# Assessing habitat suitability for black grouse broods at the bioregional scale

Alexandre Tm Defossez, Samuel Alleaume, Marc Montadert, Josselin

Giffard-Carlet, Dino Ienco, Nadia Guiffant, Sandra Luque

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Alexandre Tm Defossez, Samuel Alleaume, Marc Montadert, Josselin Giffard-Carlet, Dino Ienco, et al.. Assessing habitat suitability for black grouse broods at the bioregional scale. Wildlife Biology, In press, 10.1002/wlb3.01425 . hal-05168858

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# WILDLIFE BIOLOGY

## Research article

### Assessing habitat suitability for black grouse broods at the bioregional scale

Alexandre T. M. Defossez<fnref n="1" />✉, Samuel Alleaume<fnref n="1" />, Marc Montadert<fnref n="2" />, Josselin Giffard-Carlet<fnref n="1" />, Dino Ienco<fnref n="1" />,<fnref n="3" />, Nadia Guiffant<fnref n="1" /> and Sandra Luque<fnref n="1" />

**Correspondence:** Alexandre Defossez (alexandre.defossez@inrae.fr)

### Wildlife Biology

2025: e01425

doi: 10.1002/wlb3.01425

Subject Editor: Lance B. McNew

Editor-in-Chief: Ilse Storch

Accepted 11 June 2025

<figure><img src="image_2.png" /></figure>

The black grouse *Lyrurus tetrix*, a galliform species emblematic of the European Alps, is currently threatened by habitat change, particularly given the closure of heathland linked to the rising tree line at higher altitudes. The presence of heathlands in good ecological condition is, however, imperative for the species' reproduction. In this study, we attempted to map black grouse brood habitat suitability at a bioregional scale in the French Alps, coupling a species distribution model with multi-source remote sensing data. To predict brood habitat suitability, we used a random forest ensemble model. Altitude, ericaceous heathland, and the annual maximum normalised difference vegetation index (NDVI) emerged as the three most important variables, consistent with the ecological needs of black grouse. The proportion of ericaceous heathland was especially representative of the foraging and vegetation cover needs of black grouse hens. The resulting map was evaluated by black grouse experts and found to be consistent with their local knowledge in the context of the French Alps.

Keywords: brood habitat suitability, European Alps, *Lyrurus tetrix*, remote sensing, SDM, wildlife monitoring

## Introduction

The black grouse *Lyrurus tetrix* is a species of Eurasian galliform that favours transitional habitats between forest and grassland, including heathland with dwarf shrubs, peatlands, forbland, and grassland with tall grass and sparse trees (Bernard 1981, Storch 2007, Ludwig et al. 2009). Having virtually disappeared from the western European lowlands during the 20th century, the black grouse is now considered both a heritage species and an indicator species of alpine landscapes (Storch 2007). Black grouse populations in western Europe are declining, due to habitat degradation resulting from changing agropastoral practices (Storch 2007), climate change (Viterbi et al. 2015, Canonne et al. 2021), and increased predation pressure (Kurki et al. 2000,

<figure><img src="image_3.png" /></figure>

<footnote n="1">¹TETIS, Université de Montpellier, AgroParisTech, Cirad, CNRS, INRAE, Montpellier, France</footnote>

<footnote n="2">²Office Français de la Biodiversité, Direction de la Recherche et de l'Appui Scientifique, Vincennes, France</footnote>

<footnote n="3">³EVERGREEN, Univ. Montpellier, Inria, Montpellier, France</footnote>
Svobodová et al. 2004, Jahren et al. 2016, Cukor et al. 2021). Furthermore, hunting and overhead ski-lift cables – which increase bird collisions – can contribute to increased black grouse mortality (Buffet and Dumong-Dayot 2013, Zbinden et al. 2018, Rotelli et al. 2021). Recreational activities such as skiing, hiking, and cycling can also cause disturbances (Immitzer et al. 2014, Jäger et al. 2020, Tost et al. 2020). Habitat preservation is essential to sustain black grouse populations in the European Alps (Storch 2007). However, the optimal habitat conditions needed for the long-term viability of the species remain somewhat unclear (Grant et al. 2009, Elvesveen et al. 2023).

A potential strategy for identifying the ecological requirements of black grouse consists in identifying the main factors driving habitat selection for brood-rearing by black grouse hens during the period when their needs are most constrained (Patthey et al. 2012). In the alpine treeline ecotone and Fennoscandian forests, the heterogeneity of vegetation composition and structure, resulting from the coexistence of different successional stages (Lande et al. 2014), appears to be an essential driver for the presence of black grouse hens (Patthey et al. 2012). Two main factors were identified as ecological needs specific to habitat selection by brood rearing hens: 1) the presence of suitable vegetation cover to hide from predators (Svobodová et al. 2004, Signorell et al. 2010, Melin et al. 2016) and 2) the availability of foraging resources (Signorell et al. 2010). During the breeding season in the European Alps, black grouse hens mainly feed on ericaceous heaths such as alpenrose *Rhododendron ferrugineum*, blueberry *Vaccinium myrtillus*, and bearberry *Arctostaphylos uva-ursi*. Furthermore, the abundance of insects such as grasshoppers and ants is of major dietary importance, ensuring the survival of chicks (Ponce and Magnani 1988, Ponce 1992a, Signorell et al. 2010, Schweiger et al. 2012). Despite the well-known role played by these factors, a more precise understanding of the suitable landscape features for black grouse habitat suitability is still required to improve conservation strategies, considering the complexity of the transitional habitats between forest and grassland, the current environmental pressures threatening such habitats (e.g. climate change and traditional farming abandonment), and necessary land-use trade-offs in alpine landscapes (e.g. forestry vs tourism).

A common approach to studying species' habitat preferences is through ecological niche models known as habitat suitability models or species distribution models (SDMs; Hirzel and Le Lay 2008, Zurell et al. 2020). SDMs are used to predict species occurrence probability and habitat suitability within a given geographical area. These models employ various techniques, including linear models and machine learning methods, and rely on in situ observational data along with relevant environmental predictors (Guisan and Zimmermann 2000). Recent advancements in remote sensing techniques have greatly improved our means of generating ecologically relevant predictors, particularly the considerable range of sensors and resolutions now available for satellite imaging (Rocchini 2013, He et al. 2015, Luque et al. 2018, Leitão and

Santos 2019). Computational capabilities have concurrently improved, machine learning algorithms have become more sophisticated, and the accuracy of SDMs has consequently improved (Botella et al. 2018, Valavi et al. 2022, Adde et al. 2023). Applied to the study of black grouse and closely related grouse species inhabiting semi-open landscapes (e.g. the Caucasian black grouse *Lyrurus mlokosiewiczi*), SDMs have primarily been used to define priority conservation areas (Zhang et al. 2020) and to model past and future species distributions in response to climate scenarios (Zurell et al. 2012, Kozma et al. 2018, Tehrani et al. 2021). To the best of our knowledge, only two studies have characterised the habitat of black grouse broods by combining SDM and remote sensing data (Zurell et al. 2012, Braunisch et al. 2016). Furthermore, both studies were conducted in the same area: the Swiss Alps. By applying this approach in areas where black grouse populations are particularly threatened – especially at the edge of the species' distribution range – it may be possible to model and potentially disentangle the multiple factors influencing brood habitat selection by female black grouse. This strategy may also allow the assessment of the condition of brood habitat at a regional scale, enabling wildlife managers to make better-informed conservation decisions.

In the European Alps, numerous research efforts have been made to monitor black grouse population trends (Viterbi et al. 2015, Zbinden et al. 2018, Canonne et al. 2021, Rotelli et al. 2021, Zbinden et al. 2022). Individual counts based on field observations have proven particularly useful for estimating population dynamics metrics such as reproductive success (Ludwig et al. 2010, Rotelli et al. 2021, Wegge et al. 2022). Whilst this kind of approach is useful in order to assess the viability of local populations, it demands the mobilisation of significant human resources over potentially extensive geographic areas. In order to follow up population trends realistically, such monitoring efforts must be conducted regularly with at least one campaign per year. Another significant issue with the counting method is its failure to account for the potential non-random distribution of individuals between the counting sites. This could introduce sampling bias when monitoring a rare species (Jeliazkov et al. 2022). Mapping black grouse habitat suitability using SDMs could allow for a more equitable spatial distribution of the counting effort in the field and reduce sampling bias (Guisan and Zimmerman 2000, Guisan et al. 2006, Singh et al. 2009, Chiffard et al. 2020).

The objective of this research is twofold. Firstly, we seek to improve our understanding of the ecological conditions that underpin the presence of black grouse broods by assessing the influence of landscape composition, vegetation functioning, and habitat heterogeneity. Secondly, we aim to demonstrate how an operational map can be produced for use by wildlife managers to both inform future observation campaigns and improve the estimation of black grouse population size. To address these objectives, we have proposed an SDM approach that may be used to predict brood habitat suitability (BHS) by coupling remote sensing data and field brood observations in the French Alps.
# Material and Methods

## Study area

The study area, which we refer to as the ‘Northern French Inner Alps’ (NFIA), concentrates on a mountainous, bioclimatic zone in the Northern French Alps (‘Alpes du Nord’), which was proposed to be a bioclimatic region by Ozenda and Borel (2000). According to the wildlife managers of the Observatoire des Galliformes de Montagne (OGM; French Observatory of Mountain Galliformes), it is home to the largest population of black grouse in the French Alps. This population is primarily located between 1600 and 2400 m a.s.l. The low altitudinal limit has been determined based on the OGM’s expertise (although black grouse can be observed below 1600 m, their presence tends to be rarefied), and the high altitudinal limit corresponds to the theoretical limit of the subalpine ecotone, in other words, the upper limit of the trees. The total surface area represented by the NFIA is 2744 km² (Fig. 1).

## Methodological framework

In order to predict brood habitat suitability (BHS) in the study area, we followed two distinct steps: 1) we generated a set of spatially explicit predictor variables to describe brood habitat; 2) we used an SDM to predict BHS based on these variables

and brood observation data (Fig. 2). We used a number of variables to describe the landscape features that are important for determining brood habitat selection by black grouse hens (Table 1). In the selection of predictor variables, an effort was made to encompass diverse aspects of the landscape (composition, structure, functioning), thereby ensuring the representation of potential influencing factors of the BHS. Due to the complexity of their habitat at the interface between open and closed vegetation (habitat mosaics), and the lack of cartographic products with sufficient accuracy for this interface (e.g. the French Land Use map ‘OSO’, www.theia-land.fr/ces-occupation-des-terres/occupation-des-sols), we attempted to create our own predictors of BHS using remote sensing data. We used high-resolution SPOT-6, SPOT-7 (panchromatic and multispectral bands), and Sentinel-2 images. The BHS was predicted using methods provided by the ‘biomod2’ package (ver. 4.2.4; Thuiller et al. 2023). All data processing was performed in R (ver. 4.3.3; www.r-project.org) or Python (ver. 3; Van Rossum and Drake 2009).

## Brood observation data

The brood observation data used in this study were recorded by the OGM, which has overseen the long-term monitoring of black grouse populations in the French Alps since 1992. Monitoring was conducted in 70 counting units distributed

<figure><img src="image_4.png" /><figcaption>Figure 1. Northern French Inner Alps (NFIA) the area with the most significant black grouse population. The updated altitude range of this bioclimatic region was established between 1600 m and 2400 m a.s.l. Background map from ESRI World Imagery.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 2. Methodological framework to map brood habitat suitability (BHS) of black grouse.</figcaption></figure>

across the NFIA (Fig. 3) in late July and August – the brood-rearing season. Brood detection is carried out with the help of a pointing dog. For each counting unit, the observer and dog start at the bottom of the slope and follow perpendicular tracts up the slope until they reach the top of the counting unit. Each brood observation is recorded (i.e. number of chicks) and geolocalised. In our study, we considered the presence of at least one chick as a valid observation. In our analysis, only observations made between 2015 and 2021 were considered. This observation period (seven full years) encompasses what we believe to be a sufficiently large and geographically well-distributed number of observations for the purposes of our modelling approach (i.e. 1453 brood observations). Moreover, as the temporal distance from the reference year increases (2020), the probability of observations being influenced by landscape changes also increases. For this reason, we also chose not to include data prior to 2015.

# Habitat suitability predictor variables

## Land cover classification

We characterised landscape composition using a land cover classification process at a fine spatial resolution of 1.5 m in

the NFIA. Four classes of land cover were considered likely to affect the brood probability of brood presence: ericaceous heathland, forest, grassland with a potential positive effect, and mineral cover (e.g. rocks, scree, bare ground, rocky outcrops and possibly anthropogenic infrastructures, such as ski resort buildings, etc.). To produce the land cover map, we downloaded nine SPOT-6 and SPOT-7 images (SPOT6-7) acquired between July and September 2020 from the DINAMIS Data Terra portal (https://dinamis.data-terra.org/en). We created a panchromatic mosaic and a pansharpened multispectral mosaic (red, green, blue, near-infrared) with a spatial resolution of 1.5 m. To produce the multispectral mosaic, the original images (6 m) were reduced by fusion ('pansharpening') to the spatial resolution of the panchromatic image (1.5 m). Mosaicing and pansharpening operations were carried out with the Orfeo Toolbox (Grizonnet et al. 2017).

Segmentation and classification of the SPOT6-7 mosaic were carried out using supervised deep learning approaches using a convolutional neural network (CNN; Kattenborn et al. 2021). A supervised deep learning classification method was employed to perform land cover mapping. Six land cover classes corresponding to generic vegetation stratum (i.e. 'herbaceous', 'low ligneous', and 'high ligneous') were identified by photo-interpretation (Guiffant et al. 2024): 'grassland', 'ericaceous heathland' and 'forest', supplemented with non-vegetated classes corresponding to 'mineral', 'water', and 'shaded area'. A total of 150 training polygons per land cover class were extracted throughout the NFIA. We chose to use a two-step deep learning approach based on two convolutional neural networks (CNN). The first CNN classifies the central patch of pixels (33 × 33 pixels) centred successively on each of the training polygon pixels. This model, trained on the central pixels of the patches, is then used to classify the pixels inside extended scenes (96 × 96 pixels), exploiting information contextual to each class (Tan and Le 2019). Using the scenes as a training dataset, the second CNN classifies the entire image according to a semantic segmentation process (Ronneberger et al. 2015). This patch-based method provides the model with contextual information, making it easier to handle transition zones between classes. By splitting the classification process into two steps, we were able to significantly reduce the computational time required to classify all pixels on a regional scale. More details on the deep learning method can be found in the Supporting information.

The percentage of each land cover class was ultimately determined within a grid (cell size 10 × 10 m = 100 m²). For each land cover class, we obtained a 100 m² resolution image corresponding to the proportion of the class in question in the landscape. The 'shaded area' and 'water surface' classes were deemed irrelevant for study purposes and were excluded from the SDM. Despite the arithmetic dependency problem of land cover proportions (their sum must equal 1) and the associated difficulties (Barlett 1936), we chose to not use a categorical land cover variable in order to consider the impact of fine-scale mosaics between different vegetation types. Given the selected cell size of 100 m², we considered the effect
<table><caption>Table 1. List of predictive variables used to map BHS.</caption><thead><tr><th>Landscape dimension</th><th>Landscape feature</th><th>Predictor variable</th><th>Description</th><th>Expected effect on BHS</th><th>Remote sensing data</th></tr></thead><tbody><tr><td rowspan="4">Composition</td><td rowspan="4">Land cover</td><td>Mineral cover</td><td>Mineral cover per grid cell of 100 m² (%)</td><td>Strong negative effect at high values</td><td rowspan="4">SPOT-6 and SPOT-7 (multispectral)</td></tr><tr><td>Grassland cover</td><td>Grassland cover per grid cell of 100 m² (%)</td><td>Positive or negative effect depending on the height of the grass and the amount of forbs</td></tr><tr><td>Heathland cover</td><td>Ericaceous heathland cover per grid cell of 100 m² (%)</td><td>Strong positive effect as it provides vegetation cover and food</td></tr><tr><td>Forest cover</td><td>Forest cover per grid cell of 100 m² (%)</td><td>Negative at high value ranges (presence of forest predators) but positive at low value ranges (arthropod diversity)</td></tr><tr><td rowspan="2">Structure</td><td>Habitat (structural) heterogeneity</td><td>Haralick entropy (texture index)</td><td>Randomness in image intensity</td><td>Positive effect as a proxy for habitat heterogeneity</td><td>SPOT-6 and SPOT-7 (panchromatic)</td></tr><tr><td>Topography</td><td>Altitude</td><td>Altitude above sea level (m)</td><td>Strong positive effect for values corresponding to the altitude of favourable habitats (strong driver in vegetation succession patterns)</td><td>Digital elevation model (DEM)</td></tr><tr><td rowspan="2">Functioning</td><td>Vegetation productivity</td><td>NDVI<sub>max</sub></td><td>Maximum annual normalised difference vegetation index</td><td>Positive effect of more productive and suitable vegetation cover (food resource and shelter)</td><td rowspan="2">Sentinel-2</td></tr><tr><td>Vegetation water content</td><td>NDWI<sub>1max</sub></td><td>Maximum annual normalised difference water index</td><td></td></tr></tbody></table>

of arithmetic dependency between classes to be minimal, as there is a low probability of multiple classes being present within 100 m² given the original resolution of the land cover map of 1.5 m (i.e. pixels of 2.25 m²). While this only impacts a limited number of cells in theory, we deemed it important to consider this aspect in view of the black grouse's preference for habitat mosaics.

## Assessment of habitat structural heterogeneity

Black grouse live in the transition zone between montane forest and alpine grassland, marked by the presence of a mosaic of habitats. Trees tend to become smaller and are distributed in less dense patterns within a mosaic where shrubs are dominant. In this zone, the coexistence of various vegetation successional stages (i.e. herbaceous, low ligneous, high ligneous), is a source of vegetation structural heterogeneity. Also, local topographic variations and the presence of rocky elements (e.g. rocky outcrops and scree) can also be a source of structural heterogeneity in the landscape. Considering the preference shown by black grouse for landscapes characterised by a mosaic of habitats (Patthey et al. 2012), we decided to quantify the habitat structural heterogeneity in order to predict the BHS. To this end, we calculated the textural index Haralick entropy (Haralick 1973) from the panchromatic mosaic of

SPOT6-7 images using the Orfeo Toolbox. Since entropy is a measure of 'disorder' (here the randomness in image intensity), we used it as a proxy for structural heterogeneity in our approach. At the panchromatic resolution of SPOT6-7 (1.5 m), we expected the Haralick entropy to be able to quantify structural heterogeneity, including the structure of vegetation and local terrain variations. The resulting Haralick entropy image was resampled to a resolution of 10 m in order to align it with the final spatial resolution of the predictor variables.

## Calculation of vegetation indices

The complete Sentinel-2 time-series captured in 2020 was downloaded from three tiles (31TGK, 31TGL, 32TLR) from the Theia Data Terra portal (108 images per tile; https://catalogue.theia.data-terra.org). To summarise the spatial distribution of vegetation greenness and water content, the vegetation indices NDVI (Tucker 1979) and NDWI (Gao 1996) were calculated for each pixel of all Sentinel-2 images. The maximum annual value per pixel of each index (NDVI<sub>max</sub> and NDWI<sub>1max</sub>) was calculated in accordance with the approach developed by Coops et al. (2008). The NDVI<sub>max</sub> and NDWI<sub>1max</sub> were calculated using the latest features of the iota2 processing chain which was originally developed for land cover classification (Inglada et al. 2017). The maximum
<figure><img src="image_6.png" /><figcaption>Figure 3. Black grouse count units in the NFIA. (a) For each count unit (in red), black grouse broods are monitored every year in July–August using pointing dogs. (b) Example of a favourable black grouse environment composed of ericaceous heathland at the transition between forest and grassland. (c) Example of ericaceous bush, here *Rhododendron ferrugineum* in bloom. The images were captured on 22 June 2022 in the specified count unit in (a), close to Les Ménuires ski resort. Background map from ESRI World Imagery.</figcaption></figure>

value of the index was selected to eliminate the potential bias introduced by changing snow and cloud cover associated with high reflectance values in a mountainous context. The vegetation indices tiles were subsequently integrated into a mosaic with Orfeo Toolbox and trimmed to align with the spatial boundaries of the NFIA.

## Altitude extraction

We downloaded the DEM tiles from the BD ALTI database of the French National Geographic Institute (IGN) and assembled them into a mosaic. We resampled the mosaic to a spatial resolution of 10 m. The DEM mosaic was used to map the altitudinal spatial variation of the landscape.

## Brood habitat suitability modelling

### Predictor variable collinearity evaluation

Prior to the utilisation of the SDM to predict BHS in the NFIA, a collinearity assessment (Dormann et al. 2013) was conducted on the predictor variables over the entire study area using the 'virtualspecies' R package (Leroy et al. 2016). The Pearson correlation coefficient was used to test the correlation between predictor variables. To visualise the correlation distance between the predictor variables, we conducted a hierarchical ascendant classification (Supporting information). With a threshold of 0.7 (Dormann et al. 2013), no significant correlation was observed between the predictor variables. We decided to retain all the predictor variables in the SDM, although this result should be treated with caution

in the case of land cover proportions, due to their arithmetic dependence.

### SDM training and application

In order to predict BHS in the NFIA, we selected the widely used random forest (RF) algorithm (Breiman 2001) which has proven to perform well in a variety of contexts (Díaz-Uriarte and Alvarez de Andrés 2006, Peters et al. 2007, Mellor et al. 2013, Couronné et al. 2018, Kumar and Polepaka 2020) including SDMs (Mi et al. 2017, Benkendorf and Hawkins 2020, Luan et al. 2020, Rongcheng et al. 2025). RFs are based on multiple decision trees whereby each tree makes limited information-based predictions. The final decision is derived from the combined predictions of all the decision trees. To predict BHS, we used the RF method available in the 'biomod2' R package (Thuiller et al. 2023). Fine-tuning was not performed, and the default parameters were kept (similar to 'randomForest' package (Breiman 2018); i.e. number of trees = 500; number of variables sampled per split = 3; minimal size of nodes = 5). As the brood observation data we used is a presence-only dataset and not a presence-absence dataset, it was necessary to define pseudo-absences (PAs) to represent the range of possible values of the different predictor variables in our SDM (i.e. background data). To generate the PA dataset, an initial pool of randomly distributed PAs was generated which corresponded to 10 times the number of observations (43 590) within the NFIA. A set of 1453 random PAs was drawn from this initial pool to correspond to the number of observations, as per recommendations for machine learning
algorithms (Barbet-Massin et al. 2012). The predictor vari-
ables were stacked in the same grid (cell size = 100 m²) and,
alongside the brood observation and PA datasets, were used as
the input for the SDM. Table 1 summarises the information
associated with each predictor variable used in the model. To
ensure that the results were not overfitted, a cross-validation
was performed by randomly partitioning the data between a
training set (80%) and a validation set (20%).

To reduce the potential influence of the random draw of PAs and the partitioning of data into training and validation sets, three draws of PAs and two cross-validation repetitions were carried out. The predictions of the six resulting RF models (i.e. three PA sorts × two cross-validation partitioning) were combined by median calculation to build the final SDM. Finally, this model was projected in the FNIA, including all the pixels of the study area (characterised by the values of predictor variables). The BHS values of the resulting map had a range between 0 and 100% with a spatial resolution of 10 m.

Variable importance evaluation

We calculated the importance score of each predictor variable based on the ‘random accuracy importance’ measure (Strobl and Zeileis 2008, Strobl et al. 2009). The principle behind this method, considered the most advanced for random for- ests, is to test the association between a predictor variable and the variable to be predicted by randomly permuting the values of the predictor variable in question. The importance score is equal to one minus the value of the Pearson corre- lation coefficient between the model’s predictions with the permuted variable and its predictions without permutation. To enhance the robustness of the scores, we performed three permutation runs for each predictor variable evaluated.

To assess the influence of predictor variable variations on model predictions, we plotted the response curves associated with each predictor variable according to the method proposed by Elith et al. (2005). This method, which is complementary to the calculation of the importance score, consists in representing the variation in the model's predictions as a function of the evaluated predictor variable under consideration, while the other variables are held constant. For this study, we chose to calculate the median to ensure the variables remained constant.

Results

Brood habitat suitability mapping

The performance scores of the RF were 0.92 for AUC and
0.67 for TSS. Given the considerable size of the study area
(2744 km²) and the significant spatial resolution of the image
(10 m), it is not feasible to display the BHS map in its entirety
in a comprehensive way. We therefore decided to present the
SDM predictions for the area surrounding the Méribel ski
resort in Fig. 4 as an example. We have included the predic-
tive land cover map to support the interpretation of the BHS
map (Fig. 4c). As expected, an examination of the land cover

map reveals a clear correlation between the presence of erica-
ceous heathland and high BHS values (> 80%).

Importance of predictor variables

Regarding the importance of predictor variables, altitude (27%) was the most important variable, followed by heathland cover (20%) and NDVI<sub>max</sub> (18%) (Fig. 5). Haralick entropy (10%) and NDWI<sub>max</sub> (9%) were moderately important, whilst mineral (6%), grassland (5%) and forest cover (4%) had little influence on the probability of brood presence. For all landscape variables considered, a very low variability in the importance score between permutation runs was observed (Fig. 5).

Figure 6 shows the response curves of the SDM in relation to each landscape variable. The probability of brood occurrence peaks when NDVI<sub>max</sub> is between 750 and 1000, whilst the probability indicated by NDWI<sub>max</sub> is largely constant with a slight increase upwards of 750. With respect to altitude, the probability shows a progressive and irregular increase between 1800 and 2100 m, followed by a gradual decrease up to 2200 m, and then an abrupt drop beyond that point. Haralick entropy has minimal influence on the probability, which remains nearly constant. Regarding mineral cover, the probability decreases progressively as the mineral fraction increases from 0 to 1. Conversely, the probability increases progressively as the proportion of grassland and forest increases.

Discussion

Landscape feature influence on BHS

Land cover

One of the aims of this study was to assess the influence of landscape composition on BHS by assessing the effect of the proportion of each of our land cover classes. Ericaceous heathland cover exhibited the second highest performance score, achieving a value of 20% among the predictor vari- ables (Fig. 5). This result confirms the vital role of ericaceous heathland for black grouse brood-rearing in subalpine land- scapes (Bernard 1981, Storch 2007, Schweiger et al. 2012, Braunisch et al. 2016). Ericaceous heathland constitutes a valuable source of food (e.g. bilberries, shoots, buds) and shelter from predators (Signorell et al. 2010, Schweiger et al. 2012). In contrast, mineral, grassland, and forest covers exhibited the lowest importance scores (6, 5 and 4%, respec- tively) (Fig. 5). This finding suggests that landscape composi- tion at a fine scale (100 m²) is a potentially less significant predictor than a specific vegetation cover, which tends to meet the brood rearing needs of black grouse hens. In order to confirm this result, further research may compare the influence of ericaceous heathland cover versus compositional heterogeneity or habitat diversity metrics (Bar-Masada and Wood 2012, Cooper et al. 2020). Nevertheless, we have pro- vided strong evidence for the need to conserve ericaceous heathland as a key component of the landscape to ensure the
<figure><img src="image_7.png" /><figcaption>Figure 4. Black grouse BHS map focusing on the Méribel ski station based on the SDM and the land cover map resulting from the deep learning process: (a) Location of Méribel in the NFIA. (b) RGB map based on the SPOT6-7 mosaic. (c) Land cover map based on deep learning. (d) BHS map predicted by the SDM. Background Map from ESRI World Imagery (a) and the SPOT6-7 mosaic (b,d).</figcaption></figure>

long-term viability of black grouse populations and facilitate their breeding in the western Alps.

An examination of the model response curves (Fig. 6) revealed the positive impact of grassland, heathland and forest on BHS and the negative effect produced by mineral covers. This result complements the importance score evaluation and indicates that grassland, forest, and mineral covers do indeed influence BHS, albeit to a lesser extent than ericaceous heathland cover (Fig. 5). Mineral cover produced a negative effect on BHS, shown through a decrease in the response curve (Fig. 6). This result was expected since the presence of rocky outcrops, scree, and bare soil limit vegetation growth and prevent the formation of protective cover for broods. The positive effect of grassland cover could be attributed to the abundance of arthropods such as grasshoppers which serve as a significant protein source for the chicks (Ponce 1992b, Signorell et al. 2010). A tall grass cover or fodder may also provide suitable cover in certain instances (Bernard 1981). Given the progressive abandonment of agricultural activities in the Alps over several decades, it would be worthwhile for future research to ascertain whether BHS correlates with traditional pasture activity, and more specifically to fodder cover. A possible explanation for the slight positive effect exhibited by forest cover may lie in arthropod

diversity attributed to understorey cover (Melin et al. 2016, Huhta et al. 2017).

Although the response curves demonstrated by grassland and forest cover show a continuous increase in a manner comparable to that of ericaceous heathland cover, the

<figure><img src="image_8.png" /><figcaption>Figure 5. Importance scores (%) of the landscape variables used to predict black grouse BHS.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 6. Response curves of the SDM for each landscape variable. Each curve represents the variation in the probability of brood presence as a function of each predictor variable. For each predictor variable considered, the remaining predictor variables were set at their median value. This method, originally proposed by Elith et al. (2005), has been adapted in the framework of the ‘biomod2’ package to enable the observation of the influence of each predictor variable on the probability of occurrence.</figcaption></figure>

proposed method does not guarantee the absence of a pla-
teau, or even a decrease, with the increase in the proportion
of each of these cover types. The ‘evaluation strip’ method
employed (Elith et al. 2005) does not consider the potential
effects of interaction between predictor variables. It is pos-
sible that interaction effects are produced between classes,
as with the other predictor variables, that are not visible on
these curves. More complex statistical approaches, such as
those proposed by Douma and Weedon (2019), could help
us to better evaluate the effects of different land cover propor-
tions by accounting for the effect of arithmetic dependence.
Finally the specific conditions under which forest and grass-
land cover may promote the increase of BHS require further
clarification.

Topography

Altitude had the greatest effect on BHS predictions with an importance score of 27% (Fig. 5). The response curve indicates that the most favourable habitats are located between 1800 and 2300 m (Fig. 6), consistent with the subalpine zone which starts at 1700–1900 m and ends at 2300–2500 m in the European Alps. This zone corresponds to the transitional habitat between the montane forest and the alpine grassland, where the majority of the ericaceous heathlands are found, as well as an ensemble of ecological conditions not explicitly considered in our model, but which may affect BHS (Mayor et al. 2017). This outcome corroborates the persistence of incorporating altitude as a predictive variable for

broods, thereby underscoring the value of identifying addi-
tional covariates that may influence the BHS.

Habitat heterogeneity

The Haralick entropy had a moderate impact on BHS pre-
dictions with an importance score of 10% (Fig. 5), showing
no clear effect on BHS variation (Fig. 6). However, it has
been demonstrated that black grouse favour heterogeneous
habitats linked to the presence of diverse vegetation strata, or
structural heterogeneity (Patthey et al. 2012). Considering
the predominance of ericaceous heathland, one may infer that
the presence of a homogeneous vegetation stratum favourable
to broods remains the most significant factor affecting BHS,
ahead of structural heterogeneity.

Alternatively, the apparent absence of a clear effect of
structural heterogeneity on BHS, as we defined it, may sug-
gest that Haralick entropy is not the most suitable indicator
of habitat structural heterogeneity. Indeed, estimating the
structural and compositional heterogeneity of a landscape
remains a significant challenge (Lang et al. 2019). More com-
plex approaches, such as Fourier transform texture analysis,
may better account for the structural heterogeneity of veg-
etation (Couteron et al. 2002). The texture image that we
produced mainly reveals a heterogeneity linked to the pres-
ence of various rock outcrops, pathways, and occasionally the
presence of human infrastructures (Supporting information).
Although these elements may influence the potential pres-
ence of broods, it is expected that the vertical structure of
the vegetation holds the most sway. This approach is however
more difficult to implement at a regional scale and requires
the use of very high spatial resolution data (< 1 m).

Vegetation productivity and water content

Annual NDVI<sub>max</sub> was the third most important variable after ericaceous heathland cover and altitude (Fig. 5) with an importance score of 18%. The response curve displayed a pronounced increase in probability for NDVI<sub>max</sub> values between 600 and 1000 (Fig. 6). Vegetation productivity may therefore be said to have a positive effect on BHS, especially since more productive vegetation is likely to provide both a more effective protective cover and a more abundant food resource. However, the benefit offered by vegetation may depend on the type of vegetation considered (e.g. a forest represented by high NDVI values does not necessarily represent the most favourable habitat). In any case, this suggests that not only must the type of vegetation be considered, but also its ecological functioning and the way in which other indicators, derived from remote sensing, may thereby contribute (Cabello et al. 2012, Alleaume et al. 2018).

In contrast, the NDWI1max gave a relatively low impor-
tance score of 9% (Fig. 5) and a low peak in the response
curve around a NDWI1max value of 900 (Fig. 6). This sug-
gests that vegetation water content may be less representa-
tive of the ecological conditions important to brood habitat
suitability. In future research, the use of biophysical variables
derived from remote sensing, predicted by radiative transfer
models (RTMs) rather than spectral indices, could represent
an opportunity to clarify the association between vegetation
functioning and brood presence (Darvishzadeh et al. 2008,
Hedley et al. 2009).

Implications for black grouse monitoring

The consistency of the present study was corroborated by
OGM experts who delivered a positive assessment of the
BHS map obtained. Indeed, the SDM predictions concurred
with the knowledge of the experts consulted. The output
map provides an opportunity to enhance the monitoring of
black grouse populations within the French Alps. Notably, it
facilitates regional evaluation of the current state of the brood
habitat and could simultaneously address the monitoring
requirements of local nature reserves due to the high spatial
resolution of the SDM predictions (10 m). Better knowledge
of brood habitat preferences also provides essential data for
predicting long-term reproductive success. Provided there are
no abrupt changes to the alpine landscape, this map can be
used to plan future field campaigns and enable more efficient
sampling, reducing the bias associated with the non-random
distribution of individuals (Jeliazkov et al. 2022). Finally, our
approach could be adapted for use in other alpine bioregions
by incorporating additional variables directly related to the
ecological requirements of black grouse (e.g. the presence
of larch forests in the southern Alps; Filacorda et al. 1997,
Nelli et al. 2016).

On the other hand, it is worth noting that the approach
we proposed – through the production of a reliable land

cover map (F-scores of 0.98 and 0.85) – is likely to be use-
ful for other applications or for addressing research questions
beyond those related to monitoring black grouse popula-
tions. For instance, such an land cover map could be uti-
lised for the purpose of studying compositional heterogeneity
(Yu et al. 2018, Steele and Wolz 2019), ecological continu-
ities (Hanson et al. 2022, Préau et al. 2022), or vegetation
biomass (Natarajan et al. 2016, Mataveli et al. 2023) in the
context of alpine landscape and wildlife monitoring.

Conclusion

In this study, the ecological conditions favourable to the
presence of black grouse broods in an alpine bioregion were
examined. The results confirm some of the hypotheses we
formulated regarding our predictor variables (Table 1). The
results confirm the fundamental importance of ericaceous
heathland and, to some extent, vegetation productivity (indi-
cated by the findings relating to NDVI<sub>max</sub>) to the viability
of black brood grouse habitats. Our outcomes also suggest
a certain degree of flexibility with regards to the presence of
forest and grassland, whilst the presence of mineralised areas
demonstrates a slight negative effect. However, further inves-
tigation is required to elucidate the precise role of structural
heterogeneity, such as vertical layering of vegetation and the
presence of various rock formations, on brood habitat suit-
ability. The BHS map could potentially be used to improve
the accuracy of black grouse population size estimations in
the European Alps and facilitate the more effective imple-
mentation of sampling efforts in the field.

*Acknowledgements* – The authors would like to express their gratitude to the Observatoire des Galliformes de Montagnes (OGM) which collect and manage grouse monitoring data in the Alps, and to the Hunting associations and Office Français de la Biodiversité (OFB) services of the Haute-Savoie, Savoie, Isère and Hautes-Alpes departments for their invaluable contribution to the collection of the in situ black grouse brood dataset. We would also like to thank the OFB for their guidance on the reliability of our modelling results, and for their financial support of the ‘Tétras-Lyre’ project (cooperation agreement OFB-21-1166) which led to the publication of this work. Finally, we would like to extend our gratitude to the Écrins National Park in the French Alps, for their enthusiasm and feedback regarding the potential post-validation prospects for the BHS map.

*Funding* – This work was funded by the Office Français de la Biodiversité (OFB) as part of the ‘Tétras-lyre’ project (cooperation agreement OFB-21-1166).

Author contributions

**Alexandre T. M. Defossez:** Conceptualization (lead); Formal analysis (lead); Methodology (lead); Validation (lead); Writing – original draft (lead); Writing – review and editing (lead). **Samuel Alleaume:** Conceptualization (lead); Formal analysis (equal); Methodology (equal); Supervision
(equal); Validation (equal); Writing – original draft (lead); Writing – review and editing (equal). **Marc Montadert:** Conceptualization (equal); Data curation (equal); Formal analysis (supporting); Funding acquisition (lead); Investigation (lead); Methodology (supporting); Supervision (equal); Validation (lead); Writing – original draft (equal); Writing – review and editing (supporting). **Josselin Giffard-Carlet:** Conceptualization (equal); Formal analysis (lead); Methodology (equal). **Dino Ienco:** Conceptualization (supporting); Formal analysis (lead); Methodology (lead); Supervision (supporting); Writing – original draft (supporting); Writing – review and editing (supporting). **Nadia Guiffant:** Conceptualization (supporting); Data curation (lead); Investigation (equal); Methodology (supporting); Resources (equal); Writing – original draft (supporting). **Sandra Luque:** Conceptualization (equal); Formal analysis (supporting); Methodology (supporting); Supervision (lead); Validation (equal); Writing – original draft (lead); Writing – review and editing (equal).

**Transparent peer review**

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/wlb 3.01425.

**Data availability statement**

Data are available from the French national repository Recherche Data Gouv: https://doi.org/10.57745/7N1KMB (Defossez et al. 2025).

**Supporting information**

The Supporting information associated with this article is available with the online version.

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