Saving the Iberian lynx from extinction requires climate adaptation
1
Supplementary Methods
Below is a description of coupled niche-population models1 used to integrate Iberian lynx (Lynx pardinus)
range dynamics with that of European rabbit (Oryctolagus cuniculus) population ecology and climate change, to
better understand future extinction threats to Iberian lynx and potential management interventions.
Spatial Data and Ecological Niche Modelling
Location Data
Geographically-referenced occurrence records of the Iberian lynx and European rabbit were obtained from
multiple sources, including unpublished Spanish and Portuguese governmental databases (see below). Location
data, including historical records collected throughout the second half of the 20th century, were used for fitting
ecological niche models, and to characterize the potential future distributions of Iberian lynx and European
rabbits. We did this to more closely approximate climate-physiological limits of the species thus reducing
potential biases in the characterization of the climatic niche of the species2. Such biases are bound to exist
because non-climate factors largely influenced population contraction since 1950, at least for Iberian lynx3. It
follows that ecological niche models (see below) assume that species distributions are in equilibrium with
climate, and departures from this assumption can influence modelled responses to climate change4. This
approach strengthened future forecasts of carrying capacity in our models (see below), but prevented us from
using past location records (i.e., from 1950) to retrospectively validate dynamic mechanisms of our models.
This trade-off was sensible because important spatiotemporal drivers of non-natural mortality in the latter half
of the 20th century are not available; and strong conservation efforts have dampened their influence and
relevance for the future5.
Lynx: Occurrence maps were constructed using records provided by the Subdirección General de Biodiversidad
(Spain) and Instituto da Conservação da Natureza e da Biodiversidade (Portugal). The records for Spain were
Adapted conservation measures are required tosave the Iberian lynx in a changing climate
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based primarily on 2240 presence records constructed using three primary sources of information6-8: (i)
georeferenced records from the scientific and game literature, inventories of hunting trophies, zoological
collections, and private archives; (ii) postal surveys of rangers, hunting associations, nature conservation
associations, and taxidermists, and holders of hunting rights (1310 responses8); (iii) on-ground information,
including sight and death reports from personal interviews with people who had a high probability of detecting
lynx (shepherds, hunters, farmers) (2500 interviews); inventories of physical remains (such as pelts or skulls9)
and a first-hand assessment of habitat and land-uses6,10. Positive reports obtained by postal surveys were
confirmed using on-ground information. The reliability of people interviewed in the field was assessed, and
unreliable reports (40% of total interviews) discarded8. The official maps provided by Subdirección General de
Biodiversidad included some additional records adjacent to occupied cells in the 1950 range map for Spain8.
The occurrence records for lynx in Portugal were obtained using similar approaches and criteria as for Spain11-
13. The quality of the absence records are supported, albeit indirectly, by recent extensive14 and intensive studies
(reviewed by3,5,15) that failed to find past or present lynx occurrences in localities reported as empty in the maps
by Rodríguez & Delibes6-8.
Rabbit: The occurrence records for rabbits were also the result of systematic surveys across Spain16,17 and
Portugal 18,19.Occurrence records came from different published and unpublished sources e.g., volunteer reports
by members of the Spanish Mammal Society20. In Portugal the occurrence records came from published
literature18 and government reports19.”
Spatial Data
Climate data: We identified annual rainfall and mean temperature of the hottest and coolest months (July and
January, respectively) as being likely to have the largest potential climate influence on Iberian lynx and
European rabbit abundance. These variables are often seen as controlling physiological processes limiting the
spatial distribution of species21 and have been shown to effectively discriminate between principal
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environments in the Iberian Peninsula22, and elsewhere in Europe23. The same variables were used to model the
climate suitability of rabbits in Australia with good success24.
Spatial layers describing average July and January daily temperature and annual rainfall (for the period 1961 to
1990) for the Iberian Peninsula were provided by the meteorological institutes of Spain and Portugal.
Specifically, data in Spain were collected from 2713 stations measuring rainfall and 973 stations measuring
temperature, while data from Portugal was obtained from 89 and 51 stations respectively. Interpolation of the
climate variables for the Iberian Peninsula was done using thin plate cokriging 22.
An annual time series of climate change layers for each variable was generated according to two emission
scenarios: a high CO2 concentration stabilising Reference scenario (WRE750)25 and a Policy scenario, assuming
substantive intervention (stabilization at an equivalent CO2 concentration of 450 ppm [MiniCAM LEV1])26. The
procedure consisted of two steps:
1. MAGICC/SCENGEN 5.3 (http://www.cgd.ucar.edu/cas/wigley/magicc), a coupled gas cycle/aerosol/climate
model used in the IPCC Fourth Assessment Report27, was used to generate an annual time series of future
climate anomalies (2000 – 2100) using an ensemble of seven atmosphere-ocean general circulation models
(GCMs)28. Models were chosen according to their superior skill in reproducing seasonal precipitation and
temperature across the Iberian Peninsula. Model performance was assessed following already published
methods29. The seven GCMs were: CGCM3.1 (T47), MIROC3.2 (medres), PCM, UKMO-HadCM3, ECHO-
G, IPSL-CM4, MRI-CGCM2.3.2. Model terminology follows the CMIP3/AR4 multi-model data archive
(http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). GCM skill assessment results can be quite different
depending on the variable considered, the region studied, the month or season examined, or the comparison
metric used29. However, ensemble forecasts that include greater than 5 GCMs, tend to be more robust to
GCM choice30.
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2. Climate anomalies were downscaled to an ecologically relevant spatial resolution (1km x 1km
longitude/latitude), using the “change factor” method, where the low-resolution climate signal (anomaly)
from a GCM is added directly to a high-resolution baseline observed climatology31. Bi-linear interpolation of
the GCM data (2.5 x 2.5 º longitude/latitude) to a resolution of 0.5 x 0.5º longitude/latitude was used to
reduce discontinuities in the perturbed climate at the GCM grid box boundaries29. One advantage of this
method is that, by using only GCM change data, it avoids possible errors due to biases in the GCMs baseline
(present-day) climate. Fine resolution climate projections (1km x 1km longitude/latitude) were also up-
scaled to a grid cell resolution of 10km x 10km longitude/latitude, by aggregating and then averaging across
fine resolution cell values. This was necessary because suitable baseline climate data was not available at the
coarser resolution. No data values (i.e., sea cells) were identified and excluded from the calculation.
Land cover data: The European Environmental Agency CORINE land cover map
(http://www.eea.europa.eu/publications/COR0-landcover) was accessed for the Iberian Peninsula at a grid
resolution of 250 x 250m latitude/longitude (Supplementary Fig. S4). We used our habitat selection models32
and behaviour data33 to identify CORINE land cover types appropriate for breeding habitat for Iberian lynx and
used this information to generate a binary map of Iberian lynx breeding habitat (see Table S2). We also ranked
CORINE land cover types into 4 categories according to how they influence Iberian lynx dispersal (more details
are provided below). This information was used to construct a categorical map describing the influence of land
cover type on Iberian lynx dispersal. Maps for lynx were up-scaled to a grid cell resolution of 1km x 1km
longitude/latitude by taking the fine scale cell value with the largest proportional presence in a coarse resolution
cell.
We identified CORINE land cover types that are suitable or unsuitable for rabbits; and land cover types that are
highly productive or unproductive for rabbits (see Table S3). This information was used to generate binary
maps of suitable habitat for rabbit occupancy and to identify highly productive habitats. Maps for rabbits were
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up-scaled to a grid cell resolution of 10km x 10km latitude/longitude by aggregating fine resolution points and
averaging across these values. This process produced maps that: (i) provide an index of the proportion of the
cell that is habitable for rabbits; and (ii) the proportion of the cell that not only can be occupied, but is
exceptional rabbit habitat. The reason for a adopting a somewhat coarser spatial resolution for rabbit model
inputs is because of current computational limitations on the number of populations that can be modelled using
metapopulation approaches such as coupled niche-population models (< 7000 discrete populations)24.
Ecological Niche Modelling
Ecological niche models (ENM)34 were used to characterize climatic suitability of Iberian grid cells for the
Iberian lynx and for the European rabbit. The rationale is that ENM-modelled suitability provides a surrogate
for species’ carrying capacity, capturing more than the physiological constraints that define presence/absence at
a given location35, which can then be used in demographic models1. Because projections from alternative ENMs
can vary significantly under climate change36, we computed different models and obtained a consensus among
them37,38.
The Bio-ensembles software39 was used to generate ensembles of ENMs for Iberian lynx and rabbits
(separately) using occurrence records since 1950 (see above). The ensemble included projections with seven
methods: bioclimatic prediction and modelling system (BIOCLIM), Mahalanobis distance (MAH), Euclidean
distance (EUC), Generalized Linear Models (GLM), Random Forest (RF), Maximum Entropy (MaxEnt), and
Genetic Algorithm for Rule-set Prediction (GARP). BIOCLIM, MAH, and EUC are fitted only with the
occurrence records of the species, while MaxEnt and GARP also use the background information. GLM and RF
use the background, assuming it represents true absence of the species. By varying the assumptions regarding
absence data in our models we aimed at characterizing the variability in projections accrued from such
assumption. Cross-validation was used to assess the internal consistency of the model predictions40. To do this
we calibrated models for the climate baseline (1961-1990) using an 80% random sample of the initial data and
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evaluated it against the remaining 20% data. The true skill statistic (TSS)41 was used as the fit statistic and was
calculated for each model based on the confusion matrix expressing matches and mismatches of observed and
predicted occurrences in the validation data set. This matrix was computed after using receiver operation
characteristic (ROC) curves to convert continuous predictions into presence-absence, by applying a threshold
that maximizes AUC36. Ecological niche models were then used to forecast annual time-step probability of
occurrence (2000-2100) according to two emission scenarios (see above).
Demographic Model for the European Rabbit
The demographic model of European rabbits (Oryctolagus cuniculus) was implemented in RAMAS Metapop42.
The model was a cellular/lattice type model, consisting of ~ 7,000 cells (10km x 10km longitude/latitude grid
cell resolution). Each grid cell was modelled with a scalar type stochastic model (see “Rabbit demographic
structure”), which is a simple population projection that has three parameters, the finite rate of population
increase “R”, its variance and carrying capacity43. The carrying capacity and initial abundance of rabbits in each
cell was based on the spatial distribution of habitat suitability (see “Rabbit spatial structure”).
Rabbit Demographic Structure
Scalar models are based on time-series data of population sizes and do not include details of population age or
stage structure43. We used a scalar type approach to model rabbit demographics because the movement between
stage classes (kitten, adult) is rapid (being less than six months) and mortality is high. Thus at an annual time
step, stage classes are largely irrelevant. Furthermore, comprehensive estimates of rabbit vital rates and
associated variances based on capture-mark-recapture type approaches are rare, at least in their native range 44.
Density dependence was modelled using the Ricker equation (“scramble competition” function in RAMAS),
whereby as population abundance in a cell increases, the amount of resources per individual decreases. This
type of density dependence requires estimates of carrying capacity (see “Rabbit spatial structure”) and
maximum rate of population growth in the absence of density effects (Rmax). Time series data for O. cuniculus
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was extracted from the Global Population Dynamics Database
(http://www3.imperial.ac.uk/cpb/research/patternsandprocesses/gpdd) and used to calculate Rmax. There were
seven suitable time series available, ranging from 12 to 71 years in length (the median length was 37 years).
The time series abundance data was centred on the mid-19th century (up until 1984). By calculating the
weighted average intercept of Ricker models fitted to each time series we estimated Rmax to be 1.5, which is
similar to that calculated for O. cuniculus for semi-arid landscapes elsewhere45. A standard deviation value of
0.95 around the intrinsic rate of population growth was estimated from the abundance time series data and used
to model population fluctuations driven by environmental stochasticity, including that caused by the viral
disease myxomatosis. Myxomatosis was well established in most populations used in the time series analysis.
In contrast, the time series data predate the emergence and establishment of rabbit haemorrhagic disease (RHD)
in Europe in the 1990’s 46.
Myxomatosis and RHD strongly influence rabbit survival and recruitment44,47. The impact of myxomatosis on
rabbit abundance was modelled implicitly (through environmental stochasticity), while the impact of RHD was
modelled explicitly. The influence of RHD on rabbit mortality was greatest shortly after its arrival on the
Iberian Peninsula, causing an estimated 60% reduction in rabbit population abundance in some areas of Iberia48,
and an additional 22% annual adult mortality49. However, the severity of the RHD impact on rabbit abundance
declines with time following an initial outbreak48,50 and its impact on mortality probably conditions (dampens)
other sources of mortality51. Outbreaks of myxomatosis and RHD tend not to occur simultaneously in the same
year44, allowing its direct influence on survival to be estimated. We model RHD as a catastrophic event that
occurs every two to three years44 causing a 15-25% reduction in abundance49. The assumption here is that when
an outbreak occurs there are a large number of susceptible adults (sero-negative) in the population which results
in additional mortality. Each rabbit population (populated grid cell) was randomly attributed a frequency of
RHD occurrence and severity of RHD impact for each model iteration from within the likely bounds described
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above. We also assume that the timing and frequency of RHD outbreaks is unaffected by global warming,
because there is insufficient data to suggest otherwise.
A local threshold of 5,000 was applied to each cell (equivalent to 50 rabbits per 1 km2). If abundance falls
below this value, the model treated cell abundance as equal to zero. The abundance threshold was based on
observed minimum rabbit densities52 and, in many ways, simulates the impact of an Allee effect, whereby,
below a critical density, the population quickly spirals to extinction53.
Rabbit Spatial Structure
The HS function: Habitat suitability of each lattice cell was defined as the product of 2 components:
1. Climate (output from the ecological niche model) scaled between 0 and 1.
2. Land Cover (scaled between 0 and 1) calculated as the product of the proportion of the cell that is
habitable multiplied by the proportion of the cell that is highly suitable (based on the CORINE criteria;
see Table S3), with a threshold applied (thr). The threshold was developed iteratively, so that the spatial
coverage of occupiable cells maximised the ratio between sensitivity and specificity using field surveys
(see above) and the true skill score metric54. We have used this approach elsewhere55.
Habitat suitability (HS) in the model was defined as
Eq. 1 HS = [Climate]*thr([Land Cover], 0.2)
Carrying capacity: Carrying capacity was calculated iteratively such that: (i) the maximum recorded annual
rabbit abundance per grid-cell did not exceed field based estimates56; (ii) grid-cell abundances (and their spatial
variation) closely approximated on-ground field surveys57 and (iii) estimates of total rabbit abundance for the
entire Iberian Peninsula and sub-regions were sensible. Previously we used this sort of iterative approach to
model the carrying capacity of another lagomorph, Lepus timidus58.
The function used to model cell based carrying capacity was:
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Eq. 2 K0 = ths*30,000
Where the carrying capacity of a cell in a given year is equal to its total habitat suitability (ths) multiplied by 30,000.
Initial abundances: Initial abundance was calculated using a multiplier set at 80% of carrying capacity and a
threshold of 5,000 rabbits per cell. Thus if initial abundance was below 5 000 individuals per cell (i.e., below
annual densities observed in the most marginal habitats: 0.5ha-1), the cell was treated as unoccupied at the initial
time step. We showed elsewhere that setting initial abundance close to carrying capacity is appropriate when a
model burn-in period is then used to generate a stable age distribution and equilibrium initial patch abundance
under the assumption of no future climate change55,59 (see below).
Eq. 3 N0 = thr (ths*24,000, 5,000)
Where the initial abundance of a cell in a given year is equal to its total habitat suitability (ths) multiplied by 24,000 with
a threshold (thr) applied.
A period of 50 years (1,000 permutations), was used to generate a stable age distribution and equilibrium initial
patch abundance under the assumption of no future climate change. Spatial maps of initial abundance were sent
to rabbit experts for external verification based on field observations. There was strong agreement that the maps
provided a good representation of spatial variation in rabbit abundance across the Iberian Peninsula. We have
used a similar approach elsewhere59.
Correlations among grid cells: Environmental variability was correlated between populations depending on
their spatial separation. Thus, individual population fluctuations were highly correlated for closely separated
populations and poorly correlated for disparate populations. Spatial correlations were estimated using historic
weather station data1. Pairwise correlations were calculated for July and January temperature and annual rainfall
for 14 weather stations for the period 1985-2000. All weather stations had complete monthly records and were
chosen to ensure a good spatial representation of Spain. July temperature provided the best distance correlation
relationship.
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An exponential function, P = a.exp(Dc/b), where D is the distance between centroids of population locations, and
a, b and c are function parameters42, was then fitted to the data to calculate the pairwise correlations in vital
rates between populations. Constants were set based on the relationship between distance and July temperature
variation (a = 1, b = 530, c = 1). We used a similar approach for modelling coefficients of correlation for a
lagomorph in the UK and plants in South Africa and Australia1,58,59 .
Dispersal: In this model, dispersal refers to movement between lattice cells. Dispersal was modelled as a
declining function of centre-to-centre distance between cells. A negative exponential function was used to
determine the proportion of each population that disperses between lattice cells at each time step: P = exp(-D/b),
where D is the distance between patch centroids (in km) and b is a constant set at 2.5. When D exceeds 15km (a
maximum dispersal distance: Dmax), P is set to zero42. The dispersal function permitted ~1.8% of the population
to move annually between cells that share a border and ~ 0.30 % of the population moved between cells sharing
a corner. The dispersal rate closely approximated dispersal estimates used elsewhere (1% movement at
distances >3km)60.
Rabbit Model Simulations
100 rabbit demographic models were generated (following steps described above) and each run for a single
iteration under: (i) a high-CO2 concentration stabilising Reference scenario (WRE750); (ii) a Policy scenario
that assumes strong mitigation of greenhouse-gas emissions (LEV1); and (iii) a no-climate change scenario
where temperature and precipitation remains unchanged from the year 2000. Rabbit density in each 10km
x10km cell was then mapped for each year (2000-2100) based on the results from each run of the demographic
model. A total of 30,000 maps of rabbit density were generated (i.e., 100 models x 100 years x 3 climate
scenarios). We mapped output from single model runs to appropriately capture stochasticity in interactions
between rabbit demography, disease and habitat suitability. Maps were disaggregated to a 1km x 1km grid cell
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resolution (using nearest neighbour assignments) so that they aligned with the climate and environmental spatial
inputs used in the lynx model.
Demographic Model for the Iberian Lynx
The demographic model of the Iberian Lynx, like the European Rabbit, was implemented in RAMAS
Metapop42. The general model structure, density dependence, and model parameters were based on Gaona et al.
1998 (see 61), Palomares et al. 2005 & 2012 (see 62,63) and Ferreras et al. 1992 & 2004 (see 64,65). The model is
a spatially structured metapopulation model. Each subpopulation is modelled with a sex-structured, stage-
structured, stochastic model (see “Lynx Demographic Structure”). The spatial structure of the metapopulation
(size and location of subpopulations) is based on the spatial distribution of habitat suitability (see “Lynx Spatial
Structure”). Data used for demographic models are for current refugial populations in the autonomous region of
Andalucía in Spain (Supplementary Fig. S2).
Lynx Demographic Structure
We built an age- and sex-structured model, parameterized according to a pre-reproductive census. Gaona et al.
1998 60 used an age-and-stage structured model for Iberian lynx that included separate stages for reproductive
individuals with and without territories; the same dynamics are modelled here by calculating, at each time step,
the proportion of individuals (and females) with territories and modifying survival and fecundity values in the
stage matrix based on these proportions (see Density dependence below for details). Thus, the survival and
fecundity values for the breeding (reproductive) stages were for individuals with territories; and fecundity does
not incorporate the proportion of females breeding, or the availability of males (see Survival rates and
Fecundity rates below). Total fecundity in a population is modified to incorporate availability of males (see
below), and the density-dependence sub-model makes the modification for individuals without territories. The
density-dependence sub-model also modifies survival and fecundity as functions of rabbit (because the carrying
capacity is based, in part, on rabbit density) and lynx density. In addition, the stage matrix was for a population
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in a protected area. Populations outside protected areas were modelled with relative survival and fecundity
values that reflect additional mortality (see Populations outside protected areas below for further details).
Survival rates: S0 (survival from 0 to 12 months): Palomares et al. 2005 62 estimate 10-month survival as 0.69.
Based on this, we estimate 12-month survival as 0.6406 (= 0.691.2). This value is consistent with 75% cub
survival (from 0-3 months) and 57% survival from birth to dispersal, which happens after the first year. Note
that because the model is parameterized according to a pre-reproductive census, S0 is used in the stage matrix
only for calculating fecundity.
• SF1 (female survival from 12 to 24 months): This period includes dispersal in search of a territory.
Palomares et al. 2005 62 estimate female survival from birth to dispersal as 0.55. Thus survival from 12
months to dispersal can be estimated as 0.55/0.64 = 0.86. Survival during dispersal is 0.7865, so SF1 =
0.86*0.78 = 0.67.
• SM1 (male survival from 12 to 24 months): Palomares et al .2005 62 estimate male survival from birth to
dispersal as 0.59. Thus survival from 12 months to dispersal can be estimated as 0.59/0.64 = 0.92. Survival
during dispersal is 0.39 (see 65), so SM1 = 0.92*0.39 = 0.36.
• SF2 (annual survival of females, from 24 months to 9 years)64: 0.79
• SM2 (annual survival of males, from 24 months to 9 years)64: 0.88
• SF9 (annual survival of females, from 10 years): 0.6
• SM9 (annual survival of males, from 10 years): 0.6
We assigned old lynx a survival value similar to that for juveniles in the pre-dispersal phase (age: 10 months to
2 years)62. This is because territory holders ≥ 9 yr old (and older) are often supplanted by younger adults of the
same sex66. They have a high probability to become floaters without engaging in the long movements and low
survival associated with natal dispersal65.
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Survival could have also potentially been modelled directly as a function of rabbit density, because starvation is
suspected as a source of Iberian lynx mortality65, but there is no established information available to define this
function.
Fecundity: Because the model is parameterized according to a pre-reproductive census, the first age class is 12-
month-old individuals, and fecundity is calculated as the product of average litter size and survival rate from
birth to 12 months, then divided into two for male and female offspring.
• Age of first reproduction = 3 years62
• Age of last reproduction = 9 years62
• Average litter size = 3.162
• S0 (survival from 0 to 12 months) = 0.6406 (see above)
• Proportion of females at birth = 0.5
• F3+ (fecundity) = 3.1 * 0.6406 * 0.5 = 0.993
We modelled a polygamous mating system, with each male mating with a maximum of 4 females per time step.
Thus, we modelled total fecundity as a function of the number of breeding females only as long as there was at
least 1 male for every 4 females in adult stages. Fecundity was not directly modelled as a function of rabbit
density, because there is no correlation between the number of cubs born or number of breeding females and
population size of European rabbits62.
Populations outside protected areas: Survival rate was modelled as a function of the protection status of an
area. Iberian lynx are subject to high levels of non-natural mortality (trapping and shooting)10, especially when
moving outside protected areas. Animals in unprotected patches were modelled as having an additional 10%
mortality67. Thus, for each population, the survival rates were multiplied by (0.9+PropProtected/10), where
PropProtected is the proportion of the area of that population that is under protection. Because fecundity
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includes cub survival, which also depends on protection, fecundity values were also multiplied with the same
number.
Thus, in “Link to Metapopulation” 42, relative survival (RelSurv) and relative fecundity (RelFec) were:
Eq. 4 RelSurv = 0.9 + PropProtected/10;
Eq. 5 RelFec = 0.9 + PropProtected/10;
And, the stage matrix in the template Metapopulation file was for a protected population.
Environmental Stochasticity: We estimated temporal variability in survival rates based on a radio-tracking study
of 30 Iberian lynx from 1983 to 1989, which included juvenile (under one year old), subadult (1-2 years old),
and adult (over two years old) lynx64. This analysis resulted in an estimate of standard deviation of 0.266. After
removing variance due to demographic stochasticity68, the standard deviation was estimated as 0.252, and
coefficient of variation (CV) of survival rate as 0.41. We used this CV estimate for all age classes.
Available data allowed us to estimate a major component of temporal variability in fecundity for the Iberian
lynx. Based on data on Iberian lynx litter size from 1993 to 200062,63, we estimated the CV of litter size to be
0.42. Fecundity variation would also incorporate variation in survival from age 0 to age 1 (S0), but these data
were not available, so we used CV = 0.42 for fecundity. This value was slightly higher than what we estimated
for the European lynx (Lynx lynx) (CV of 28-34%) based on established data69.
Density dependence: The density dependence sub-model was based on Gaona et al. 1998 61 and implemented as
a “user-defined function” in RAMAS Metapop42. When the number of reproductive-age adults exceeds the
carrying capacity (K) of a population, (i) only those individuals with territories in that population can breed, and
(ii) individuals (in reproductive age classes) without a territory suffer 10% additional mortality. To simulate
these effects, the model multiplies fecundity with the proportion of individuals (in reproductive age classes) that
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hold territories in that population (T), and multiplies survival rates with T+(1-T)*0.9. The density dependence
source code is available from the authors on request.
Carrying capacity and initial abundance: In this model, carrying capacity (K) of each population was the
number of territories in that population. Thus at equilibrium, K was smaller than total population size. Carrying
capacity was estimated as a function of total habitat suitability (ths), which is a function of the average rabbit
density in that population (based on the rabbit model results), climate suitability, protection status, whether
there is active lynx management, and whether the land is suitable for breeding (see Spatial Structure below).
The carrying capacity function was:
Eq. 6 K = thr(ths*0.5,4)
The thr constant in this function was generated iteratively, to approximate the number of females with
territories in Doñana and Sierra Morena populations based on 2004-2010 surveys5,66,70 and expert advice. This
was done by generating a new constant based on the performance of the previous constant. We have used a
similar approach elsewhere58.
Initial abundances were calculated in a similar way to K, but with a different scaling factor (0.5/0.41, instead of
0.5) because K is based only on the number of individuals in the reproductive age classes, and the proportion of
individuals in these classes is 0.41:
Eq. 7 N0 = ths*0.5/0.41*0.8
An additional multiplier (0.8) is used to reflect the assumption that 80% of territories are occupied at t1. The
initial abundance of currently unoccupied populations was set to zero. The first 15 time steps of the model
simulation (i.e., 2000-2015) were used to generate a stable age distribution and equilibrium initial patch
abundance59. Number of animals and territories in 2015 closely approximated the most recent census (data not
used to parameterize the model) under a Reference climate change scenario.
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Disease: We modelled the probability of an outbreak of Feline Leukemia Virus (FeLV) as a catastrophe71,72.
Probability of a disease was modelled as a per population catastrophe.
Eq. 8 Probability of catastrophe = obs/(yrs*no. pops)
where number of observed outbreaks (obs) is proportional to the number of years of population monitoring (yrs)
multiplied by the number of populations being monitored (no. pops).
Both extant populations (Doñana or Sierra Morena) have been intensively monitored for the past twenty years 3
and a single outbreak was observed in Doñana over this period71,72. Disease prevalence during the Doñana
outbreak was estimated at 27% (see 72) and 29% (see 71) causing 54%-58% mortality71,72. We modelled FeLV
occurring at a rate of 1 in 40 years (0.025) amplifying the mortality of lynx (≥ 1 year), through a decrease in
survival rates (multiplier = 0.15); and the potential for a disease outbreak to occur owing to a pro-virus
immigrant.
Spatial Structure
The spatial structure is based on the distribution of lynx habitat requirements defined by a habitat suitability
model as the product of 4 components:
1. Rabbit: Rabbit densities per 10 x10 km cell were converted to rabbit density per ha by dividing each cell by
10000. A threshold of 1 rabbit per ha was used to remove areas of effective territory where rabbit abundance
was too low to support Iberian lynx reproduction in a given year 56. An upper estimate of annual average
rabbit abundance (10 per ha) was used to scale rabbit densities between 0 and 1.
2. Climate: Ecological niche model output with a threshold of occurrence applied (0.4; based on an AUC
threshold, see above) and scaled between 0 and 1.
3. Management: A multiplier that accounts for the additional day-to-day management supplement feeding,
habitat restoration, disease monitoring; 3,72,73 that presently goes into supporting the viability of the two
extant populations 5. A function was applied to a binary map of the two extant lynx populations (Doñana or
Sierra Morena = 1; all other areas of the Iberian Peninsula = 0) to reduce the suitability of non-actively
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managed lynx populations by 50%. This figure was chosen iteratively to maximise the fit between field data
and model estimates of patch area and population abundance for the two extant populations in the year 2000.
4. Breeding: A binary mask of lands suitable/not-suitable for breeding (see Table S2).
Thus, habitat suitability (HS) for Iberian lynx was defined by the following equation:
Eq. 9 HS = (thr(([Rabbit]/10000),1))/10)*thr([Climate],0.4)*max([Management],0.5)*[Breeding]
Where a threshold function (thr) is applied to Rabbit and Climate and a maximum of two arguments function (max) is
applied to Management. Each component of the HS function in parenthesis is described in detail above.
A threshold of 0.2 was applied to HS. The threshold was calculated based on quantiles of occurrence records
from the decade leading up to the year 2000. We explored 2.5, 5 and 10% quantiles and found 5% gave results
that best matched the present-day occurrence records (i.e., maximised sensitivity41) . The approach is described
in detail elsewhere59.
Our predator-prey model, with a slanted predator isocline, whereby prey abundance determines the predator's K:
is functionally similar to other models74; is seen as an improvement on the Lotka-Volterra model75; is more
supported by data than Lotka-Volterra models76; and has been used for modeling a variety of predator-prey
systems, including wolf-moose77, lynx-hare78, bark beetles79, and others. Alternatively, modelling the effect of
rabbits on Iberian lynx via fecundity and survival would have required a function linking Iberian lynx survival
and fecundity to rabbit abundance at different Iberian lynx densities. This is because we modelled Iberian lynx
abundance with a density-dependent model. No such empirical data exists for rabbits and Iberian lynx.
Subpopulations and potential habitats are identified as patches based on the habitat suitability map for each
year, using a habitat suitability threshold parameter and a neighbourhood distance parameter 42. Patches were
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identified as clusters of grid cells that had a HS greater than 0.2 (see above) and were within 4 grid cells (~ 4
km) of each other. A threshold of 4 grid cells was used to approximate the typical territory size66.
Correlations among grid cells: The correlation distance function was identical to the function used in the rabbit
model (see above).
Dispersal: In this model, dispersal refers to movement among subpopulations. Dispersal was modelled as a
declining function of edge-to-edge distance between subpopulations. Distances between subpopulations (d in
the equation below) were based on “friction” values that represented the difficulty of movement through
different land-cover types. A value of 1 indicates “normal” or standard habitat types. Habitats that present a
higher difficulty for dispersal have higher values. Friction values were assigned considering the results of
habitat selection analyses on detailed behavioural data of subadult lynx during their natal dispersal phase33,67,80,
habitat-related risk of mortality during dispersal65, and patterns of occurrence records that revealed habitat types
usable by dispersing individuals7. We constrained CORINE land-cover polygon values into 4 categories using
behavioural studies33,81: very good for dispersal (Friction value = 1); appropriate for dispersal (5); unsuitable for
dispersal (10); highly unsuitable for dispersal (50) (see Table S2). The spatial layer is available from the authors
on request.
Dispersal was calculated as:
Eq. 10 dispersal = 0.15 exp (-d/18.8), if d ≤ 45 km and as 0 if d>45 km
Where mean dispersal distance equals 18.8 km (Coto del Rey subpopulation =25.8 km ; Reserva Biologica de Doñana
subpopulation=11.9 km)65 and maximum dispersal distance = 45 km (Junta de Andalucia, unpublished data ). See Ferreras
et al. 2001 for a similar estimate of maximum dispersal81. There is anecdotal evidence that Iberian lynx may achieve
dispersal distances > 45km, but this has never been reported in natural conditions (i.e., for non-translocated individuals),
even in the nearly saturated lynx population of Sierra Morena.
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Stage-specific dispersal: Stage 1 individuals (12+ months old) are able to disperse over the next 12 months. All
adults can disperse, but dispersal probability is low for those holding territories, thus, we assumed that adults
(stages 2 and above) disperse at half the rate of juveniles (first stage) i.e., half the rate given by Eq. 10.
Density-dependent dispersal: Because only individuals without territories disperse, dispersal from a population
is expected to be greater when that population has a larger number of breeding-age individuals. This was
approximately modelled using density-dependent dispersal rates. The dispersal rate calculated as described
above is assumed to be the dispersal rate when the population is at carrying capacity. The dispersal rate was
modelled to change linearly with the number of breeding aged individuals with no dispersal when N = 0 (for a
practicle example, see 82).
Lynx Model Scenarios
Scenario 1 − Influence of Climate Change
We compared the influence of climate change severity on extinction risk for Iberian lynx by modelling
population persistence under a high CO2 concentration stabilising Reference scenario, a more conservative
Policy scenario that assumes substantive intervention and a No Climate Change scenario, where it is assumed
that present-day climate conditions (or more specifically, those in the year 2000) remain unchanged. For each
climate scenario 100 Iberian lynx demographic models were built, with each model drawing on a unique time
series (2020 – 2090) of rabbit maps (output from a single run off the stochastic rabbit model) for that specific
climate scenario. Models were run for 100 iterations and the first 15 years of the simulation was discarded.
Thus model results were based on the period 2015 to 2090.
Scenario 2 – Efficacy of Present-day Management Efforts
We evaluated whether extending present-day management efforts to conserve Iberian lynx beyond the
autonomous region of Andalucía will decrease extinction risk for Iberian lynx under two different greenhouse
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gas emission scenarios (Reference and Policy). Active management was modelled by approximating present-
day efforts to boost Iberian lynx numbers in Doñana and Sierra Morena5. To do this we modified the habitat
suitability equation (Eq. 9) so that the max threshold constraining Management increased linearly (from 0.5 to
1) over a thirty year period (2020 to 2050). Thus under this scenario, in 2050 all patches of suitable Iberian lynx
habitat on the Iberian Peninsula received the same positive management benefits as Doñana and Sierra Morena.
We built 100 Iberian lynx demographic models for the Reference and Policy climate scenario, each drawing on
a different time series of rabbit maps (paired to climate scenario) and run for 100 iterations, discarding results
from the first 15 years.
Scenario 3 − Managed Reintroductions
We compared the efficacy of increasing habitat suitability (i.e., based on scenario 2) with and without managed
reintroductions. The Iberian Conservation Breeding Program aims to develop and maintain 60-70 Iberian lynx
as breeding stock for translocation programs83. The program is being developed in such a way that 85% of
genetic diversity presently found in the wild will be preserved84. In 2009 the program consisted of 58 captive
animals, including 22 reproductively active females. Managed introductions of lynx into the wild are presently
being informed by simple habitat models70 that do not explicitly account for the interaction between
metapopulation processes, prey abundance and how these might change under a shifting climate.
We developed a model to determine a sustainable number of younger animals that could be removed annually
from a captive breeding population of 60 lynx − the present relocation scheme, and that planned for the future,
favours removing younger animals from a breeding population of 60 animals83. To do this we: assumed that cub
survival would be 20% higher in captivity than in the wild; that first year male survival (i.e., 12-24 months)
would be similar to that of females, because increased mortality associated with migrating and establishing a
territory would be avoided; and that population growth is not regulated by density. We concluded that removing
6 females and 6 males (aged between 1-4 years) each year, for 100 years, had little influence on population
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persistence. Median population abundance after 100 years was 46 (based on 1 000 RAMAS simulations).
Similarly, Lacy and Vargas83 showed that 12 to 13 cubs could be supplied for reintroduction per year, while still
maintaining the required (for maintenance of gene diversity) nucleus population of 60 breeders.
In a second step we explored the number of animals and the frequency of releases needed to support a viable
lynx population with an initial abundance of zero. We concluded that releasing 3 females and 3 males (aged
between 1-4 years) each year for 3 years would allow a population to persist, assuming temporal stability in
carrying capacity. Releasing fewer animals in an introduction event, or less frequently introducing animals,
increased the risk of the population failing to establish because of environmental and demographic stochasticity.
Two metrics were used to evaluate and rank which patches to release Iberian lynx into.
• One metric evaluated patches based on carrying capacity, initial population size and relative rate of survival
and fecundity (Eq. 11).
• The second metric also considered the connectivity of favourable habitat within 20km of a release site (a
distance similar to the average distance travelled by a dispersing Iberian lynx), accounting for the influence
of habitat type on movement (see modelling dispersal using friction values, above) − by summing the
suitability of patch i at time t across neighbouring patches, the metric favours release sites that are
surrounded by patches that have a high growth potential (Eq. 12).
Patch suitability was evaluated over a moving three year period, matching targeted reintroductions.
Eq. 11 ���� � � ���������������� � ����
Eq. 12 ����� � �∑ ����������������� � �����
where, Si,t is the suitability of patch i at time t; Ki,t is the carrying capacity of patch i at time t; Kmax,t is the maximum
carrying capacity of all patches at time t and Ri,t is the relative fecundity and survival multiplier of i at time t.
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Patch rankings were quite similar for both metrics. However, because connectivity has been shown elsewhere to
be negatively associated with forecast extinction risk under climate change85, we used Eq. 12 to rank patches for
all simulations.
We simulated two contrasting reintroduction schemes:
1. Geopolitical scenario: Where the underlying aim was to establish viable Iberian lynx breeding populations in
every autonomous region within its recent historical range (Supplementary Fig. S2). This scenario simulates
what is likely to be implemented by policy makers15. To do this, we spread (where ever possible)
introductions evenly across autonomous regions – targeting the most suitable patch of habitat in each of two
autonomous regions, for three consecutive years.
2. Peninsula-wide scenario: Where the underlying aim was to ensure the persistence of Iberian lynx by moving
animals to areas of most favourable habitat regardless of autonomous region. Here we introduced animals to
the two best ranked habitats regardless of regional location for three consecutive years.
The number of patches targeted (n= 2) and animals released (3 females and 3 males aged between 1-4 years
each year for three years) between 2015 and 2080 were the same for the Geopolitical and the Non-geopolitical
scenarios.
For each climate reintroduction scenario, 100 Iberian lynx demographic models were generated, with each
model drawing on a different time series of rabbit maps (paired to climate scenario) and run for 100 iterations,
discarding results from the first 15 years.
Assessing Extinction Risk and Vulnerability
We calculated extinction risk and vulnerability metrics for each scenario. Extinction risk prior to 2090 was
measured using expected minimum abundance (EMA), probability of total population size declining to zero and
median time to extinction 42. Metrics were calculated by averaging across model runs (i.e. 100 model outputs)
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for each modelled scenario. Measures of vulnerability such as annual average abundance and metapopulation
patch occupancy were also recorded.
Model Sensitivity
We undertook a global sensitivity analyses on the rabbit and lynx models separately to determine: (i) whether
our rabbit model was sensitive to assumptions surrounding disease, carrying capacity and population growth
rates; and (ii) if assumptions surrounding spatial and/or non-spatial parameters largely influenced our lynx
model. To ensure that sampled values covered the entire parameter space, we used Latin hypercube sampling86,
whereby the values for each parameter were selected from uniform increments within set ranges (for a practical
example see 87). Latin hypercube sampling allows model parameters to be varied concurrently, permitting
interactions – in contrast to local sampling methods which vary one parameter at a time88. We used generalised
linear models (GLM) to explore the relative importance of different parameter values on key indicators of
population viability89. We calculated the standardised regression coefficients (coef/S.E.) for each term in the
saturated model (six term model) as a relative metric of prevalence sensitivity to variation in vital rates90.
Confidence intervals for the coefficients were generated through a bootstrapping procedure (10,000 bootstraps)
that modelled the relationship between the response and predictor variables.
Rabbit Model Sensitivity
We used 100 sampling dimensions drawn from realistic ranges of variation (±10%) for Rmax and its standard
deviation, carrying capacity, probability of a catastrophic rabbit disease outbreak and the strength of its negative
influence on abundance. The sensitivity analysis was run for a Policy climate scenario. Each model was run for
1,000 iterations and we recorded final mean population size of non-terminal runs (i.e., those that did not end in
extinction). We normalised final mean population size using a square root transformation. We used GLMs
(Gaussian distribution and identity link function) to explore the relative importance of different parameter
values on final mean population size. We found that model results were most sensitive to perturbations in the
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population growth rate (Rmax) and were less sensitive to uncertainties regarding RHD frequency and severity
(see Table S4).
Lynx Model Sensitivity
We used 200 sampling dimensions drawn from realistic ranges of the following parameters: adult survival (±
5%), fecundity (±10%), carrying capacity (± 20%), mean dispersal distance (±20%), environmental variation
(±10%), the probability of a disease outbreak (±10%) and its influence on survival rates (±10%). The
sensitivity analysis was run for a Geopolitical reintroduction management scheme and Policy climate scenario.
For each iteration of randomly selected parameter values, we projected the population to 2090 (based on 100
lynx models each run for 100 iterations each) and recorded mean population size of non-terminal runs. We used
GLMs (Gaussian distribution and identity link function) to explore the relative importance of different
parameter values on mean final abundance in 2090 (for persistent model runs only). The results are reported in
Table S1.
Model Uncertainty
To strengthen climate change forecasts, and account for inter-model differences, we assessed the skill of GCMs
in reproducing seasonal precipitation and temperature across the Iberian Peninsula, and then generated an
ensemble forecast based on seven skilful models28,29. However, our estimates of future change in precipitation
and temperature are still influenced by strong assumptions regarding the strength of climate forcings (such as
aerosols) and climate sensitivity (the equilibrium warming for a CO2 doubling) and future rates of greenhouse
gas emissions.
We also used an ensemble type approach to generate ENM projections. Here we generated consensus
projections based on a subset of skilful ENMs using internal evaluation to rank models (see above). Although
this approach can reduce uncertainty in projections of species’ habitat suitability and/or range91, there is still an
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assumption that species distributional limits reflect physiological limits (i.e., climate-equilibrium assumption4);
and that climatic niches are stable through time92. Because dispersal limitation often constrains a species from
accessing some habitable areas93, and human impacts can prevent establishment or modify abundances92, we
used occurrence records from the second half of the 20th century to characterize the potential distribution of
Iberian lynx prior to its range collapse. This allowed us to more closely approximate climate-physiological
limits, and better capture the species’ potential niche (see above).
The level of the complexity in the rabbit and Iberian lynx metapopulation models reflect a balance between the
need for management actions to be based on realistic models that do not exclude major factors, and making the
models as robust as possible to underlying uncertainties. Our sensitivity analysis provided important feedback
for future model refinement, by identifying which spatial and non-spatial demographic parameters had the
largest influence on the model results based on uncertainties in their estimates. However, our model framework
did not allow for model error to propagate over time as a result of uncertainties at each step of the modeling
framework i.e., climate projections, ecological niche projections, rabbit model, lynx model. Propagating all
known uncertainties, occurring at each step of the modelling processes into forecasts of Iberian lynx range and
abundance under different management scenarios would be extremely difficult and would require an inordinate
amount of model development and processing time. Recently, it has been shown that niche-population models
can be fitted in a Bayesian framework to explore parameter uncertainty more explicitly94. Bayesian “range
dynamics models” are a promising development that could help improve our theoretical understanding of range
dynamics for species’ with simple demographic characteristics95. However, using Bayesian techniques to
explore uncertainty in complex models, such as the Iberian lynx model, is computationally demanding and
independent evaluation of such models is extremely complicated.
We could not directly assess the ability of our lynx niche-population model to predict lynx occurrence across
time using hindcasting techniques96 because important spatiotemporal records of human impacts (shooting,
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trapping etc.) on lynx demographic traits are not available during the period of accelerated lynx range
contraction (i.e., after or around 1975). Similarly, it is not possible to validate our 21st century lynx model using
techniques that substitute space for time, because lynx today exist only in two heavily managed areas of
Andalucía.
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Saving the Iberian lynx from extinction requires climate adaptation
Table S1: Results of the Latin-hypercube-sampling sensitivity analysis for expected mean
final abundance in 2090 (for persistent iterations of the stochastic demographic model only)
of Iberian lynx under a Geopolitical reintroduction management scheme and Policy climate
scenario that assumes strong mitigation of greenhouse gas emissions.
Dependent Variable SRC Coeff Lower CI Upper CISurv. 0.281 7539 7189 8144
Fecund. 0.083 2233 2147 2661Env. var. 0.033 -895 -1117 -663
K 0.021 570 59 796Catast. prob. 0.542 -14537 -24149 -6953Catast. mult. 0.036 -974 -1867 696
Dispersal 0.003 77 -167 302
Standardized regression coefficients (SRC; dimensionless and sum to 1), actual model
coefficients (Coeff) and their upper and lower confidence intervals (0.025 and 0.975
bootstrap percentiles) for final mean population size of non-terminal runs according to the
saturated generalized linear model, with the independent variables: adult survival (Surv.),
fecundity (Fecund.), environmental variation (Env. Var.), carrying capacity (K), probability
of an outbreak of Feline Leukemia Virus (FeLV) (Catast. prob.), severity of a FeLV outbreak
(Catast. mult.) and dispersal.
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Saving the Iberian lynx from extinction requires climate adaptation
Table S2: CORINE land cover categories used to define breeding habitat (suitable/unsuitable) and friction values for Iberian lynx dispersal. Friction values represent the difficulty of movement through different land-cover types: very good for dispersal (Friction value = 1); appropriate for dispersal (5); unsuitable for dispersal (10); highly unsuitable for dispersal (50)
Broad class Category Breeding Friction Artificial surface Continuous urban fabric Unsuitable 50 Artificial surface Discontinuous urban fabric Unsuitable 50 Artificial surface Industrial or commercial units Unsuitable 50 Artificial surface Road and rail networks and associated land Unsuitable 50 Artificial surface Port areas Unsuitable 50 Artificial surface Airports Unsuitable 50 Artificial surface Mineral extraction sites Unsuitable 50 Artificial surface Dump sites Unsuitable 50 Artificial surface Construction sites Unsuitable 50 Artificial surface Green urban areas Unsuitable 50 Artificial surface Sport and leisure facilities Unsuitable 50 Agricultural areas Non-irrigated arable land Unsuitable 50 Agricultural areas Permanently irrigated land Unsuitable 50 Agricultural areas Rice yields Unsuitable 50 Agricultural areas Vineyards Unsuitable 10 Agricultural areas Fruit trees and berry plantations Unsuitable 50 Agricultural areas Olive groves Unsuitable 10 Agricultural areas Pastures Unsuitable 10 Agricultural areas Annual crops associated with permanent
crops Unsuitable 50
Agricultural areas Complex cultivation patterns Unsuitable 10 Agricultural areas Land principally occupied by agriculture
with significant areas of natural vegetation Unsuitable 1
Agricultural areas Agro-forestry areas Unsuitable 5 Forests and semi-natural areas
Broad-leaved forests Unsuitable 1
Forests and semi-natural areas
Coniferous forests Unsuitable 1
Forests and semi-natural areas
Mixed forests Unsuitable 1
Forests and semi-natural areas
Natural grasslands Unsuitable 5
Forests and semi-natural areas
Moors and heathland Unsuitable 1
Forests and semi-natural areas
Sclerophyllous vegetation Suitable 1
Forests and semi-natural areas
Transitional woodland-shrub Suitable 1
Forests and semi-natural areas
Beaches, dunes, sands Unsuitable 50
Forests and semi-natural areas
Bare rocks Unsuitable 50
Forests and semi-natural areas
Sparsely vegetated areas Unsuitable 5
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Saving the Iberian lynx from extinction requires climate adaptation
natural areas Forests and semi-natural areas
Glaciers and perpetual snow Unsuitable 50
Wetlands Inland marshes Unsuitable 50 Wetlands Peat bogs Unsuitable 50 Wetlands Salt marshes Unsuitable 50 Wetlands Salines Unsuitable 50 Wetlands Intertidal flats Unsuitable 50 Water bodies Water courses Unsuitable 50 Water bodies Water bodies Unsuitable 50 Water bodies Coastal lagoons Unsuitable 50 Water bodies Estuaries Unsuitable 50 Water bodies Sea and ocean Unsuitable 50
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Saving the Iberian lynx from extinction requires climate adaptation
Table S3: CORINE land cover categories used to define rabbit habitat as unsuitable, habitable or highly productive.
Broad class Category Habitat Artificial surface Continuous urban fabric unsuitable Artificial surface Discontinuous urban fabric unsuitable Artificial surface Industrial or commercial units unsuitable Artificial surface Road and rail networks and associated
land unsuitable
Artificial surface Port areas unsuitable Artificial surface Airports unsuitable Artificial surface Mineral extraction sites unsuitable Artificial surface Dump sites unsuitable Artificial surface Construction sites unsuitable Artificial surface Green urban areas habitable Artificial surface Sport and leisure facilities unsuitable Agricultural areas Non-irrigated arable land habitable Agricultural areas Permanently irrigated land unsuitable Agricultural areas Rice yields unsuitable Agricultural areas Vineyards habitable Agricultural areas Fruit trees and berry plantations habitable Agricultural areas Olive groves habitable Agricultural areas Pastures highly
productive Agricultural areas Annual crops associated with permanent
crops habitable
Agricultural areas Complex cultivation patterns habitable Agricultural areas Land principally occupied by
agriculture with significant areas of natural vegetation
habitable
Agricultural areas Agro-forestry areas habitable Forests and semi-natural areas
Broad-leaved forests unsuitable
Forests and semi-natural areas
Coniferous forests unsuitable
Forests and semi-natural areas
Mixed forests habitable
Forests and semi-natural areas
Natural grasslands highly productive
Forests and semi-natural areas
Moors and heathland habitable
Forests and semi-natural areas
Sclerophyllous vegetation habitable
Forests and semi-natural areas
Transitional woodland-shrub highly productive
Forests and semi-natural areas
Beaches, dunes, sands habitable
Forests and semi-natural areas
Bare rocks unsuitable
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Saving the Iberian lynx from extinction requires climate adaptation
Forests and semi-natural areas
Sparsely vegetated areas highly productive
Forests and semi-natural areas
Burnt areas habitable
Forests and semi-natural areas
Glaciers and perpetual snow unsuitable
Wetlands Inland marshes unsuitable Wetlands Peat bogs unsuitable Wetlands Salt marshes unsuitable Wetlands Salines unsuitable Wetlands Intertidal flats unsuitable Water bodies Water courses unsuitable Water bodies Water bodies unsuitable Water bodies Coastal lagoons unsuitable Water bodies Estuaries unsuitable Water bodies Sea and ocean unsuitable
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Saving the Iberian lynx from extinction requires climate adaptation
Table S4: Results of the Latin-hypercube-sampling sensitivity analysis for expected mean
final abundance in 2090 (for persistent iterations of the stochastic demographic model only)
of Rabbits under a Policy climate scenario that assumes strong mitigation of greenhouse gas
emissions.
Dependent variable SRC Coeff Lower CI Upper CIRmax 0.56 20292.10 20140.68 21046.70
Env. var. 0.24 -10042.60 -10400.52 -9481.97Catast. prob. 0.16 5745.20 5501.87 6428.11Catast. mult. 0.03 -1094.90 -1499.04 -800.03
Carrying capacity 0.01 403.40 49.41 772.25
Standardized regression coefficients (SRC), actual model coefficients (Coeff) and their upper and
lower confidence intervals (0.025 and 0.975 bootstrap percentiles) for final mean population size of
non-terminal runs according to the saturated generalized linear model, with the independent variables:
maximum annual finite rate of population increase (Rmax), year-to-year variation in Rmax (Env. var.),
probability of a RHD outbreak (Catast. prob.), severity of an RHD outbreak (Catast. mult.) and
carrying capacity.
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Saving the Iberian lynx from extinction requires climate adaptation
Fig. S1: Forecast lynx abundance and number of populations in Iberia between 2015 and
2090 according to three possible management options and a high-CO2 concentration
stabilising Reference scenario (WRE750). The interventions are: (i) present-day conservation
practices, including increasing prey (lagomorph) densities, habitat alteration, preventing
disease and non-natural mortality (Present); (ii) reintroducing captive-bred lynx to
unoccupied habitat according to a geopolitical scheme that favours establishing lynx
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populations in every autonomous region in Spain, plus Portugal as an additional ‘region’,
within its recent historical range (Geopolitical); and (iii) a peninsula-wide strategy, focused
on releasing animals into the best quality habitat regardless of region (Peninsula-wide). The
solid lines show mean estimates for each scenario. Band widths represent 5th and 95th
percentiles.
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Saving the Iberian lynx from extinction requires climate adaptation
Fig. S2: Reintroduction areas for Iberian lynx on the Iberian Peninsula according to a
Geopolitical scenario that aims to establish viable breeding populations in five autonomous
Spanish regions, plus Portugal, within its recent historical range. The Spanish regions are:
Andalucía, Castilla-La Mancha, Comunidad Valenciana, Extremadura and Murcia. In
Portugal, suitable habitat currently exists south of the black-dashed line.
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Saving the Iberian lynx from extinction requires climate adaptation
Fig. S3: Location of lynx populations in Iberia in 2050 under the Peninsula-wide and
Geopolitical reintroduction scenarios for two climate change scenarios: a high-CO2
concentration stabilising Reference scenario (WRE750) and an alternative Policy scenario
that assumes strong mitigation (LEV1). Maps capture lynx demographic responses to spatial
patterns of rabbit abundance (conditioned by disease, climate and environmental variation)
and changes in climate suitability and landscape modification. Only grid cells where lynx
were present in 75% of runs were treated as populated. See Methods for further details.
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Saving the Iberian lynx from extinction requires climate adaptation
Fig. S4: Variation in CORINE land cover categories across the Iberian Peninsula: CORINE land cover types are shown for artificial surfaces (A), forests and semi-natural areas (B), agricultural areas (C) and water bodies. See Table S2 and S3 for CORINE land cover types that make up artificial surfaces and wetlands and water bodies.
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