404)
Wikelski M, Kays RW, Kasdin NJ, Thorup K, Smith
JA, Swenson Jr GW. 2007 Going wild: what a global
small-animal tracking system could do for
experimental biologists. J. Exp. Biol. 210, 181 –186.
(doi:10.1242/jeb.02629)
Di Marco M, Buchanan GM, Szantoi Z, Holmgren M,
Grottolo Marasini G, Gross D, Tranquilli S, Boitani L,
Rondinini C. 2014 Drivers of extinction risk in
African mammals: the interplay of distribution state,
human pressure, conservation response and species
biology. Phil. Trans. R. Soc. B 369, 20130198.
(doi:10.1098/rstb.2013.0198)
Le Saout S et al. 2013 Protected areas and effective
biodiversity conservation. Science 342, 803 –805.
(doi:10.1126/science.1239268)
Turner W. 2013 Satellites: make data freely
accessible. Nature 498, 37. (doi:10.1038/498037c)
8
Phil. Trans. R. Soc. B 369: 20130193
39.
50.
planning. Biol. Conserv. 144, 1980 –1988. (doi:10.
1016/j.biocon.2011.04.018)
Cabeza M. 2003 Habitat loss and connectivity of
reserve networks in probability approaches to
reserve design. Ecol. Lett. 6, 665–672. (doi:10.
1046/j.1461-0248.2003.00475.x)
Hay GJ, Marceau DJ, Dube P, Bouchard A. 2001
A multiscale framework for landscape analysis:
object-specific analysis and upscaling. Landscape
Ecol. 6, 471–490. (doi:10.1023/A:1013101931793)
Blaschke T. 2010 Object based image analysis for
remote sensing. ISPRS J. Photogramm. 65, 2–16.
(doi:10.1016/j.isprsjprs.2009.06.004)
Karl JW, Maurer BA. 2010 Multivariate correlations
between imagery and field measurements across
scales: comparing pixel aggregation and image
segmentation. Landscape Ecol. 24, 591–605.
(doi:10.1007/s10980-009-9439-4)
Geldmann J, Barnes M, Coad L, Craigie ID, Hockings
M, Burgess ND. 2013 Effectiveness of terrestrial
protected areas in reducing habitat loss and
population declines. Biol. Conserv. 161, 230 –238.
(doi:10.1016/j.biocon.2013.02.018)
Duncan C, Kretz D, Wegmann M, Rabeil T, Pettorelli
N. 2014 Oil in the Sahara: mapping anthropogenic
threats to Saharan biodiversity from space. Phil.
Trans. R. Soc. B 369, 20130191. (doi:10.1098/
rstb.2013.0191)
Schipper J et al. 2008 The status of the world’s land
and marine mammals: diversity, threat, and
knowledge. Science 322, 225 –230. (doi:10.1126/
science.1165115)
Herold M, Mayaux P, Woodcock CE, Baccini A, Schmullius
C. 2008 Some challenges in global land cover mapping:
an assessment of agreement and accuracy in existing
1 km data sets. Remote Sens. Environ. 112, 2538–2556.
(doi:10.1016/j.rse.2007.11.013)
Asner GP, Levick SR. 2012 Landscape-scale effects of
herbivores on treefall in African savannas. Ecol. Lett.
15, 1211–1217. (doi:10.1111/j.1461-0248.2012.
01842.x)
Rondinini C, Stuart S, Boitani L. 2005 Habitat
suitability models and the shortfall in conservation
rstb.royalsocietypublishing.org
37.
with MODIS and SPOT vegetation NDVI data.
Int. J. Remote Sens. 26, 4485–5598. (doi:10.
1080/01431160500168686)
R Core Team. 2012 R: a language and environment
for statistical computing. Vienna, Austria: R
Foundation for Statistical Computing. See http://
www.R-project.org.
GRASS Development Team. 2012 Geographic
resources analysis support system (GRASS) software,
v. 6.4.2. Open Source Geospatial Foundation. See
http://grass.osgeo.org.
Neteler M, Bowman MH, Landa M, Metz M. 2012
GRASS GIS: a multi-purpose open source GIS.
Environ. Model. Softw. 31, 124–130. (doi:10.1016/j.
envsoft.2011.11.014)
Bivand R. 2013 spgrass6: interface between GRASS 6
and R. See http://cran.at.r-project.org/web/
packages/spgrass6/index.html.
Hijmans RJ. 2013 raster: geographic data analysis
and modeling. See http://cran.r-project.org/web/
packages/raster/index.html.
Bivand R, Keitt T, Rowlingson B. 2013 rgdal:
bindings for the geospatial data abstraction library.
See http://cran.r-project.org/web/packages/rgdal/
index.html.
Bivand R, Rundel C. 2013 rgeos: interface to
Geometry Engine – Open Source (GEOS). See http://
cran.r-project.org/web/packages/rgeos/index.html.
Wickham H. 2007 Reshaping data with rhe reshape
package. J. Stat. Softw. 21, 1–20.
Lenth RV. 2009 Response-surface methods in R,
using rsm. J. Stat. Softw. 32, 1 –17.
Wegmann M. 2009 r.pi software manual. See http://
svn.osgeo.org/grass/grass-addons/grass6/raster/r.pi.
Woodcock CE, Strahler AH. 1987 The factor of scale
in remote sensing. Remote Sens. Environ. 21,
311–332. (doi:10.1016/0034-4257(87)90015-0)
Ricotta C, Avena GC, Volpe F. 1999 The influence of
principal component analysis on the spatial structure
of a multispectral dataset. Int. J. Remote Sens. 20,
3367–3376. (doi:10.1080/014311699213712)
De Ornellas P, Milner-Gulland EJ, Nicolson E. 2011
The impact of data realities on conservation