Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Identifying corridors for Lemur dispersal, for two future climate change scenarios.
A. Cameron*1, S. Phillips2, C. Golden1, R. J. Hijmans3, E. Louis Jr. 4 , P. C. Wright5, C.
Kremen1,
Corresponding author*: acameron@nature.berkeley.edu
1
Department of Environmental Sciences, Policy and Management
137 Mulford Hall, University of California, Berkeley, CA 94720-3114, USA
2
AT&T Labs-Research, 180 Park Avenue, Florham Park, NJ 07932, U.S.A.
4
Center for Conservation and Research, Henry Doorly Zoo, Omaha, NE, 68107, U.S.A.
5
Department of Anthropology, State University of New York, Stony Brook, NY, 11794,
U.S.A.
1
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
ABSTRACT
A key consideration in planning climate change is whether species can track their
climate niche as climatic conditions change.
Their ability to do so depends both on the
spatial distribution of suitable habitat over time, and their dispersal abilities. For 23
lemur species in Madagascar, we created niche-based distribution models based on
locality points and climate surfaces in 2000, and then projected these models for
conditions in 2020, 2050 and 2080, based on two different climate change scenarios,
modeled using three different climate change models. We then defined suitable habitat
within the suitable climate space as all grid cells containing > 30% forest cover. After
identifying regions of suitable habitat for each time slice, we used a graph-theoretic
approach to assess each species’ ability to move between patches of suitable habitat in
different time slices. We assumed that species could only move through grid cells with
at least 15% forest cover, and assessed their ability to reach suitable habitat patches
between time slices based on species-specific estimates of annual dispersal rates
(diameter of home range/age at first reproduction). These results are preliminary
because we will be updating the species models with additional occurrence records, and
reducing the interval between climate slices. Within these preliminary results, however,
all but two species are predicted to have some suitable habitat in Madagascar in all four
time slices. Factoring in dispersal ability shows, however, that some species may not be
able to track their climate niches to reach these suitable areas. Specifically, assuming
that current forest cover is maintained, one species would not be able to reach areas of
suitable habitat. Assuming that no forest cover remains outside of protected areas, four
additional species would not be able to reach areas of suitable habitat. However, if all
current forests could be protected, the remaining 20 species would either remain stable
of expand with climate change – providing additional motivation for further forest
protection. The next step is to conduct an “optimal maximum flow analysis” (a
recently-developed technique) to identify an efficient configuration of “climate change
corridors” to allow species to track their preferred climate conditions, and to identify
the set of areas that would potentially protect the most vulnerable species (e.g the seven
species currently predicted not able to reach suitable areas under the scenario of forest
loss outside of PAs) and also reduce the vulnerability of other species, without requiring
protection of all remaining forests.
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
INTRODUCTION
The work presented here is a feasibility study. The overall goal, which will be addressed fully
in our final analysis, is to design dispersal corridors connecting suitable conditions for each
lemur species between time periods, subject to each species' limited dispersal ability, and
minimizing the total area requiring additional protection. While the spatial connectivity of
protected areas has received much attention we wish to identify practical methods to measure
and optimize connectivity in the conservation landscape through space and time, accounting
for the potential needs of multiple species.
In this preliminary paper we use a leading method (Maxent) to model species distributions.
We then use a new method for predicting the potential dispersal, or movement, of individual
species, from areas of currently suitable climate to areas that are predicted to have suitable
climate in the future. We present summary statistics across all lemur species and also show
composite maps showing potential lemur species richness under two different climate change
scenarios, modeled using three different climate change models. These final species richness
illustrations provide preliminary assessments of where the majority of lemur species may be
located under future conditions.
METHODS
Species locality data
The majority of the species locality data was derived from museum specimens, field capture
data, or field observations, collected by the co-authors. Additional published locality data was
also captured from Wilme et al. (2007) and from a GERP report (2005 – for the Makira
region). The taxonomy was standardised by each data provider, in accordance with the most
updated taxonomies available. The coordinates vary in precision from contemporary GPSderived to historical post facto georeferenced data. If estimates of geographical error were
available, then records with greater than 2.5km error were discarded. A total of 31 lemur
species were considered to have sufficiently accurate taxonomic and geographic records to
generate reliable species distribution models; while this study is limited to 24 of these species
for now, but the final study will include all 31 (Appendix 1).
3
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Species distribution modelling
Species distribution models were produced using Maxent (Phillips, 2006).
Climate variables
Climate variables representing the climate in two periods ~1950 and ~2000 were obtained
directly from the worldclim website (Hijmans 2005, www.worldclim.org ). Each set of
climate layers include the following variables:
1) RealMat = Mean annual temperature (°C)
2) RealMar = Mean annual rainfall (sum of mean monthly rainfall) (mm)
3) MinTemp = The minimum temperature of the coldest month (°C)
4) MaxTemp = Maximum temperature of the hottest month (°C)
5) MinPrec = Minimum precipitation of the driest month (mm)
6) MaxPrec = Maximum precipitation of the wettest month (mm)
7) Etptotal = evaportanspiration total for 12 months added together (mm)
8) Wbyear = Water balance for the year (mm) (wbyear = realmar – etptotal)
9) Wbpos = Number of months with a positive water balance (integer value between 1 and
12)
Species locality data preparation
We used the Samples With Data (SWD) format for data input to Maxent. Records from prior
to 1975 were assigned data from the 1950 climate layers; records from 1975 to the present
were assigned data from the 2000 climate layers, as this best approximates the climate
conditions experienced by the animal at the time of the data collection event. After
referencing point locality data to a 30 arc-second grid (0.86 km2 at the Equator), duplicate
spatial points within each time period (pre 1950 and post 1950) were removed, thus retaining
temporal duplicates.
Selection of background points
Sampling bias was corrected for by constraining the selection of background points from only
those grid cells that have been sampled for Lemurs - not using the default setting in Maxent
where they are drawn randomly from across the whole region. This necessitates the use of a
Samples With Data (SWD) input file for the background points, in this case each sampled
4
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
point selected as a background point was attributed environmental data from the current, or
2000, climate layers (backgroundLEMURmask2007.csv). The SWD input file allows an
unbiased model to be constructed, for the sampled region.
Projecting species distributions to future climate change scenarios
Matching climate layers for three future time periods

2020

2050

2080
were prepared by Robert Hijmans, from three different Coupled Atmospheric-Oceanic
General Circulation Models:

CCCM (Canadian Center for Climate Change Modelling)

CSIRO (Commonwealth Scientific and Industrial Research Organisation, Australia)

HadCM3 (Hadley Centre, United Kingdom),
Robert used each of these three Coupled Atmospheric-Oceanic General Circulation Models
to create matching sets of future layers for two different IPCC (Intergovernmental Panel on
Climate Change) response scenarios:

A2a

B2a
This resulted in a total of six sets of future climate layers for each of the three future time
slices. Maxent was used to project the individual species distribution models to each of these
potential future conditions.
Post-modeling treatments of Maxent climate suitablility models
Maxent produces a continuous prediction for each species, essentially a map with values
ranging from zero to one. We required binary maps as inputs (presence/absence or 1/0) for
the dispersal analysis, and this requires some post-modelling manipulation of the Maxent
outputs. First we thresholded the continuous predictions (Fig 1a) for each species using
species-specific threshold values. This produces a binary, or presence/absence model (Fig
1b). These presence/absence maps represent all areas with potentially suitable climate
conditions for the species. We used the 10 percentile training presence value as the threshold,
which means that the ten percent of each species records with the lowest predicted model
5
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
values will fall into the absence (white) regions of the thresholded model, and the presence
regions (green) will encompass the other 90% of the distribution records, which have the
highest model values.
However, the models for 2000 often predict patches of potential “presence” in bio-geographic
regions which have suitable climate conditions, but which the species has been unable to
reach. Because we require our starting point to reflect the current distribution of the species
we removed areas predicted as suitable (green) if they did not contain any presence records.
We call our method for removing regions of bio-geographic over-prediction “pruning” and
have illustrated the two steps involved in figure 1, parts c and d below.
Because there may be areas of highly suitable climate, in between the occupied patches, and
it is reasonable to assume that the species has been able to disperse to these patches, we place
a minimum convex hull around the occupied patches (Fig 1c), and eliminate only unoccupied patches outside of that hull (Fig 1d).
Fig1. Applying a threshold (a,b), and “pruning” (b,c,d) regions of biogeographic
overprediction from current models (c,d) to the 2000 model for Varecia variegate.
a. Maxent: Logistic output
b. Thresholded Maxent
output
c. Minimum convex hull
around occupied areas
d. Eliminate un-occupied
areas outside minimum
convex hull
Note that whilst step a to b was applied to all current and future models we did not apply
steps b to c, or c to d, to the future models, as the aim of the next part of our analysis is to use
species specific dispersal parameters to estimate whether each species will be able to reach its
potentially suitable climate conditions in the future. In other words, the aim of the exercise is
6
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
first to identify new areas of suitable climate created by climate change that the species has
perhaps not yet reached, and then ask, based on our knowledge of its potential for dispersal,
whether it is feasible for the species to reach these new areas. Simultaneously, the first step
also reveals which areas of suitable climate in one time slice become unsuitable in the next.
Because lemurs are highly forest dependent, forest cover almost certainly restricts the
distribution of lemur species within the potentially suitable climate space. We therefore
mapped suitable habitat within the suitable climate zones as the areas within these zones for
each time slice that contain > 30% forest cover per grid cell.
Dispersal Modelling
To obtain species-specific estimates of dispersal ability, we collated published and
unpublished data on home range size and age at first reproduction for as many of the 31
species as possible. If no data was available for a given species, we used data from its closest
relative (4 species were proxied by another species within the same genus; 5 sub-species were
proxied by another sub-species, or an average across sub-species, within the same species).
To estimate an annual dispersal rate from these data, we assumed that dispersing individuals
would travel a distance equivalent to the diameter of their home range, and we calculated
these diameters assuming that home ranges are circular. We assumed that dispersal events
would occur once in an animal’s lifetime and prior to becoming reproductive. We therefore
estimated the “annual” dispersal distance as the total distance traveled (home range
diameter)/age at first reproduction. Since home range and age at first reproduction data were
often reported as a range for a given species (within or across studies), we calculated a
minimum and a maximum dispersal rate. Whilst we restrict species distributions to
climatically suitable areas with more than 30% forest cover we apply a rule that species are
able to disperse through climatically suitable regions with more than 15% forest cover.
We consider two forest cover scenarios, one where forest cover is held constant at the
coverage present in 2000, and the other where forest cover is removed outside the protected
areas. Forest cover was summarized as % forest cover per 30 arc second grid cell, from forest
cover change maps originally produced at 28m grid cell resolution by Conservation
International (Harper et al. in press).
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Graph theory was applied to determine the potential of each species to disperse from the
modeled distribution in 2000, through the available time slices (2020, then 2050), to end with
2080. Whilst “suitable habitat” for persistence in any time period was determined by
intersecting the species climate model with grid cells containing >=30% forest cover, our
dispersal rule allowed dispersal through grid cells with >=15% forest cover. Potential
dispersal distance per step was determined per species using the individual rates described
above.
RESULTS
Suitable habitat at each time slice
Figure 2 shows the suitable habitat within the suitable climate conditions at each time slice
for Varecia variegata (red), defined as areas with > 30% forest cover per grid cell. Note
firstly that the area of suitable habitat for this species is actually predicted to increase with
climate change, due to its expanded climate suitability (Fig 1). Note also that for this
analysis, we assume no change in forest cover in the future.
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Fig 2. Modelled species distribution for Varecia variegate (a-d) and Eulemur rubriventer (eh), for the CCCM climate model for the A1a scenario, for four time slices. Panel 2a shows
the density of forest cover (as percent forest cover per grid cell) within the pruned 2000
model of climatic suitability for Varecia variegata.. Panels 2b, 2c and 2d show the density of
forest cover (as percent forest cover per grid cell) within the future models (unpruned) of
climatic suitability for Varecia variegata. Red = Suitable climate conditions, with >=30%
forest cover; Light green = Suitable climate conditions, with >=15% and <=30% forest
cover; Pale blue = Suitable climate conditions, with <=15% Blue = unsuitable
a.
e.
2000
b.
f.
2020
c.
g.
2050
d.
2080
h.
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Dispersal between suitable habitat areas in different time slices
By modeling the dispersal process, we next calculate the ability of each species to
disperse from patches of suitable habitat in one time slice to patches of habitat in adjoining
time slices (“reachability model”). Fig 3 shows for one expanding and one contracting
species (Varecia variegata and Eulemur rubriventer, respectively) the regions of suitable
habitat that persist from 2000 all of the way to 2080 (red regions that are consistent between
panels), as well as regions of expansion (red areas that were not previously red), regions of
future contraction (orange areas), and regions of suitable climate that are never reached
(green areas). The blue areas are the currently protected areas, and underlay the other colors.
By comparing back and forth between the habitat suitability maps in Fig 2 and Fig 3, which
takes into account not only habitat suitability but dispersal, it becomes clear that the region
that can actually be occupied by a dispersing species can be smaller than the available
suitable habitat (e.g. Eulemur rubriventer).
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Fig 3. Reachability model results for Varecia variegata (a – d) and Eulemur rubriventer (e –
h). Forest cover is held constant at 2000 cover: Areas of currently suitable habitat (a, e), and
potentially suitable habitat under potential climate change (b-d; f-h) that could be reached by
dispersal. This particular set of future models are based on the A1a climate change scenario
produced from the CCCM climate model for three time slices (2020, 2050, 2080). Red:
suitable habitat cells that either persist through to 2080 or can reach new areas of habitat
suitability present in 2080; Orange: suitable cells that are reachable from the previous time
slice, but that will not permit dispersal to suitable habitat areas all the way through to 2080;
Green: suitable habitat cells that can't be reached from the previous time slice; Blue:
protected areas (sometimes hidden under red/orange/green cells) – blue areas do not reflect
the species distribution.
a.
2000
b.
2020
c.
2050
d.
2080
0
e.
f.
g.
h.
11
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Summary Statistics
Under the scenario of retaining 2000 forest cover and IPCC climate change climate scenario
1a, three of the 24 species would not be able to reach any suitable habitat by 2080 (Table 1).
For two of these three species, this is because there is no suitable habitat predicted to occur
by 2080. On a brighter note, distributions are predicted to expand for 9 species, to remain
roughly constant for 9 others, and to contract for 1 other.
However, the scenario of no further forest loss is of course unrealistic, and the situation
changes considerably if we assume that by 2020, no forest occurs outside of what is currently
protected. In this case seven species can no longer reach suitable habitat by 2080 (Table 1),
and most of the remaining species will inevitably have lost large portions of suitable habitat.
For the seven species in Table 1, most are primarily rain forest species or species with small
ranges most likely to be affected by climate change. With the exception of Propithecus
species, the other species are likely to be tolerant of secondary habitat which could mitigate
their response to climate change.
Table 1. The species predicted not to reach suitable habitat patches in 2080, first under the
assumption that all current forest cover is protected, and second, assuming that all forests
outside of current protected areas will be gone by 2020.
Genus
Avahi
Eulemur
Eulemur
Propithecus
Propithecus
Propithecus
Lepilemur
Reach suitable habitat in 2080
Species
2000 forest cover: no change Protected area forests only
laniger
N
N
macaco flavifrons
Y
N
rubriventer
N
N
diadema perrieri
N
N
tattersali
Y
N
verreauxi coquerli
Y
N
septentrionalis
Y
N
To produce an overall assessment of the potential effects of climate change on lemur
distributions we produce a composite surface of the climatically suitable areas in 2080 with
more than 30% forest cover, that we predict can be reached by each lemur species (e.g. the
red areas in fig 3) by 2080 (Figure 4), under two scenarios of forest cover. These are maps of
potential lemur species richness for 2080 that take into account not only climate and habitat
suitability, but species’ dispersal abilities (e.g. reachability models). By comparing species
richness in panels a (current climate and forest) with b (future climate but forest is maintained
12
Lemur corridors: Preliminary results and feasibility study. Cameron et al.
at 2000 levels), one can observe that some species rich areas are predicted to become less rich
(e.g. Masoala), while other areas become richer (eastern central Madagascar) – in other
words, the hotspot of species richness moves from the North to central Madagascar.
Combining the effects of climate change and forest cover loss (under the scenario of only
protected areas maintaining forest cover) dramatically reduces species richness throughout
Madagascar. Figure 5 presents the differences among climate scenarios and models using the
composite surfaces for 2080 under the scenario that forest cover remains at 2000 levels.
Figure 4. a. Species richness generated from species distribution models with climate and
forest cover in 2000. b. Species richness generated from reachability models for climatesuitable areas in 2080 under climate scenario A1a, assuming that forest cover doesn’t change
from its 2000 levels. C. Species richness generated from reachability models for climatesuitable areas in 2080, assuming that no forest cover outside current protected area persists
past 2020. Color scale, red = 11 species, blue = 0 species.
a.
b.
c.
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Fig 5. Lemur species richness for 2080 for two IPCC models (2 rows) and 3 future climate
change models (3 columns), assuming forest cover remains constant at 2000 cover.
HadCM3
CCCM
CSIRO
A2a
A2b
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
DISCUSSION
While these results do not yet identify “corridors”, they identify the pattern in individual
species distributions and in species richness expected if all forest is retained (best case
scenario) or all forest cover within protected areas is retained (worst case scenario). The
importance of forest corridors for maintaining lemur species richness and for conserving
selected species (Table 1) is evident – without protecting these forest corridors, impacts of
climate change on lemur distributions will be far more severe. With such protection, areas of
peak species richness may shift. An additional interesting finding is that, assuming forest
protection, a number of species have stable distributions (are resilient to climate change), and
some species distributions are predicted to expand with climate change. Considerable
additional work is needed to refine and finalize these results in order to make
recommendations for conservation planning. Some initial models could be improved by
obtaining additional distribution points. The remaining steps in this analysis are outlined
below. The final product of this analysis would use optimization techniques to identify the
set of corridors needed to be added to the existing reserve network, in order to permit species
to reach new areas of suitable habitat. These results can also be used to aid in identifying
interventions for individual species. When the results are finalized, the full set of species
models from 2000-2080 will be made available. In the meantime, please contact Alison
Cameron (acameron@nature.berkeley.edu) with specific information requests.
NEXT STEPS
1) Add more species locality data to the species distribution modelling stage, to improve the
Maxent model outputs.
2) Obtain climate change layers for equal intervals (possibly at 4 year intervals matching the
age at first reproduction and thus dispersal for many lemurs) rather than the unequal intervals
that we have used in this feasibility study (2000, 2020, 2050, 2080).
3) Compare the effects of conservative and optimistic assumptions about species dispersal
rates.
4) Compare the effects of applying a higher requirement for forest cover in the dispersal
model.
5) Set targets for minimum representation to be maintained through time per species
6) Conduct maximum flow analysis that identifies alternative dispersal chains.
7) Conduct optimization analysis to identify efficient configuration of corridors.
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
Author contributions
AC, CG, CK and SP developed the analytical plan for this work. EL and PCW contributed
lemur locality data. RH provided climate layers for future scenario modelling. AC conducted
the Maxent modelling. CG and CK evaluated the species distribution models and collated
dispersal and life history data. SP conducted the species dispersal analyses. AC and CK
drafted the manuscript.
Acknowledgements
This study would not have been possible without the collaboration of a large number of lemur
biologists, and many people must be thanked for their efforts in the field. Felix Ratelolahy,
Mitch Irwin, Steig Johnson, Chia Tan, Summer Arrigo-Nelson, Justin Solo, Jonah
Ratsimbazafy, Shawn Lehman, Mireya Mayor, Toni Lyn Morelli, Jean Luc Raharison,
Volontiana Razafindratsita and Matthew Banks assisted with lemur surveys. Erik Patel,
Christophe Schwitzer, Ute Radespiel, David Meyers, Deborah Overdorff, Giuseppe Donati,
Richard Lawler, and Rebecca Lewis provided lemur life history and dispersal data, which
was used in estimating species specific dispersal rates.
References cited
Groupes d'Etudes de Recherches sur les Primates de Madagascar (GERP), “Annual report to
the Makira Project” (2005).
G. Harper, M. Steininger, C. Tucker, D. Juhn, F. Hawkins, Environmental Conservation
(2008, in press).
R. J. Hijmans, S. E. Cameron, J. L. Parra, P. G. Jones, A. Jarvis, International Journal of
Climatology 25, 1965 (Dec, 2005).
S. J. Phillips, R. P. Anderson, R. E. Schapire, Ecological Modelling 190, 231 (Jan, 2006).
L. Wilme, S. M. Goodman, J. U. Ganzhorn, Science 312, 1063 (May, 2006).
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Lemur corridors: Preliminary results and feasibility study. Cameron et al.
APPENDIX 1.
List of species included.
Additional species that will be included.
Avahi laniger
Cheirogaleus major
Eulemur coronatus
Daubentonia madagascariensis
Eulemur fulvus albifrons
Eulemur albocollaris
Eulemur collaris
Eulemur fulvus fulvus
Eulemur fulvus sanfordi
Eulemur fulvus rufus
Eulemur macaco flavifrons
Hapalemur aureus
Eulemur macaco macaco
Hapalemur griseus
Eulemur rubriventer
Microcebus rufus
Indri indri
Lemur catta
Lepilemur septentrionalis
Microcebus griseorufus
Propithecus diadema candidus
Propithecus diadema diadema
Propithecus diadema edwardsi
Propithecus diadema perrieri
Propithecus tattersalli
Propithecus verreauxi coquereli
Propithecus verreauxi coronatus
Propithecus verreauxi deckenii
Propithecus verreauxi verreauxi
Varecia rubra
Varecia variegate
17