A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems
et al. (2014) A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in
Semiarid Mediterranean Agricultural Systems. PLoS ONE 9(3): e92790. doi:10.1371/journal.pone.0092790
A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems
Laura Cardador 0
Miquel De Ca ceres 0
Gerard Bota 0
David Giralt 0
Fabia n Casas 0
Beatriz Arroyo 0
Fran cois Mougeot 0
Carlos Cantero-Martnez 0
Judit Moncunill 0
Simon J. Butler 0
Llus Brotons 0
Francisco Moreira, Institute of Agronomy, University of Lisbon, Portugal
0 1 Forest Sciences Center of Catalonia (CTFC) , Solsona, Catalonia, Spain, 2 Estacio n Experimental de Zonas A ridas (EEZA-CSIC) , La Can ada de San Urbano , Almer a, Spain, 3 Instituto de Investigacio n en Recursos Cinege ticos (IREC)-(CSIC-UCLM-JCCM), Ciudad Real , Spain , 4 Departament de Produccio Vegetal i Cie`ncia Forestal, Universidad de Lleida (UDL) , Lleida , Spain , 5 School of Biological Sciences, University of East Anglia , Norwich , United Kingdom , 6 CREAF, Bellaterra, Catalonia , Spain
European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species' key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species' distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species' occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models.
Funding: This study was supported by the Project Steppeahead funded by Fundacio n General del Consejo Superior de Investigaciones Cientficas from Spain
(FGCSIC) and Banco Santander. F.C. was supported by a JAE-Doc contract financed by CSIC and the European Social Fund (ESF). L.C. was supported by a
postdoctoral contract funded by FGCSIC and Banco Santander. FARMDINDIS project, funded by Infraestructures.cat (Generalitat de Catalunya) and Aig ues del
Segarra-Garrigues SA, provided data on field censuses. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of
Competing Interests: The authors have declared that no competing interests exist.
Traditional low-intensity agricultural systems are often
associated with high biodiversity conservation value in different regions
of the world . As a result of thousands of years of agricultural
expansion, a large number of wild species live on land dedicated to
human food production, and their preservation strongly depends
on traditional low-intensity practices [2,3]. This is particularly
relevant in some regions, such as Europe, where agricultural
landscapes represent the major part (about 60%) of non-urban
areas [2,3]. Here, traditional agricultural systems, based on low
intensive farming and extensive grazing, have historically provided
highly heterogeneous landscapes capable of holding species-rich
communities of organisms . However, in recent decades these
systems have come under pressure due to socio-economic changes,
increased food demands and new technological opportunities .
As a result, farmland in many industrialized countries is being
profoundly altered, mainly through agricultural intensification and
land abandonment, posing a major challenge for biodiversity
conservation today [5,6]. Unless the detrimental impacts of
present and future agricultural practices can be prevented or
mitigated, many agricultural landscapes will suffer from further
degradation in the coming decades . Managing the
environmental effects of these agricultural changes thus requires the
development of frameworks that allow the exploration of their
potential threats and opportunities, even before the changes occur
Habitat models may provide valuable tools for predicting
species responses to different land-use alternatives. To date, the
potential effects of changing landscapes on species dynamics and
biodiversity have tended to be addressed through correlative
models whereby species-habitat associations are estimated by
statistically relating current distributions to particular structural
land cover types [10,11]. When habitat conditions remain fixed
temporally and spatially and there is appropriate information to
use as a surrogate for factors relevant to species habitat selection
, such habitat association models can be successful at
predicting species occurrence or population dynamics from
habitat characteristics. However, they can be much less successful
when used to make predictions outside the area or habitat
conditions for which the model has been calibrated . As a
consequence, it has recently been proposed that, instead of using
structural land cover types, land use population dynamics
relationships might be better examined in the context of functional
cover types, such as foraging or nesting habitat, identified on the
basis of resource dependencies of species or species groups
Unlike habitat-based approaches, resource-based models assess
the relative quality of a selected habitat type (e.g. crop or
agricultural practice) on the basis of key factors underpinning the
distribution and abundance of the considered species [11,16]. For
example, species habitat associations, if present, are largely
dictated by the availability of key resources in such habitats, rather
than the habitat per se [14,15]. Basing distribution and abundance
models on resource availability rather than habitat types is
therefore likely to allow more robust predictions, even under
changing environmental conditions. This is particularly important
in farmland landscapes, where intra- and inter-annual changes in
crop types and agricultural management are usual [15,17].
However, to date there has been little application of
resourcebased models with predictive purposes in conservation studies
because they are often difficult to build, relying on robust
knowledge of the biology of the target organisms and about the
In this study, we develop a resource-based model framework to
estimate habitat suitability for target species, according to simple
information on species key resource requirements, and examine
whether it can be used to link land-use and local species
distribution. We adopt a simple definition of a species
requirements, characterised by diet, foraging habitat and nesting site,
because previous research has shown significant associations
between changes in the expected availability of these resources
and population trends [7,8,15]. We use this framework to evaluate
the ability of different cover types to provide suitable and sufficient
resources to support viable populations of farmland bird species
during the breeding period, taking as a study case four steppe bird
species in a lowland area within north-eastern Iberian Peninsula.
We validate our model by determining the relationship between
resource-based habitat suitability predictions and information on
species local distribution. We also compare the performance of
our resource-based approach to that of a simple habitat-based
model statistically relating species occurrence and structural cover
types. Finally, we use our approach to predict the effect that
changes in farming systems can have on farmland bird habitat
suitability, and compare these predictions with those obtained
using the habitat-based model.
Materials and Methods
The project had the permission of the relevant national
authorities (i.e., Direccio General de Medi Natural, Departament
dAgricultura, Ramaderia, Pesca, Alimentacio i Medi Natural de
la Generalitat de Catalunya). Field work conducted was not
invasive and did not require the manipulation of live animals. All
censuses were performed using public rural tracks and no
additional permission to access to privately owned land was
Study Area and Species
The study area is located in the Catalan Ebro basin,
northeastern Spain (Fig. 1a, b). This area comprises around 400 km2 of
farmlands, mostly included in Special Protection Areas (SPA), sites
established under 2009/147/EC Birds Directive and included in
Natura 2000 network (i.e., the European network of nature
protection areas). The landscape is predominantly flat and low
altitude, broken by discrete ranges of small hills (0400 m asl), and
has a semiarid Mediterranean climate. Traditionally, agriculture
in this area was dominated by extensive cultivation of cereal crops
(mainly wheat and barley) and fallows, with some olive and
almond trees in the steepest areas. During the 20th century,
agriculture in this area underwent several important changes,
mainly due to the introduction of different irrigation schemes,
including the replacement of traditionally cultivated cereals with a
variety of alternative crops (basically fodder crops, i.e., alfalfa and
maize, and orchards) and a substantial decrease in the area of
fallow lands and field margins [19,20]. Nowadays the study area is
composed of discrete irrigated and non-irrigated areas. Drylands
consist of extensively managed winter cereal crops and fallows,
olive and almond trees. Irrigated areas include intensively
managed crops such as alfalfa, corn and some winter cereal; and
orchard production with peach, pear, apple, nectarine and other
As in other regions in Europe, agricultural intensification in this
area has led to decreases in biodiversity and in the breeding
population size of several species [3,6,21]. Of particular concern
has been the decline of steppe bird populations in the study area
over recent decades, as it is part of the western European
stronghold of many of these species . In the present study we
used data on ecological requirements and field censuses of four
ground-nesting steppe bird species, which are still widely
distributed in the study area, to implement and validate our
models. These include Little Bustard Tetrax tetrax, Stone Curlew
Burhinus oedicnemus and Calandra Lark Melanocorypha calandra, all of
conservation concern and protected at European, national or
regional levels, and Red-legged Partridge Alectoris rufa, a
widespread but declining farmland game bird species of significant
importance to the local rural economy.
Bird Occurrence Data
We established a total of 145 linear transects in the study area in
2010 and 2011 (83 surveyed in both years, 40 just in 2010 and
22 just in 2011) (Fig. 1b), along which observers walked and
recorded all birds either acoustically or visually detected. Transect
lines had a length of ca. 500 m and were spaced more than 1 km
apart. We established maximal 100 m-wide belts on each side of
the transect line. Censuses were performed in May, to match
observation effort to periods of high activity for three of the studied
species in the study area. In contrast, detectability of red-legged
partridges maybe somewhat decrease in May as compared to
earlier months. However, as we used presence/absence rather
than abundance data for analyses (see below), we do not think this
represents a significant bias in our data. Censuses were performed
from 6 a.m. to noon and only in periods with good weather
conditions (with no rain and no or light wind). Transects were
chosen to contain different proportions of the six dominant
herbaceous cover types, i.e. combinations of crop types and
cropping management for those crops, present in our study area
. These cover types included dry extensively-managed cereal,
irrigated intensively-managed cereal, till fallow (arable land that
was not cultivated for one or more seasons, and which was
ploughed to avoid weed development regularly), no-till fallow
(arable land that was not cultivated for one or more seasons, and
where weeds were managed, if necessary, using herbicides),
irrigated intensively-managed alfalfa and irrigated
intensivelymanaged maize. These cover types together represented $80% of
the total surface of all monitored transects. Other cover types (i.e.,
shrub, urban areas and orchards) were considered unsuitable as
the study species rarely use them. The relative proportions of
different cover types in each transect remained constant
throughout the breeding period.
For the analyses below, we classified each of our four study
species as either present or absent on each transect survey. We
used presence/absence data rather than abundance data because
of its high performance for low density or cryptic species , such
as those monitored in the present study. Since all cover types
considered are herbaceous, with broadly similar vegetation
structure, and because censuses were conducted in periods of
high activity of birds by either visual or acoustic contact, we do not
think detectability differences among habitat types significantly
biased our presence/absence data. For Little Bustards, only
information on males was used for analyses, due to poor female
detectability using transect line methods .
Figure 2 sets out the general framework we used to model
habitat suitability as a function of nesting and foraging resource
availability. This approach first identifies the pool of potential
habitat types in a particular study area according to their
agronomic, environmental and socio-economic characteristics (in
our study case and for validation purposes, these were the six
dominant herbaceous cover types defined above). The framework
then follows four steps: (1) the construction of a matrix to describe
species resource requirements for each vital activity (i.e. nesting
and foraging) and for each time period categorized; (2) the
quantification of resource availability in each habitat type and for
each time period; (3) the calculation of habitat suitability indices
for each of the vital activities for each species in each habitat type
and for each time period; (4) the temporal and/or spatial
integration of habitat suitability indices to encompass temporal
and spatial variation at the scales at which considered vital
activities occur. Note that the most appropriate duration of each
time period categorized will depend on both the temporal
dynamics of the system being considered and species resource
requirements. In the following subsections we give details on these
steps for our case study system (a detailed example can also be
found in Appendix S1).
Step 1: Matrix of resource requirements. Resource
requirement data for each of the ecological requirements
considered (i.e. foraging and nesting habitat characteristics
specifically vegetation height [26,27] - and diet content) are
described in a species6resource table R = [rij] for each time period.
Using data from 26 studies across the species main distribution
range areas (Table 1), here we categorized resource requirements
on the basis of preferred vegetation height for foraging or nesting,
or presence of a given food resource in the diet. This allowed us to
categorise resource requirements using a simple and comparable
approach between species and regardless of data quality in the
original publications. Four vegetation height categories (025 cm,
2550 cm, 50100 cm, .100 cm) were defined to describe
foraging and nesting resources related to habitat characteristics.
For each vegetation height category, the rij value assigned reflected
an assessment of the capability of species i for using vegetation
height j (0 not preferred i.e. vegetation heights used only
occasionally or avoided; 1 preferred i.e. vegetation heights where
an important and high proportion of the individuals forage or
nest). For dietary resources we considered four main food types
(seeds, plants, invertebrates and vertebrates), with each rij value
reflecting an ordinal measure of the degree of preference for each
food type j by species i (0 - not used i.e. food never or very rarely
consumed; 0.5 - rarely used i.e. food consumed secondarily, when
usual food is not widely available; 1 - preferentially used i.e. a
primary and frequent food resource for a given species) .
Further, we evaluated the sensitivity of our resource-based model
to preference values by considering three alternative ways of
scoring preferences: (i) 0/0/1; ii) 0/0.25/1 and (iii) 0/0.75/1 for
resources not used, rarely used and preferentially used,
respectively. Values were derived for two periods, spring (April-June) and
summer (July-September), to reflect temporal changes in resource
requirements through the breeding season.
Step 2: Matrix of resource availability. Resource
availability data for both habitat and dietary resources are described in
a habitat type 6 resource table A = [akj] for each time period. Each akj
value is a measure of the availability of resource j in habitat unit k.
In our study case, we used available information on agricultural
practices applied to different cover types in our study area (i.e.,
sowing and harvesting dates, fertilizers used, irrigation and plough;
) in combination with authors expert knowledge based on 10
years of field surveys, to qualitatively describe the probability of a
given cover type k having a given vegetation height j (i.e., 025 cm,
2550 cm, 50100 cm, .100 cm) in each time period (0 - not
possible i.e. vegetation height category never or very rarely
present; 0.5 - rare i.e. infrequent or marginal vegetation height
category; 1 - usual i.e. dominant vegetation height category). We
then transformed these values to relative frequencies by dividing
the score of each category by the sum of scores of all categories in a
given period and land cover type, so that the sum of all categories
was 1. We also evaluated the sensitivity of our resource-based
model to the choice of probability scores by applying the following
alternative scoring options for the three probability classes (not
possible, rare, usual): (i) 0/0/1; (ii) 0/0.25/1 and (iii) 0/0.75/1.
Values were derived monthly according to vegetation growth
patterns and land management (for more detailed information, see
For dietary resources, akj values indicate the relative abundance
of resource j in cover type k in a given time period. We assumed
that the abundance of dietary resource j in cover type k was
inversely related to both the number of agricultural practices that
negatively affect that resource (n) and the intensity of these
practices ( f ) [7,8]. Specifically, we calculated relative food
abundance for each resource as,
akj ~1=(n:f z1),
where n ? f +1 was used in order to avoid infinite akj values. For
these calculations, we considered the effect of three main practices
known to be directly related to food abundance: agro-chemical
use, irrigation and ploughing . Thus, n values were bounded
by 0 and 3. These practices can lead to reduction in food supply of
our study species directly (e.g. reduction in weed availability
through the use of herbicides) or indirectly (e.g. elimination
through competition of many broad-leaved plant species and
se o se se se o se se o o se
Y N Y Y Y N Y Y N N Y
se se o e e e e e e e e
s s s s s s s s
Y Y N Y Y Y Y Y Y Y Y
invertebrates associated with them by stimulation of crop growth
through crop irrigation or fertilizer use) [4,29]. We used field yield
(tonnes/ha) as the scaling factor for production system intensity
( f ), with f for fallow lands set to 1. We also examined the
sensitivity of our model to the choice of this scaling factor by: (1)
setting the scaling factor to one for all agricultural systems
considered; and (2) using a coarser qualitative measures describing
the degree of intensification of practices (1 - low-intensive, applied
to dry extensively-managed cereal, till fallow and no-till fallow; 2
high-intensive, applied to irrigated intensively-managed cereal,
alfalfa and maize). Expected food abundance was calculated for
spring (April-June) and summer ( July-September), based on land
management (Table 2).
Step 3: Calculation of habitat suitability for each vital
activity. Habitat suitability for a particular species in a given
habitat type and period considered is broadly defined as the degree
of coincidence between species key ecological requirements and
resource availability in that habitat type. For each ecological
requirement considered (i.e., dietary resources, foraging vegetation
height or nesting vegetation height) and period, we define a habitat
type 6 species table S = [sik], where suitability sik of habitat type k for
species i is defined as the scalar product of the corresponding
vectors of matrices A (availability) and R (requirements):
Suitability values derived from vegetation characteristics were,
by definition, bounded between 0 and 1. To meet the same
criterion, we truncated suitability values associated with food
abundance to 1, acknowledging that species can use
complementarily different food resource in relation to their availability in a
given cover type and species trophic niche until total diet
requirements are satisfied (but not over this threshold).
In our framework, foraging habitat suitability depends on both
expected food abundance and accessibility to food due to habitat
preferences (related to efficiency of foraging or predator
avoidance) [17,26]. Since both components (abundance and
accessibility) are considered obligate, a multiplicative approach is used.
Thus, we defined foraging suitability siFk for species i and habitat
type k as the product of the corresponding suitability derived from
foraging habitat characteristics (siFkH ) and the suitability derived
from expected food abundance (siFkD):
Calandra Lark and Red-legged Partridge and between April and
July for Stone Curlew) . Note that no nesting resourcerelated
habitat suitability measures were calculated for Little Bustard, and
perceived habitat suitability is therefore based only on foraging
habitat for model validation; as discussed above, occurrence data
for this species were based solely on displaying males, which do not
take part in nesting activities, and nests do not necessarily occur
within territories of displaying males . All indices were
implemented in R software (R Development Core Team, 2011).
Step 4: Integration of habitat suitability estimates. The
final objective of our modelling framework is to obtain single
habitat suitability estimates to represent probability of occurrence
for each target species in the periods and areas of interest.
Integration of habitat suitability estimates will depend on spatial
and temporal variation of habitat suitability estimates and species
ecology, as well as on the purpose of the application. For model
validation in our study case, spatial integration was done at the
scale of surveyed transects (200 m6500 m), which was considered
to be broadly representative of the scale of habitat used by studied
species during the breeding season (or territories of displaying
males in the case of Little Bustard, see above). We calculated
foraging and nesting habitat suitability estimates for species i in
each monitored transect t (hereafter, siFt and sit ) as weighted
averages of siNk and siFk estimates across all cover types present in
such transect. In these calculations we defined the weight of each
cover type k as its relative proportion within the 200 m6500 m
area encompassed by the transect belt. Relative proportions of
such cover types were calculated with respect to the total transect
surface, which takes into account that in some transects unsuitable
habitats (i.e., shrub, urban areas and orchards) were also present
(see above in bird occurrence data). In this way, we considered the
effects of both the quality and quantity of available resources on
final habitat suitability .
Next, temporal integration of monthly siFt and siNt estimates was
conducted by averaging monthly values across: (1) the complete
breeding season (i.e. April September); and (2) different windows
of time around bird surveys: (2a) the month when the survey was
undertaken (May); (2b) including the month before sampling
(AprMay); (2c) including one month after sampling (May-Jun); and (2d)
including one month either side of sampling (Apr-Jun). This was
done to evaluate the strength of the relationship between May
occurrence data and suitability over different time periods
because, although it is known that habitat suitability throughout
the complete breeding season is important for species fitness,
habitat selection patterns may respond to more discrete periods
[31,32]. For temporal integration of sit , only months within
nesting period were taken into account. Finally, since both nesting
and foraging habitat suitability are essential to ensure population
viability , we calculated total habitat suitability as the
geometric mean of nesting-and foraging-related habitat suitability
(i.e., that total habitat suitability is 0 if one of these components is
0). All analyses were implemented in R software.
We employed generalized linear models (GLM) to analyse
factors affecting species occurrence (binomial error distribution;
logit-link function), using presence/absence of each species during
bird census as the dependent variable. For duplicate samples (i.e.,
transects monitored in both years) and to avoid pseudoreplication,
one sample was randomly selected to be used in models. We used
the percentage of each herbaceous cover type (i.e. dry
extensivelymanaged cereal, irrigated intensively-managed cereal, no-till
fallow, till fallow, irrigated intensively managed alfalfa, irrigated
In contrast, nesting habitat suitability (siNk ) is defined as the
suitability derived from nesting habitat characteristics (siNkH ) only:
In our study case, we calculated siFk and siNk values monthly,
according to the temporal resolution of vegetation height data.
However, we assumed that (1) food preferences remained constant
across both spring (April-June) and summer (July-September)
periods and (2) expected food abundance was constant within each
period but potentially varied between them. Nesting-related
habitat suitability was only calculated for those months when
nesting activity occurs, according to the known breeding
requirements of the different species (between April and June for
M S + + + + + 1 0 0 0 1
U F S + + +
tscap rcop ilrtaae sseed rcop rtsae rcop ilrtaae rcop rtsae rcop rcop irtaae sseed rcop tsae
im fo tm fo fo teb fo tm fo teb fo fo tm fo fo rb
y ss n ss s re ss n s re ss ss n ss ss rte
K L p Lo sLo ivn L p sLo ivn L L p L L v
e o la o la o o la o o e
intensively-managed maize) and a category other to account for
additional cover types as the fixed effects. Both the linear and
quadratic forms of these variables were tested. Using a multimodel
inference approach , model-averaged parameter estimates
were derived on the basis of corrected Akaikes information
criteria (AICc) for all possible subsets of models constructed from
combinations of these variables. Multimodel inference was
implemented in R software by the functions dredge and
model.avg from the MuMIm library.
We used a five-fold cross-validation procedure to generate
model predictions, so that data used for model assessments were
independent from the data used for calibration. In this way, the
occurrence dataset was randomly divided into 5 independent
partitions, with four used for model calibration and the remaining
partition (20% of data) for model assessment. This procedure was
repeated five times, so that we obtained a prediction for each
Model Validation and Comparison
We relied on three indices to compare model predictions to
observed species occurrence: prediction success (i.e., proportion of
correctly predicted observations), sensitivity (i.e., proportion of
correctly predicted presences) and specificity (i.e., proportion of
correctly predicted absences). For these analyses, we used the value
of presence probability that maximized the sum of sensitivity plus
specificity as a threshold to transform our model predictions to
presence/absence data . Further, we also used AUC (Area
Under the Receiver Operating Characteristic Curve) as a
threshold independent measure of model performance. Model
performance was assessed using the functions somers2,
optim.thresh and accuracy from the Hmisc and SDMTools libraries
in R software.
As an example of its application, we use our resource-based
model to predict the potential effects, on the study area, of
removing the set-aside support across Europe following the
2008 CAP (Common Agricultural Policy) Health check, which
has generated strong debate over its possible negative impacts on
biodiversity conservation [36,37]. Under this policy decision, the
most likely agronomic scenario for our study area includes the loss
of fallow land in favour of cereal fields , with consequent
changes in habitat structure and food availability through the
breeding season. This process was not apparent in the study area
by the date of this study, but could lead to important changes in
habitat composition in the near future. At the time of our surveys,
the study area comprises 71% of dry cereal fields, 3% of till fallow,
7% of no-till fallow, 1% of maize, 9% of irrigated cereal and 4% of
alfalfa. Here, we assessed the effect on the habitat suitability for
each species of shifting 30%, 50% and 100% of current fallow land
(till plus no-till) to dry cereal within the study area. We also
compared resource-based model predictions with predictions
obtained using the habitat-based models. For these analyses, only
models considered sufficiently robust (AUC $0.6) were used .
As with the transect data, probability of occurrence was calculated
as the average suitability values across habitat types, using their
proportions in each agronomic scenario as weights. Further, we
translated model predictions into presence/absence data at the
transect scale using threshold values obtained in the model
validation (see above and in Table 3). In all calculations we
assumed that proportion of till and no-till fallow systems lost will
Cover Types and Resourced-based Habitat Suitability Estimates
Estimated habitat suitability based on the resource-based
models for each species varied markedly both between cover
types considered and throughout the breeding cycle (Fig. 3).
Overall, the highest foraging and nesting habitat suitability
estimates were calculated for fallow systems (till and no-till),
particularly late in the season when they offered a low vegetation
height (see Fig. S1) and higher expected food abundances than the
other systems (Table 2). Temporal variation of total habitat
suitability estimates, was also detected at the transect scale, where
monthly habitat suitability estimates were highly correlated with
those expected based just on the most abundant cover type in that
transect (Pearson correlation coefficients for different species and
months ranges between 0.8 and 0.9). Dominant cover types
represented on average 76616% of the surface of monitored
Resource-based Model Validation and Sensitivity
The agreement between observed and predicted occurrence was
reasonable for Stone Curlew, Calandra Lark and Little Bustard
(AUC range: 0.650.74); resource-based models correctly classified
between 70 and 77% of total observations for these three species
(Table 3). For male Little Bustard and Stone Curlew, specificity
values were superior than sensitivity values, suggesting that a
considerable proportion of presences occur at locations with low
habitat suitability. On the contrary, sensitivity values were larger
than specificity for Calandra Lark. Prediction success was low for
Red-legged Partridge (AUC ,0.6). Model performance using
habitat suitability calculated for different sub-periods of the
breeding cycle was highly in accordance with habitat suitability
calculated for the whole breeding season (Table 3) and no clear
pattern of variation was observed. Sensitivity analyses for model
parameters resulted overall in model presence/absence predictions
being only slightly affected by alternative parameter values (#10%
of change in habitat suitability predictions at the transect level) for
Stone Curlew and Calandra Lark, although changes in the
particular value of habitat suitability could be higher (Table 4).
However, presence/absence prediction variation was high for
Little Bustard in relation to scaling factor used for production
system intensity, and for the Red-legged Partridge in relation to
scores for food preferences and habitat availability (Table 4).
Habitat-based Models and Model Comparison
The agreement between observed and predicted occurrence,
based on multimodel inference, was again reasonable for Stone
Curlew and Calandra Lark (AUC was 0.64 and 0.76, respectively).
Indeed, all measures of model performance indicated that
habitatbased models performed very similarly to resource-based models
for these species (Table 3), with strong correlation in transect-level
predictions between the two approaches (Calandra Lark: r = 0.60;
Stone Curlew: r = 0.63). In contrast, prediction success for Little
Bustard and Red-legged Partridge was low (AUC = 0.52 and 0.47
respectively) and correlations between predictions were weaker
(male Little Bustard: r = 0.53; Red-legged Partridge: r = 0.20).
Habitat suitability for male Little Bustard, Calandra Lark and
Stone Curlew was predicted to decrease between 4% and 23% in
the study area as a result of a 30% to 100% decrease in the
proportion of fallow land (Fig. 4a,b). These trends were very
similar to those produced by habitat-based models in the study
area for Stone Curlew and Calandra Lark (Fig. 4c). Expected
changes in habitat suitability values were not equivalent for all
months of the breeding season according to the resource-based
models (Fig. 5). This was particularly apparent in the case of
Calandra Lark and Little Bustard, for which major losses of
foraging- and nesting-habitat suitability (for Calandra Lark) were
expected to occur between May and July, which are also the
months of the breeding season with lowest suitability values.
In this study, we developed a resource-based model, whereby
land uses and agricultural practices are defined in terms of
availability of key foraging and nesting resources for target species
[7,8]. Habitat suitability estimates generated by our models were
congruent to independent species occurrence data in our study
area and overall performed similarly (and better in the case of one
study species) to habitat-based models based on current
distributions. Acceptable resource-based models were obtained for three
out of four species considered (AUC: 0.650.74), suggesting that
overall the assumptions of the model structure are reasonable for
these species, but at the limit of what can be considered of useful
application according to standards posed by several authors (i.e.,
AUC = 0.7). However, given that these results arose from
application of our models to relatively homogeneous extensive
agricultural landscapes (the most suitable region for these species
in Catalonia, most of it included in the Natura 2000 network) and
that we use presence/absence data for model validation, which
tends to be insensitive to small variations in habitat suitability, we
take them as support and encouragement for further work to
improve our resource-based models in the near future.
Contrary to the other species studied, our resource-based
suitability estimates failed to predict the occurrence of Red-legged
Partridge in the study area. This could be due to a number of
factors. First, our resource-based model might be conservative
since it assumes that if suitable habitat exists, species will be able to
access and use it. However, this might not be the case if
populations are maintained at low-density (for example because
they are hunted), or there are other abiotic or biotic constraints
(e.g. microclimate, topography or interspecific interactions) that
also limit current ranges. The former process might be occurring
to the Red-legged Partridge in the study area, which is subject to
For habitat-based models, model performance according to predictions obtained using cross-validation is shown. For resource-based models, model performance of
resource-based suitability estimates for the whole breeding season and for different windows of time across bird surveys (in parenthesis) are shown. The threshold that
maximizes the sum of sensitivity plus specificity is also given. RP = Red-legged Partridge, SC = Stone Curlew, CL = Calandra Lark, LB = male Little Bustard. N = 145.
high hunting pressure . In this situation it might be expected
that resource-based models over-predict species current range.
Consistent with this prediction, the sensitivity index (percentage of
correctly classified presences) clearly exceeded the specificity index
(percentage of correctly classified absences) for this species. The
fact that censuses were conducted in May, which is relatively late
in the breeding season for this species, might also contribute to
such result. Furthermore, compared to other study species, the
Red-legged Partridge may be considered as a habitat generalist
. It may be that our inability to adequately predict occurrence
of this species (by both resource-based and habitat-based models) is
underpinned by a more general pattern related to the difficulty of
estimating resource requirements in generalist species due to
interindividual variability [10,40]. Currently this limitation is an
unsolved problem in distribution modelling and might affect both
resource-based and habitat-based models .
For Stone Curlew and Calandra Lark, resource-based and
habitat-based models performed similarly when predicting current
species local distribution but the former performed better for Little
Bustard. This relatively small improvement in predictive capability
associated with the resource-based models, despite the additional
time and data required for their implementation, may be related to
the fact that, although not explicitly considered,
resourceprovisioning might be implicitly incorporated in habitat-based
models when applied to current conditions. For example, if a
strong linear relationship between the area of a particular cover
type and the availability of the key resource type underpinning a
species habitat selection exists, one would expect to see a strong
association between the species and that habitat type .
However, these relationships are often context specific (both
temporally and spatially) and particular anthropogenic definitions
of cover types may not necessarily closely reflect the same
underlying resource availability in times or places other than that
of model calibration, thus limiting the effectiveness of conservation
strategies based on these approaches [15,41]. Greater differences
in predictive capability would therefore be expected if, for
example, the models were used to predict occurrence in other
areas or the impacts of changes such as altered management
practices that are not easily incorporated into habitat-based
By allowing the explicit incorporation of management practices,
as defined by farmers/agronomists, into functional habitat types,
resource-based models may also enhance the establishment of links
between the languages of conservationists and
farmers/agronomists, who are ultimately responsible for the management of
agricultural systems and the implementation of the majority of
conservation measures developed . For example, while wide
consensus exists among conservationists about the positive role of
fallow land on farmland biodiversity [37,43,44], it is rarely taken
into account that, according to farmer/agronomist management,
different types of fallows may exists (e.g. till or no-till fallow in our
study area) and that they may be perceived as different habitats by
As with habitat-based models, resource-based models are
limited by the availability of appropriate data and also the
mechanistic links between ecological requirements and resource
provisioning need to be sufficiently understood. For example, in
our study we used vegetation height as a measure of foraging
habitat and nesting resources because we were working with
ground-nesting farmland birds, for which vegetation height has
been described as a good measure of nesting and foraging habitat
but, for example, information on the type of forest and its
horizontal and vertical structure might be required to if using this
approach to quantify habitat suitability for forest birds .
In this regard, the development of our resource-based models
required a detailed revision of available information on farming
systems and steppe birds, a process that also highlighted key data
limitations and areas requiring further research. Firstly, our
spatial-temporal integration of habitat suitability along monitored
transects assumes that species occurrence depends on the crop
surface along those transects (500 m6200 m). Since this level of
spatial resolution is broadly representative of the scale of habitat
used during the breeding season by our study species, we do not
think that this assumption introduced significant bias into our
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T o s P V D In P d
results. However, including finer resolution information of the
scale at which nesting and foraging habitat use occur would likely
improve model accuracy [41,46]. Secondly, our assessment of
habitat suitability is based solely on the structure and management
of the cropped areas during spring and summer. However
resource availability over winter can also limit bird distribution
and a model incorporating habitat suitability for the whole year
would thus likely improve model accuracy for sedentary species
. Additionally, due to the scarcity of quantitative information
on food abundance and vegetation structure in the different
agricultural systems present in our study area, expected food
abundance and vegetation structure had to be calculated
qualitatively and at the cover-type level according to their different
management regimes. Whilst we believe this assumption is justified
for the analyses presented here, based on previous information of
the effect of considered agricultural practices and their intensity on
resource provisioning , further research to quantitatively
identify food abundance and vegetation structure in different
agricultural systems, as well as intra-system variations over the
breeding season would allow more accurate predictions to be
made. In a similar way, while using qualitative information on
species resource requirements allowed direct comparisons
between species for which the amount and quality of information is
highly variable, iterative refinement of model parameters (through
collection of more experimental or observational data or model
calibration to other existing distribution data sets) would also allow
more accurate predictions to be made [11,17]. In this respect,
sensitivity analyses can help to clarify the proportion of the total
error that might be accounted for by uncertainty in models
parameters, while also providing valuable insights for prioritizing
new data collection . For example, our sensitivity analyses
suggested that, while availability and preferences for different
vegetation structure and food resources are important, collecting
empirical data on management intensity is a higher priority, at
least for Little Bustard. Further, our models indicate that habitat
suitability explained the probability of occurrence (and thus
distribution), but it would be worthwhile in future works exploring
the relationship between suitability and abundance as this would
provide greater resolution to our understanding of species
responses to changes in resource availability ; predicting and
responding to population decline may also be more efficient and
effective than predicting and responding to changes in species
distribution. Finally, predictive abilities of our resource-based
models, which were based on vegetation height and food
requirements, may be improved by considering additional
requirements, such as vegetation cover or heterogeneity, or
modulating the suitability of habitats in relation to distance to
unsuitable areas. Further work may explore the convenience of the
inclusion of such type of additional information for model
Frameworks for assessing the potential effects of different
landuse alternatives on biodiversity are fundamental for guiding
objective management decisions. Resource-based models, such as
the one developed here, provide a structure for using an
understanding of the functional links between agricultural
practices, provision of key resources and the response of
organisms, to predict potential effects of changing land-uses on
habitat suitability. When management alternatives lie within the
range of agricultural options available in a given area and
management practices are expected to remain constant,
resourcebased and habitat-based models seem to offer overall similar
predictions. However, if new cover types or new management
strategies are introduced (for which habitat-based models are not
parameterised), resource-based models offer a structure for
integrating inter-disciplinary knowledge (agronomic and ecological
knowledge) to allow the impact of those changes to be evaluated.
These models could be iteratively refined (through collection of
more experimental or observational data or model calibration to
other existing distribution data sets) so that more accurate model
parameters can be incorporated. However, a key challenge is to
provide models that are sufficiently accurate and general to enable
application to a variety of temporal and spatial context while
remaining feasible to parameterise. Conservation of threatened
species in humanized landscapes has not always been addressed in
a multidisciplinary and mechanistic way where, by integrating
information on possible land-uses or land-management practices
and species key ecological requirements, habitat suitability could
be determined a priori. However, the development of frameworks
that allow establishing explicit links between agronomic and
environmental realities in humanized landscapes are essential to
inform decision-making processes and to design agronomic
solutions delivering acceptable trade-offs between agricultural
production and conservation.
Figure S1 Crop vegetation height in different
agricultural systems considered in our study throughout the
breeding season, according to agricultural practices
applied and authors expert knowledge.
We wish to thank J. Estrada, S. Sales, A. Bonan, J. Castello, F. Gonzalez,
X. Larruy, M. Anton and A. Petit, who assisted in the fieldwork, as well as
N. Pou and R. Bosch for their help on the data base maintenance.
Conceived and designed the experiments: LC MDC GB DG FC BA FM
CC-M JM SB LB. Analyzed the data: LC MC. Contributed reagents/
materials/analysis tools: GB DG FC CC-M JM. Wrote the paper: LC
MDC GB DG FC BA FM CC-M JM SB LB.
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