The role of broadleaved hedgerows and landscape composition for biodiversity conservation in a pine plantation context

The role of broadleaved hedgerows and landscape composition for biodiversity conservation in a pine plantation context | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The role of broadleaved hedgerows and landscape composition for biodiversity conservation in a pine plantation context Nattan Plat, Eric Allan, Severin Jouveau, Olivier Bonnard, Jean-Baptiste Rivoal, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7131998/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Biodiversity and Conservation → Version 1 posted 12 You are reading this latest preprint version Abstract In forested landscapes, compositional and configurational heterogeneity have been shown to enhance biodiversity. However, changing the type of land cover to improve landscape heterogeneity remains a logistical challenge for forest managers. While hedgerows and forest patches have been widely studied for their role in promoting biodiversity in agricultural landscapes (i.e., “bocage”), it remains unclear to what extent increasing the share of these interstitial elements would enhance the diversity of different taxonomic groups in plantation landscapes. To address this question, we conducted our study in a homogeneous and monospecific pine plantation landscape in southwestern France, where we compared the diversity of six taxonomic groups in broadleaved hedgerows vs pine stand edges. We also analysed the effect of the connectivity of hedgerows to broadleaved stands and the proportion of broadleaved stands in the landscapes. Beyond species richness and community composition of each taxon, we calculated multidiversity indexes across all groups (using dominant, rare, or forest specialist species). Multidiversity was significantly higher in hedgerows than in pine stand edges. Hedgerows were home to communities with a distinct composition, including a greater abundance of rare species and forest specialist species. Increasing broadleaved cover in the landscape had a negative effect on multidiversity but altered community composition in three out of six groups. The connectivity of hedgerows to broadleaved stands had no significant effect on biodiversity. Preserving or planting broadleaved hedgerows therefore emerges as an effective and practical management method for enhancing biodiversity, particularly of forest specialist species, in pine plantation landscapes. Multidiversity Plants Birds Arthropods Reptiles Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Biodiversity loss is increasing worldwide due to multiple anthropogenic factors such as land-use change, resource over-exploitation, pollution, climate change and biological invasions, which ultimately lead to the degradation of ecosystem functions and services (Balvanera et al., 2006 ; Cardinale et al., 2011 ; Christian, 2023 ; Jaureguiberry et al., 2022 ). Forests represent the most species-rich habitat on the planet, particularly in the tropics, and are therefore essential for the conservation of global biodiversity (Brockerhoff et al., 2017 ; Gibson et al., 2011 ; Muys et al., 2022 ). In the context of global change, which is increasing the frequency of abiotic and biotic disturbances (Forzieri et al., 2024 ; Patacca et al., 2023 ), protecting forest ecosystems is therefore crucial to preserving biodiversity. Monospecific forests are particularly vulnerable to disturbances, provide fewer ecosystem services and host a lower biodiversity than mixed forests (Feng et al., 2022 ; Gamfeldt et al., 2013 ; Jactel et al., 2017 ). Increasing tree species diversity can enhance both α-diversity and β‐diversity of associated taxa at the stand level (Ampoorter et al., 2020 ; Brockerhoff et al., 2008 ; Kremer et al., 2025 ) and landscape level (Duflot et al., 2022 ; Heinrichs et al., 2019 ; Muys et al., 2022 ). According to community ecology theory, these differences in local biodiversity patterns may emerge from a sequence of filters acting on the regional species pool (Cadotte & Tucker, 2017 ; Germain et al., 2018 ; Weiher & Keddy, 1995 ). First, dispersal limitations determine which species can reach and establish in suitable habitats. Second, abiotic conditions (e.g., temperature, precipitation) select species able to occupy and survive in the habitat based on their physiological tolerances and traits (Keddy, 1992 ; Woodward & Diament, 1991 ). Third, biotic interactions, such as trophic interactions, competition or facilitation, further shape community composition even among proximate sites with similar abiotic conditions (Kraft et al., 2015 ; Marteinsdóttir & Eriksson, 2014 ). Landscape heterogeneity depends on landscape configuration (the spatial arrangement of patches) and composition (the relative proportion of habitat types) (Duflot et al., 2022 ; Marini et al., 2022 ) and can strongly influence the dispersal filter. Indeed, species colonisation and individual movement depend on the connectivity between habitat patches (e.g., the distance separating them) and the permeability of the intervening matrix, which allows for a certain degree of dispersal (Dunning et al., 1992 ; Hodgson et al., 2011 ). However, as it is well established that larger habitat patches can support more species and individuals (i.e.: "species area relationship"; Gleason, 1922 ), theoretical studies have suggested that the amount of habitat in the surrounding landscape may be the most relevant metric to explain species richness, as it incorporates both patch size and connectivity (the “habitat amount hypothesis”; Fahrig, 2013 ). Moreover, landscape heterogeneity promotes β-diversity, and favours habitat complementation (i.e. species that need resources from different habitats throughout their life cycle (Dunning et al., 1992 ; Priyadarshana et al., 2024 ). Despite the well-documented benefits of these landscape ecology concepts, increasing landscape heterogeneity in production forests can be challenging because it implies increasing the diversity, in space and time, of stand composition (e.g. diversity of tree species and understorey vegetation), structure (e.g. diversity of tree age and diameter distribution) and management (e.g. rotation length, deadwood retention) (Duflot et al., 2022 ; Muys et al., 2022 ). These changes (e.g. replace production plantations with semi-natural forest to promote biodiversity) can be complex to implement in the absence of governance systems or incentives, particularly in areas composed of small private holdings (Jactel et al., 2009 ; Löfroth et al., 2024 ; Tiebel et al., 2022 ). Instead of altering the land use of existing patches, one can establish a new habitat at their interface, often referred to as interstitial habitats, linear elements, or boundary habitats (Hinsley & Bellamy, 2000 ; Holland, 2016 ). Examples include hedgerows, flower strips and grassy margins in agricultural landscapes or riparian forests and firebreaks in forest landscapes (Arroyo-Rodríguez et al., 2020 ; Holland, 2016 ; van Halder et al., 2007 ). Hedgerows between crop fields (sometimes referred to as “bocage landscapes”; Boinot et al., 2023 ) have been widely studied (Burel, 1996 ; Montgomery et al., 2020 ), and are considered a nature-based solution (Johnson et al., 2022 ) due to their role in conserving biodiversity, and more particularly natural enemies, which contribute to pest control in adjacent crops (Ferrante et al., 2024 ; Holland, 2016 ). Hedgerows constitute a suitable habitat for many plant and animal species (Boutin et al., 2002 ; Pywell 2005, de Zwaan 2024) and enhance connectivity by providing effective dispersal corridors for animal species (Boinot et al., 2023 ). Yet the potential of hedgerows in homogeneous monospecific forest landscapes remains underexplored. Most studies aiming to describe habitat or landscape effects on biodiversity have focused on a single taxonomic group using standard metrics (species richness, abundance and composition). Nevertheless, adopting a multi-taxonomic approach is particularly necessary to capture landscape-level processes that vary between and within taxa, depending on species’ home ranges and dispersal abilities (Allan et al., 2014 ; Fahrig, 2013 ). Furthermore, taxa often respond differently to landscape characteristics, so that studying one particular taxon does not allow to predict the responses of others (Barbaro et al., 2005 ). For example in forest plantation landscapes, Barbaro et al. ( 2005 ), found that bird species richness responded positively to landscape heterogeneity, whereas carabid species richness was negatively affected. Additionally few studies have examined habitat and landscape effects with both a multi-taxonomic perspective and a focus on dominant vs rare species or generalist vs specialist species (Rajaonarimalala et al., 2024 ; Soliveres et al., 2016 ). Dominant species, i.e. species that are abundant relative to others, exert proportionate effects on environmental conditions, community diversity and ecosystem functions (Avolio et al., 2019 ). On the other hand, rare species, which are both geographically limited and have small populations, are particularly threatened by human activities or environmental disturbances, but often possess particular functional traits linked with unique ecosystem functions (Avolio et al., 2019 ; Dee et al., 2019 ). While a previous study found contrasting effect of landscape composition on dominant and rare weed species (Dornelas et al., 2009 ), a gap remains in the literature regarding the effect of landscape heterogeneity on dominant and rare species, especially with a multi-taxa perspective. Additionally, accounting for generalist and specialist species is particularly relevant, as numerous studies have highlighted the widespread replacement of specialist species by generalist ones due to habitat loss, disturbances, or climate change (Clavel et al., 2011 ), leading to functional homogenization. While increasing the amount of habitat for specialist species is crucial for their conservation (Fahrig, 2013 ), few studies have explored the contrasting effects of implementing interstitial habitats on generalist versus specialist species (e.g., Batáry et al., ( 2012 ) for birds; Litza et al., ( 2022 ) for plants). The Landes de Gascogne Forest - the largest planted forest in Europe - offers an ideal setting to answer these questions. This forest is dominated by pure, even-aged maritime pine stands ( Pinus pinaster Ait). Within this pine plantation matrix, broadleaved remnants and riparian forests (subject to low-intensity management) still persist and serve as crucial refuges for biodiversity, particularly for forest specialist species that may not find suitable habitat in young pine plantations (Barbaro et al., 2005 ; Brockerhoff et al., 2008 ; van Halder et al., 2007 ). As the creation of new broadleaved stands is often undesirable for forest owners or logistically unfeasible, the establishment of broadleaved hedgerows (hereafter referred to as “hedgerows”) could represent a practical alternative to increase the overall amount of broadleaved habitat, enhance connectivity among existing broadleaved remnants, and offer complementary habitats for biodiversity. In the Landes de Gascogne forest, old broadleaved hedgerows are interspersed throughout the landscape. These hedgerows typically occur along the edge of pine plantations, adjacent to roads, forest tracks, or drainage ditches. They have been conserved for different reasons such as property boundaries, hunting, mushroom picking, firewood collection, or difficulties associated with harvesting wood along ditches (Plat et al., 2025 b). However, their role in biodiversity conservation has not yet been investigated in such a forest landscape context. In this study, we surveyed six different taxa and adopted a multi-taxonomic approach to assess biodiversity. We chose to compare biodiversity in broadleaved hedgerows with biodiversity in pine edges (i.e., the first lines of trees along a forest road) because: (1) they represent the part of the forest stand that has been replaced by a hedgerow, and (2) biodiversity often differs substantially between forest edges and interiors (Deconchat, 2014 ; Terraube et al., 2016 ). Additionally, to disentangle the effects of habitat amount and distance to the nearest neighbouring patch on biodiversity, we surveyed biodiversity in hedgerows that were either connected to or isolated from a broadleaved stand, while also accounting for the broadleaved stand cover in the surrounding landscape (Fahrig, 2013 ; Haddad et al., 2017 ). As community composition and species richness are known to vary between forest types (e.g., coniferous vs broadleaved stands; Ampoorter et al., 2020 ), shifts in dominant and rare species, as well as between generalist and forest species, may occur between hedgerows and pine edges. In addition, rare species, as well as forest species, may be more affected by intensive management of pine plantations and, conversely, favoured by the non-management of hedgerows. At the landscape scale, given that increased landscape heterogeneity tends to promote species turnover and supports greater diversity of locally adapted species (Dornelas et al., 2009 ; Fahrig et al., 2011 ), we expect a shift in community composition with increasing landscape heterogeneity, from communities with regionally dominant species associated with pine stands to more locally dominant species. Greater landscape heterogeneity may also benefit rare species and forest species (Dornelas et al., 2009 , van Halder 2007). Finally, although European forest ecosystems are considered the last refuge for many endangered species, further measures are needed to investigate how remnants of sub-natural woodlands such as broadleaved hedgerows and stands can contribute to their protection (Muys et al., 2022 ). More particularly, by surveying the biodiversity of hedgerows and pine edges, accounting for their isolation and for the broadleaved cover in their surrounding landscape, we tested the following hypotheses: H1) The species richness of each taxon and the multi-taxonomic diversity are higher in hedgerows than in pine edges particularly when hedgerows are connected to a broadleaved stand (i.e. isolated vs connected hedgerows). Additionally, biodiversity is higher in sites located in a landscape with a high cover of broadleaved stands compared to those with a low broadleaved cover. H2) Community composition of the studied taxa differs between pine edges, isolated hedgerows and connected hedgerows, as well as between landscapes with high and low broadleaved cover. H3) Considering all taxonomic groups, rare species, locally dominant and forest species occur more frequently in hedgerows than in pine edges, and more frequently in sites located in a landscape with a high cover of broadleaved stands compared to those with a low broadleaved cover. The opposing results should be observed for the regionally dominant species (i.e. they are expected to be more frequent at pine stand edges and in landscapes with a low broadleaved cover). Methods Study region Located in south-western France, the Landes de Gascogne Forest covers approximately 1.16 million hectares and is predominantly composed of pure stands of maritime pine ( P. pinaster ) (Barbaro et al., 2005 ). These plantations are intensively managed, with soil preparation and thinning operations before clear-cut harvesting at 30 to 40 years, creating temporary open areas across the landscape. Within this homogeneous landscape matrix, a limited number of unmanaged riparian forests (dominated by Alnus glutinosa L. and Quercus robur L.) as well as low-intensity managed stands of native oaks (primarily Q. robur and Q. pyrenaica Willd.) persist (Mora et al., 2012 ). Experimental design For the purpose of the study, the Living Lab “Forest Bocage” ( https://www.plantedforests.org/fr/infrastructures/superb-bocage-forestier/ ) was created in the Gironde district, in the Landes de Gascogne forest (50,000 ha area, barycenter coordinates: X: −0.776865; Y: 44.560623). In the Living Lab area, we used high-resolution infrared colour orthophotographs (IGN; 20 cm pixel resolution, https://geoservices.ign.fr/documentation/donnees/ortho/bdortho ) analysed at a 1:2500 scale in a GIS environment to identify (1) broadleaved hedgerows and (2) broadleaved and mixed forest stands. Then, we selected 36 study sites according to factorial design with two factors following the recommendation from Fahrig, 2013 . Factors included (1) the type of habitat with three modalities (pine plantation edge, connected hedgerow or isolated hedgerow), (2) landscape composition with two modalities (low or high amount of broadleaved stands in the landscape). This resulted in 6 combinations with 6 replicates (Fig. 1 ). Sampled hedgerows and pine edges were 100 to 325 m in length. Pine edges were at least 6 m in height and adjacent to a forest road, representing the control treatment. Hedgerows were defined based on field observations as linear formations of broadleaved trees at least 8 meters in height, typically one or two trees wide, with continuous canopies, and dominated by native oak species ( Q. robur and Q. pyrenaica ). Only hedgerows in between two pine plantations were sampled, with an average distance of 26 m between the two pine stands. Connected hedgerows were directly adjacent at one end to a broadleaved stand (i.e., distance to the nearest natural habitat patch = 0 m). Isolated hedgerows and pine edges with no hedgerow were at least 100 m distant from a broadleaved stand (i.e., distance to the nearest natural habitat patch > 100 m) and 50 m from other hedgerows. These distance thresholds were selected to assess the influence of habitat connectivity on multi-taxonomic biodiversity, particularly for organisms with limited dispersal abilities such as ground beetles and spiders. However, they may represent relatively short distances for taxa with greater dispersal capacity, such as birds (Fahrig, 2013 ; Slagsvold et al., 2013 ). Nevertheless, these distances were chosen as a compromise, given the difficulty in identifying truly isolated hedgerows in landscapes with high broadleaved cover due to (1) the high density of hedgerows, and (2) the limited availability of landscapes with high broadleaved cover. The percentage of broadleaved forest cover in the surrounding landscape (hereafter “broadleaved cover") was quantified within a 500 m buffer around the sampled hedgerows or pine stand edges, at each study site. Two classes of broadleaved cover were selected: low (0–6%; n = 18) and high (14–36%; n = 18). The 36 sampled sites were evenly distributed across the study area, with an average inter-site distance of 1.7 km (range: 0.7–3.9 km) to minimize spatial autocorrelation (see the map of the Living Lab: Fig. S1 ). Multi-taxonomic biodiversity survey We selected six different taxonomic groups, ranging from primary producers to secondary predators, in order to cover a wide range of dispersal abilities and biotic interactions. Additionally, all the groups studied are known to be sensitive either to forest tree composition, landscape composition or to hedgerows in agricultural landscapes (Ampoorter et al., 2020 ; Montgomery et al., 2020 ). Understorey vegetation All vascular plant species were recorded along a 50 × 1.5 m transect positioned within the hedgerow or the first line of trees for pine edges. All species present in the transect were classified into two vertical strata: the herbaceous layer, comprising herbaceous species and ligneous species under 0.5 m in height, and the shrub layer, including only ligneous species and climbers (vines) between 0.5 and 5 m in height (Canullo et al., 2011 ). To estimate plant species cover, three 5 × 1.5 m quadrats were placed within this the transect. Two pairs of trained observers (JD/TB and IVH/NP) independently assessed species cover within each stratum using the Domin scale (Rich et al., 2005 ). Understorey vegetation was assessed in June 2023 to capture nearly the entire local species pool. Species identification was conducted using a taxonomic guide (Rameau et al., 2018 ), and complex identifications were verified in the laboratory under a binocular magnifier. Following Corcket et al. ( 2020 ), Domin scale values were converted to mean percentage cover, and these values were then averaged across the three quadrats to obtain a cover value per species per site. For ligneous species present in both strata, cover values were summed. Species observed within the transect but absent from the quadrats were assigned the lowest mean cover value (0.033%). Seedlings and saplings from Q.robur , Q.pyrenaica and P.pinaster were removed from analysis as study sites were selected to be dominated by trees of these species. Butterflies Butterflies were surveyed using the line-transect method (Pollard & Yates, 1993 ). At each site, one trained observer (IVH) walked along a 100 m transect (either along the hedgerow or the pine edge), recording all butterflies observed within 2.5 m on either side of the transect line and up to 5 m ahead. Individuals were identified visually or, when necessary, captured with a net and released after identification. The transect was walked in both directions, but only the maximum number of individuals per species observed over one 100 m transect was retained for analysis to avoid double counting. Each of the 36 study sites was visited three times, once a month, between May and July 2023, which allowed observing different individuals and species. Surveys were carried out between 10:00 and 18:00, and only under suitable weather conditions (temperature > 20°C, low cloud cover, and wind speed < 30 km/h). The order of site visits was randomized across the three survey periods. For each site, the total number of individuals per species was summed across the three visits and used in statistical analyses. Carabid beetles and ground dwelling spiders Carabid beetles (here after “carabids”) and ground-dwelling spiders (hereafter “spiders”) were sampled using pitfall traps (Brown & Matthews, 2016 ). Each trap consisted of a glass jar (90 mm in diameter, 100 mm in height, 445 mL volume) filled with a mixture of propylene glycol and water, and covered with a plastic rain guard. At each site, three traps were placed along the hedgerow: one at the center and two others located 25 m away on either side. Traps were active during three sampling periods: April, June, and July 2023, each lasting two weeks, for a total of 42 trapping days. Collected arthropods were stored in a deep freezer prior to identification. Carabids were identified to the species level by an expert (SJ), using external morphological discriminating characters as well as genitalia and aedeagus examinations if necessary (Janovska et al., 2013 ). Reference books were those for the French carabids fauna (Coulon et al., 2011 ; Jeannel, 1941 ), and the European fauna (Hurka, 1996 ; Trautner & Geigenmüller, 1987 ). Spiders were identified to the species level by two experts (OB and SJ), using one taxonomic book (Roberts, 1985 ) and two taxonomic websites for spiders for France and Belgium ( https://arachno.piwigo.com/ ) and for Europe ( https://araneae.nmbe.ch/ ). Juvenile spiders were excluded from the analysis due to identification difficulties. Captures from the three seasons were pooled to obtain a species richness value per site. Although the number of individuals captured per species represents an activity density metric, it was used as an acceptable proxy of abundance (Chaladze, 2020 ) for codominance analysis. Birds Bird communities were surveyed using passive acoustic monitoring, combined with expert identification of species-specific calls and songs (Schillé et al., 2024 ). In April 2023, one Song Meter Mini Bat recorder (Wildlife Acoustics), equipped with an omnidirectional microphone, was installed in each of the 36 study sites. Devices were installed at a height of 2 meters on pine trees located along the pine edge, either adjacent or not to hedgerows. Recorders operated for five consecutive days, recording one-minute audio file every three minutes from sunrise to sunset. Within a period of two days with no rain and low wind speed (< 30 km/h), a subset of five one-minute recordings was extracted between 08:30 and 08:45 am. This time period was selected as it corresponds to 1.5 hour after sunrise, matching the peak bird vocal activity. This resulted in a total of 360 audio files of one-minute (5 recordings × 2 days × 36 sites), corresponding to six hours of acoustic data. Expert ornithologists (YC, MS, and IGC) listened at each recording to identify all bird species present based on their vocal activity. For each site, the number of files in which a given species was detected (ranging from 0 to 10) was used as a proxy for bird vocal activity (Schillé et al., 2024 ). Although individual birds may have been recorded multiple times, vocal activity was considered a reliable indicator of relative abundance and used for subsequent codominance analyses. Reptiles Reptiles were surveyed using a combination of active visual search and artificial refuge methods to optimize detection efficiency (Michael et al., 2012 ; Pottier, 2023 ). In January 2023, four artificial refuges (asphalt roofing sheets) were installed at 20 m intervals along a 100 m transect, placed either along the hedgerow or the pine edge. Two pairs of trained observers (JBR/TB and OB/NP) conducted visual surveys along the transect, recording all reptiles observed within the hedgerow or pine edge, including individuals located on or beneath the artificial refuges. Each of the 36 study sites was surveyed five times, once per month from April to October 2023, excluding July and August due to excessively high temperatures. Surveys were conducted between 09:00 and 13:00, which corresponds to the period during which reptiles remain in the sun for thermoregulation. Rainy days and those with wind speed exceeding 30 km/h were avoided. The order of site visits was randomized across the five survey periods. As individuals were not marked, for each site, the maximum number of individuals per species observed during one visit was used as an abundance metric in codominance analyses. Dominant and rare species evaluation Numerous metrics have been developed to identify dominant and rare species, but most were designed for plant communities (Avolio et al., 2019 ; Dee et al., 2019 ). To our knowledge, only two studies have investigated species dominance and rarity using a multi-taxonomic framework (Allan et al., 2014 ; Soliveres et al., 2016 ). However, both relied on arbitrary thresholds to define dominant (e.g., top 10% most abundant) and rare species (e.g., bottom 50–90% less abundant). In contrast, we applied the codominance approach proposed by Gray et al. ( 2021 ), which (1) more accurately captures dominance patterns by identifying, for each taxon and community, the optimal number of codominant species based on their relative abundances, and (2) can be applied across taxa regardless of differences in abundance metrics. The approach selects subsets of the most abundant species and calculates a harmonic mean of their relative abundances, favouring subsets where species have similar abundances. This shared abundance is then contrasted with that of the next most abundant species to compute a codominance index. The optimal codominant subset is the one that maximizes both internal similarity and contrast with subordinate species, ultimately yielding the number of codominant species in the community (see Gray et al., 2021 for details). This method also allowed to detect mono-dominated communities, when the ratio between the first and second species in terms of abundance is higher than 3. The codominance metric was calculated for communities of each taxonomic group at two spatial scales: (i) at the local level, for each of the 36 study sites independently, and (ii) at the regional level, by aggregating species abundances across all 36 sites. This method allowed us to classify species within each taxonomic group into 3 categories: (1) locally dominant species, defined as codominant in at least one site, (2) regionally dominant species, codominant in the entire dataset and (3) rare species, never identified as codominant at the local or regional level. We assessed local dominance only in communities with more than 20 individuals. Consequently, no reptile species were classified as locally abundant. At the local level, in a few cases, no clear pattern of codominance emerged; in these cases, no species were classified as locally dominant. The complete classification of species according to dominance status is provided in Table S2. Notably, between 1 and 5 species per group were classified as regionally dominant, and these species were always locally dominant in at least one site. This approach also allowed the classification of between 5 and 16 species as locally dominant. Forest specialist species identification For each taxonomic group, we identified species considered as forest specialists at the French or European scale. As classification methods differ among groups, we applied group-specific criteria designed to consistently exclude generalist species and retain only those with a strong forest affinity (hereafter referred to as “forest species”). For understorey vegetation, we used the European Forest Plant Species List (Heinken et al., 2022 ). Using data specific to the Atlantic region of France and following the same methodology as Litza et al. ( 2022 ), we selected species classified in the first two categories: (1.1) taxa primarily found in closed forest habitats, and (1.2) taxa typically associated with forest edges and open forest conditions. Forest butterflies were identified using the European classification of butterfly species by biotope (van Swaay et al., 2006 ). For carabid beetles, we adopted the classification method used by Jouveau et al. ( 2022 ) in the same study region, classifying species into three habitat classes: forest specialists, generalists, and open-habitat specialists. For species not covered in Jouveau et al. ( 2022 ), we supplemented the habitat information using the publications of Murdoch ( 1967 ) and Jaskula & Soszyńska-Maj ( 2011 ). Spiders were classified using the World Spider Trait database (Pekár et al., 2021 ), focusing on the "Light 2" trait, which categorizes species based on their light preference (Buchar et al., 2020 ). Values for this trait range from 1 (species of open habitats) to 5 (species of dark habitats). Species with a score > 3.0, corresponding to a preference for shaded or dark environments, were classified as forest dwellers. When a species had multiple habitat associations in the database, we used the mean score above 3 as threshold for classifying it as forest-associated. Forest bird species were identified based on the STOC report (Fontaine et al., 2019 ). This French national monitoring program classifies birds as forest species when they occur significantly more often in 1 km² areas dominated by forest cover compared to areas dominated by agricultural or urban land uses, based on 30 years of national survey data. Reptiles were excluded from the analysis, as only five species were present in the dataset, all being classified as generalist species (INPN; https://inpn.mnhn.fr ). Red List species identification The conservation status at the European level was checked for every species according to the IUCN Red list ( https://www.iucnredlist.org/ , 04-2025). 47 understorey plant species were classified as NA as they were either data deficient species (n = 39), invasive species (n = 5) or species identified at genus level only (n = 3). As spiders are not included in the IUCN European Red list, we used the red List of French spiders ( https://uicn.fr/wp-content/uploads/2023/03/liste-rouge-araignees-de-france-metropolitaine.pdf ). There was no red list available for carabids at the European or French level. Data analysis To integrate the responses of all taxonomic groups into a single biodiversity metric, we computed a multidiversity index following the method proposed by Allan et al. ( 2014 ). This index is used to weigh taxa so they have the same importance. It was calculated by scaling the species richness of each group by its maximum observed value across the 36 study sites. The standardized richness values were then averaged across all six taxonomic groups to obtain one multidiversity value per site. To evaluate the effect of broadleaved cover on biodiversity, since the spatial scale at which landscape composition influences the presence of species can vary depending on both species' movement ranges and the ecological context (Fahrig, 2013 ), we assessed the relevance of the 500 m buffer size through a model-based approach. Specifically, we constructed 10 linear models using multidiversity as a composite biodiversity response variable, with broadleaved cover measured at buffer distances ranging from 100 m to 1000 m in 100 m increments. We then plotted the R² values of these models against buffer size. The highest R² values were observed for buffers between 400 m and 1000 m. Moreover, broadleaved cover estimates were highly correlated across this range, supporting the choice of a 500 m buffer as a representative and ecologically meaningful scale (Fig S2, Table S1 ). In all analyses and in line with our study design, broadleaved cover was treated as a quantitative variable with two classes (low or high), rather than as a continuous gradient, because its range in our study area was limited (0–36%) and values were not evenly distributed along the gradient. We used the following statistical models to test our hypotheses: H1: To assess the effects of habitat type and broadleaved cover on multi-taxonomic biodiversity, we first used the species richness of each taxonomic group separately using Generalized Linear Models (GLMs) with a Poisson distribution, appropriate for count data. Second, multidiversity was used as a response variable in a Linear Model (LM). H2: To examine the influence of habitat type and broadleaved cover on community composition of each taxonomic group, we performed Permutational Multivariate Analysis of Variance (PerMANOVA) using a Bray–Curtis dissimilarity matrix, appropriate for ecological community data. Species abundance data were Hellinger-transformed to reduce the disproportionate influence of dominant species (Legendre & Gallagher, 2001). PerMANOVAs were performed on the full dataset for each taxonomic group to assess differences between landscapes with low and high broadleaved cover categories, and on sub-datasets including only two of the three habitat types (pine edge, connected hedgerows and isolated hedgerows), to compare community composition between each pair of habitat types. Principal Coordinate Analysis (PCoA) was used to visualize patterns in species composition. For reptiles, the results must be interpreted with caution due to the low number of species and individuals. H3: To test the effect of habitat type and broadleaved cover on dominant species (local or regional level), rare species, and forest species, we calculated additional multidiversity indexes based on different subsets of the dataset (i.e. multidiversity of local dominance, multidiversity of regional dominance, multidiversity of rare species and multidiversity of forest species). Reptiles were excluded from both the local dominance and forest analyses, as no species in this group met the respective criteria. Multidiversity indexes served as response variables in separate LMs. For GLMs and LMs, the interaction between habitat and broadleaved cover was tested and retained only when statistically significant. Model assumptions of residual normality and homoscedasticity were assessed graphically. We checked for spatial autocorrelation of the residuals of each model using Moran’s I (4-nearest neighbours) across the 36 sites and found no significant effect. Post hoc comparisons between habitat types were conducted using Tukey’s HSD tests. All statistical analyses were conducted in R version 4.4.0 (R Core Team, 2016), using the following packages: lmerTest for GLMs and LMs, vegan (function adonis2 ) for PerMANOVA, emmeans for post hoc comparisons and spdep for Moran’s I values. Results Based on the multi-taxonomic surveys of 6 taxonomic groups in the 36 study sites, we identified a total of 279 species including 79 understorey plants (28%), 108 spiders (39%), 41 birds (14%), 30 butterflies (11%), 16 carabids (6%) and 5 reptiles (2%) (Species list available in Table S2). H1) Effect of habitat and broadleaved cover on species richness and multidiversity Among the 6 taxonomic groups surveyed, understorey plants were the only one to respond to habitat and landscape modalities (Fig. 2 , Table 1 , R²=0.47). Species richness of understorey plants was significantly higher in connected hedgerows and isolated hedgerows than in pine edges. In addition, the species richness of understorey plants was significantly higher in sites in a landscape with a low broadleaved cover compared with high broadleaved cover. The species richness of the five other groups showed no significant difference between habitat or landscape modalities (Fig. 2 , Table 1 ). Table 1 Summary of GLMs and LMs testing the effect of habitat and broadleaved cover in the landscape on the species richness of each group (H1) and on multidiversity indexes (H1-H3-H4). For each model, low broadleaved cover in the landscape is the reference modality compared to high broadleaved cover. When the habitat variable was significant, a Tukey’s post-Hoc was applied to show differences between modalities (P = pine edge, IH = isolated hedgerow, CH = connected hedgerow). For each model, interactions were removed because not significant, except for H3 with multidiversity of local dominance (Tukey’s post-Hoc results are visible in Fig. 3 ). Significant variables are indicated in bold. Sample size was n = 36 sites. H Response variable Predictors Tukey’s post-Hoc x2 Df Coeff. ± SE P R² adj H1 Species Richness of understorey vegetation Habitat 18.9 2 < 0.001 0.47 P - IH -0.35 ± 0.11 0.006 P- CH -0.47 ± 0.11 < 0.001 IH - CH -0.11 ± 0.10 0.501 High broadleaved cover 3.91 1 -0.17 ± 0.09 0.048 H1 Species Richness of butterflies Habitat 1.19 2 0.551 0.09 High broadleaved cover 2.20 1 -0.22 ± 0.15 0.139 H1 Species Richness of carabids Habitat 1.08 2 0.581 0.04 High broadleaved cover 0.21 1 -0.08 ± 0.18 0.649 H1 Species Richness of spiders Habitat 0.27 2 0.874 0.01 High broadleaved cover 1.11 1 -0.09 ± 0.08 0.291 H1 Species Richness of birds Habitat 1.14 2 0.567 0.04 High broadleaved cover 0.34 1 0.06 ± 0.11 0.560 H1 Species Richness of reptiles Habitat 1.73 2 0.420 0.08 High broadleaved cover 1.24 1 -0.31 ± 0.28 0.269 H1 Multidiversity Habitat 0.06 2 0.016 0.29 P - IH -0.09 ± 0.03 0.023 P- CH -0.08 ± 0.03 0.043 IH - CH 0.01 ± 0.03 0.964 High broadleaved cover 0.05 1 -0.07 ± 0.03 0.010 H3 Multidiversity of local dominance Habitat 0.02 2 0.427 0.04 High broadleaved cover 0.03 1 -0.05 ± 0.03 0.102 H3 Multidiversity of regional dominance Habitat 0.01 2 0.119 0.02 High broadleaved cover 0.03 1 -0.06 ± 0.04 0.102 H3 Multidiversity of rare species Habitat 0.10 2 0.175 0.18 P - IH -0.10 ± 0.05 0.049 P- CH -0.11 ± 0.05 0.047 IH - CH -0.01 ± 0.05 0.996 High broadleaved cover 0.03 1 -0.06 ± 0.04 0.101 H4 Multidiversity of forest species Habitat 0.23 2 < 0.001 0.48 P - IH -0.15 ± 0.03 0.004 P- CH -0.19 ± 0.03 < 0.001 IH - CH -0.04 ± 0.03 0.542 High broadleaved cover 0.02 1 0.04 ± 0.03 0.126 When all groups were combined into one index, multidiversity was significantly higher in connected hedgerows and isolated hedgerows than in pine edges. Multidiversity was also significantly higher in low broadleaved cover landscapes than in high broadleaved cover landscapes (Fig. 3 A, Table 1 , R²=0.29). H2) Effect of habitat and broadleaved cover on community composition Each of the three habitat types (i.e. pine edge, isolated hedgerow and connected hedgerow) hosted unique species of the different taxonomic groups. This was especially visible for understorey plants with 24 species identified only in connected hedgerows, 8 in isolated hedgerows and 5 in pine edges (see Venn diagrams in Fig. 4 for details). Habitat type had a taxon-dependent effect on community composition (Fig. 4 , Table 2 ). For understorey vegetation and spiders, pine edges hosted distinct communities compared to both isolated and connected hedgerows, while communities in the two types of hedgerows were similar. For butterflies and birds, only the communities in connected hedgerows differed significantly from those in pine edges, while communities in isolated hedgerows did not significantly differ from either. Carabid and reptile communities were not significantly affected by the type of habitat. Table 2 Summary of PerMANOVA testing the effect of habitat and the broadleaved cover in the landscape on the community composition of each group. Community are based on a Bray Curtis dissimilarity matrix with Hellinger transformation. For broadleaved cover, the test was applied to the complete dataset (n = 36). For habitat, the test applied for each pair of modalities (n = 24; P = pine edge, IH = isolated hedgerow, CH = connected hedgerow). Significant variables are indicated in bold. Group Predictors Modalities F P R² adj Understorey vegetation Habitat P - IH 5.32 < 0.001 0.19 P- CH 4.69 0.004 0.18 IH - CH 0.51 0.860 0.22 Broadleaved cover Low - High 2.37 0.039 0.06 Butterflies Habitat P - IH 1.33 0.220 0.06 P- CH 2.09 0.040 0.09 IH - CH 0.66 0.680 0.03 Broadleaved cover Low - High 3.41 < 0.001 0.09 Carabids Habitat P - IH 1.81 0.102 0.08 P- CH 1.41 0.168 0.06 IH - CH 0.90 0.552 0.04 Broadleaved cover Low - High 1.51 0.156 0.04 Spiders Habitat P - IH 3.43 < 0.001 0.13 P- CH 3.87 0.002 0.15 IH - CH 0.58 0.896 0.03 Broadleaved cover Low - High 1.39 0.130 0.04 Birds Habitat P - IH 1.48 0.131 0.06 P- CH 2.22 0.009 0.09 IH - CH 0.43 0.937 0.02 Broadleaved cover Low - High 1.52 0.118 0.04 Reptiles Habitat P - IH 2.13 0.158 0.09 P- CH 3.06 0.103 0.13 IH - CH -0.04 0.894 0.01 Broadleaved cover Low - High 3.85 0.045 0.11 Statements and Declarations While the number of species exclusively present in sites within landscapes of low versus high broadleaved cover was similar, these two types of sites hosted significantly different communities for understorey vegetation, butterflies, and reptiles, whereas spider, carabid, and bird communities showed no significant differences (Fig. S3, Table 2 ). H3) Effect of habitat and broadleaved cover on dominant, rare and forest species As expected, multidiversity (including all species) was positively correlated with the multidiversity of regionally -dominant species (P < 0.05; R² = 0.10), the multidiversity of locally dominant species (P < 0.001; R² = 0.38), and the multidiversity of rare species (P < 0.001; R² = 0.64; Pearson correlations, data not shown), as these metrics were calculated from subsets of the overall biodiversity data. The multidiversity of rare species was significantly higher in hedgerows than in pine edges, whereas broadleaved cover did not influence this metric. Neither habitat type nor broadleaved cover had a significant effect on the multidiversity of regionally -dominant or locally dominant species (Fig. 3 , Table 1 ). Among the 279 species identified in our study, our method enabled us to classify 60 of them as forest species: 18/89 plant species (20%), 5/30 butterfly species (17%), 7/16 carabid species (44%), 17/108 spider species (16%) and 13/41 bird species (32%). Multidiversity of forest species was significantly higher in connected hedgerows and isolated hedgerows than in pine edges. Multidiversity of forest species was also slightly higher in high broadleaved cover landscapes than in low broadleaved cover landscapes although this difference was not significant (Fig. 3 E, Table 1 , R²=0.48). No clear relationship was observed between forest species and their dominance classification, as 4/14 (28%) were regionally dominant, 12/58 (21%) were locally dominant and 36/207 (17%) were rare (Table S2). Red list species One endangered spider species ( Dysdera fuscipes ) was identified in two sites that were both hedgerows in a high broadleaved cover landscape. Three vulnerable species were identified. One bird species, the European turtledove - Streptopelia turtur , was present in one site, a pine edge within a high broadleaved cover landscape. One butterfly species, the small skipper - Thymelicus sylvestris , was present in two sites, one hedgerow in high broadleaved cover landscape and one pine edge in a low broadleaved cover landscape. One reptile species, the aspic viper - Vipera aspis , was present in one site, a hedgerow in a low broadleaved cover landscape (Table S2). Discussion By sampling species belonging to six different taxa in 36 sites in pine plantation landscapes, we found that broadleaved hedgerows harbour greater multi-taxonomic biodiversity than the edges of pine stands. Community composition in hedgerows differed markedly from that in pine edges across most taxonomic groups, highlighting the complementary role of hedgerows in preserving biodiversity. Especially, we demonstrated that hedgerows are particularly relevant for the conservation of forest specialist species and rare species. In contrast, the connectivity of hedgerows to broadleaved stands had almost no effect on overall multi-taxonomic biodiversity. Effect of habitat type on biodiversity Our results demonstrate that hedgerows, which are rare and small sized interstitial habitats in the landscape, support a higher multidiversity than pine plantation edges and harbour distinct communities for most taxa compared to pine plantation edges, which are much more abundant in the forest landscape. These differences in biodiversity between the two habitat types can be explained by biotic and abiotic factors associated with broadleaved or coniferous trees and by the disturbance regime resulting from the intensive management of pine plantations compared with unmanaged hedgerows (Brockerhoff et al., 2008 ; Kennedy & Southwood, 1984 ; Willson & Comet, 1996 ). It has been shown that broadleaved trees harbour more species of herbivorous insects than conifers, which in turn can increase the diversity of predators (Brändle & Brandl, 2001 ; Kennedy & Southwood, 1984 ). Tree species composition, particularly the contrast between broadleaved and coniferous species, also affects the quantity and quality of leaf litter, which in turn influences the soil chemical properties and decomposition rates. This has an impact on organisms that are dependent on soil and litter properties (plants, ground dwelling beetles and spiders ; Augusto et al., 2002 ; Pywell et al., 2005 ; Scherer-Lorenzen et al., 2007 ). Differences in canopy structure driven by tree composition also modify the microclimatic conditions of the understory, including light availability, temperature, and humidity, which affect the diversity of understorey plants (Christiansen et al., 2022 ; Corcket et al., 2020 ). Moreover, pine plantations are subject to intensive silvicultural practices, such as mechanical weeding stand thinning every 10 years, and clear-cutting every 30–40 years (Mora et al., 2012 ), which can significantly alter community composition compared to unmanaged hedgerows (Brockerhoff et al., 2008 ). Additionally, hedgerows may act as refuges for species following disturbances in pine stands, as has been observed for several arthropod groups in agricultural landscapes after crop harvesting (Montgomery et al., 2020 ; Pywell et al., 2005 ). In our study, multidiversity was significantly higher in hedgerows compared to pine edges. Since each taxonomic group was weighted equally in the multidiversity index, this approach allowed us to aggregate the effect of hedgerows across all groups, which was always positive for each group taken individually, even if sometimes small and not statistically significant. This confirms an overall greater biodiversity in hedgerow habitats than in pine plantations. In contrast, a previous study using the same index in forest environments found no effect of tree composition on multidiversity, mainly due to opposing (positive and negative) responses among the different groups studied (Leidinger et al. 2021 ). This finding highlights the interest of multi-taxa approaches to identify habitat conditions that optimize overall biodiversity (Allan et al., 2014 ; Ampoorter et al., 2020 ). The weaker response of some groups could be explained by their position in the food chain. Indeed, differences in tree composition and management between habitat type result in forest stands with different structures, which have been shown to directly influence biodiversity of lower trophic levels (i.e., primary producers and primary consumers), as these groups are more closely associated with the trees (Ampoorter et al., 2020 ; Scherber et al., 2010 ). This was supported by our results, as understory vegetation was the only taxonomic group to exhibit both higher species richness in hedgerows and a distinct community composition compared to pine edges. Following a similar pattern, butterfly communities were also influenced by habitat type, likely due to their dependence on understory vegetation for host plants and floral resources (Ouin & Burel, 2002 ; van Halder et al., 2007 ). In contrast, the effect of habitat on secondary consumers (i.e. predators) varied, with no consistent pattern across groups. The absence of a significant response of carabids to habitat type is consistent with previous studies in the same study region, which found no significant effect of tree species composition on carabid richness, as most are generalist predators and mobile species (Barbaro et al., 2005 ; Jouveau et al., 2020 ). In contrast, hunting spider communities, known to benefit from hedgerows and riparian forests in agricultural landscapes (Buddle et al., 2004 ; Kratschmer et al., 2024 ), were strongly affected by habitat type. This may be attributed to the greater food availability in hedgerows and the lower dispersal capacity of ground dwelling spiders compared to carabids (Feber et al., 2015 ; Kennedy & Southwood, 1984 ). Although bird species richness did not differ significantly between hedgerows and pine edges, bird community composition differed between the two habitat types, in line with previous studies comparing coniferous and broadleaved stands (Willson & Comet, 1996 ). Similar results have been observed in oil palm plantations where interstitial riparian forests shelter a different bird community (Mitchell et al., 2018 ). Finally, the lack of a clear response from reptiles to hedgerows, in contrast to findings in agricultural landscapes (Lourdais et al., 2025 ), could be due to their preference for forest edges that provide higher light exposure (Duchesne et al., 2023 ). Furthermore, the richness and abundance of reptile species are low in the study area, requiring a longer period of study to observe sufficient numbers of individuals. Overall, hedgerows benefited several taxonomic groups and consistently maintained diversity levels at least as high as those observed along pine edges, highlighting their positive contribution to biodiversity. Our results not only demonstrated that community composition differed between habitat types, but also highlighted the capacity of hedgerows to shelter more forest species across all taxonomic groups, in contrast to pine plantation edges. A similar pattern was observed in Madagascar, where the effect of tree species richness was stronger on endemic multidiversity than on general multidiversity (Rajaonarimalala et al., 2024 ). These findings are also consistent with those of van Halder et al. ( 2007 ), who worked in the same region than us, and reported that butterfly communities differed between pine and broadleaved stands, with a greater number of forest butterfly species found in broadleaved stands. In general, forest specialists typically benefit from the conditions found in older forests, which are characterized by different abiotic conditions as well as greater accumulation of deadwood, more diverse microhabitats and a well-developed vertical structure (Brockerhoff et al., 2008 ; Muys et al., 2022 ). As such, unmanaged hedgerows likely offer more favorable conditions for these forest species than intensively managed pine forests. Although our study did not include broadleaved forest stands as a sampled habitat, it is reasonable to infer that hedgerows may shelter a subset of species typically found in these forests, since hedgerows are interstitial habitats that cannot fully reproduce abiotic conditions of forest interiors (Collard et al., 2025 ; Litza & Diekmann, 2019 ). For example, van Halder et al. ( 2007 ) recorded 34 butterfly species (including five forest specialists) in broadleaved stands within the same region. Of these, 22 species (65%) were also observed in the hedgerows that we sampled, including four of the five forest specialists ( Argynnis paphia, Limenitis reducta, Pararge aegeria, Quercusia quercus ), as well as one additional forest specialist ( Satyrium ilicis ) not previously recorded in the broadleaved stands. Future studies should consider hedgerow characteristics such as age, structure, and volume, as these variables have been shown to enhance the capacity of hedgerows to replicate forest interior conditions and support forest species in agricultural landscapes (Kratschmer et al., 2024 ; Litza et al., 2022 ). By partitioning our species data into dominant and rare species, we found that rare species were more numerous in hedgerows than along pine edges, consistent with the pattern observed for overall multidiversity. In contrast, dominant species (both at the regional and local level) were not significantly affected by the presence of hedgerows. Given the well-established sensitivity of rare species to land-use intensity in agricultural systems (Allan et al., 2014 ), this effect is likely related to the intensive management of pine stands compared to the lack of management in hedgerows. Moreover, broadleaved hedgerows probably offer more ecological niches for biodiversity than coniferous plantations (Brändle & Brandl, 2001 ), resulting in a greater capacity to preserve rare species. Future studies in forests should examine the respective contributions of rare and dominant species across several taxa to ecosystem functioning, as it has been shown only for birds that rare species contribute the most to functional diversity (dos Anjos et al., 2023 ; Leitão et al., 2016 ). These findings also suggest that rare species may serve as useful indicators of forest management intensity. Effect of landscape on biodiversity Landscape-scale variables showed contrasting effects on multi-taxonomic diversity. Notably, change in landscape composition, through the increase in broadleaved cover in the landscape, was associated with a decrease in multidiversity. This finding contrasts with predictions from the habitat amount hypothesis, which suggests that increasing the amount of suitable habitat should enhance species richness (Fahrig, 2013 ). It also contradicts recent global meta-analyses conducted in agricultural landscapes, which found that both semi-natural habitat cover and crop heterogeneity positively influence biodiversity across most taxa (Priyadarshana et al., 2024 ). While literature reviews have proposed that these concepts can also apply to forest ecosystems, through variation in tree species composition and forest management regimes (Duflot et al., 2022 ; Muys et al., 2022 ), our findings suggest that the relationship may be more complex. One explanation, as proposed by Fahrig ( 2013 ), is that the habitat amount hypothesis is most relevant when considering species specialized on a single habitat type, rather than total biodiversity across a range of taxa. Consistent with this prediction, when we restricted the multidiversity to forest species only, we observed higher values in landscapes with a higher broadleaved cover although this difference was not statistically significant. The general absence of landscape-level effects may be due to the nature of the surrounding matrix, as pine plantations form a suitable habitat for a certain number of species and represent a more permeable matrix for species movement and colonization than crops do in agricultural landscapes (Brockerhoff et al., 2008 ). Moreover, the range of broadleaved cover considered “high” in our study (14–36%) remains below the 40% threshold recommended in the review from Arroyo-Rodríguez et al. ( 2020 ) for maintaining forest-dwelling species in the landscape. This limitation could have constrained our ability to detect stronger landscape effects. Finally, the unexpected negative relationship between broadleaved cover and multidiversity may be explained by the functional role of hedgerows. As hedgerows represent a sub-optimal habitat compared to broadleaved stands, particularly for forest species, it is possible that species rely on hedgerows in landscapes with low broadleaved cover, but shift their use toward more suitable forest habitats when these are available in greater quantity. A similar pattern has been reported for birds in agricultural landscapes, where hedgerows serve as substitute habitats in more simplified landscapes but become less critical in more forested ones (Hinsley & Bellamy, 2000 ). Nevertheless, three out of the six taxonomic groups examined (understory vegetation, butterflies and reptiles) showed differences in community composition between landscapes with low vs high broadleaved cover. This aligns with general trends observed in agricultural landscapes, where increasing landscape heterogeneity through the addition of semi-natural habitats often leads to changes in species composition (e.g., plants: Cursach et al., 2020 ; butterflies: Perović et al., 2015 . 2015; carabids: Vanbergen et al., 2005 ). In contrast, the spatial connectivity of hedgerows to the nearest broadleaved forest stand, measured as the distance to the nearest neighbour, had no significant effect on multidiversity or on community composition. This result is consistent with a meta-analysis on the habitat amount hypothesis by Martin ( 2018 ), which reported that patch isolation tends to have neutral or weakly negative effects on species richness once habitat amount is accounted for. Similarly, other studies have emphasized the importance of considering landscape configuration, including both composition and spatial arrangement, rather than focusing solely on habitat amount or isolation, as it can significantly influence biodiversity patterns (Arroyo-Rodríguez et al., 2020 ; Haddad et al., 2017 ). Finally, at the landscape level, hedgerows may play an important role in habitat supplementation and complementation processes. As previously discussed, hedgerows can supplement broadleaved stands or riparian forests in the area. They can also provide complementary habitats or food for animal species that need different types of resources to complete their life cycle as it has been illustrated in our study area in two case studies involving birds. The Eurasian hoopoe ( Upupa epops ) and the great tit ( Parus major ) both use broadleaved hedgerows and stands for nesting in tree cavities, while foraging along pine stand edges to prey on the major native pest insect species of the area (the pine processionary moth; Thaumetopoea pityocampa ) (Barbaro et al., 2008 ; Plat et al., 2025 ). Landscape composition, as reflected by the amount of broadleaved cover, had no significant effect on either dominant or rare species. This contrasts with the findings of Dornelas et al. ( 2009 ) for weed communities, which highlighted a strong response of dominant weed species to landscape heterogeneity in agricultural lands, while no response was observed for rare species. Although we hypothesize that regionally dominant species in our study were primarily associated with the homogeneous pine plantations matrix, the absence of a replacement of these generalist species by a combination of locally dominant and rarer species could be due to: (1) differing responses among taxonomic groups depending on their dispersal capacity, and (2) the amount of broadleaved cover, which may have been too low to detect an effect on the composition of dominant species. Conclusion To our knowledge, our study is the first to demonstrate the value of broadleaved hedgerows for the conservation of multi-taxonomic biodiversity in conifer plantation landscapes. For the majority of the six taxa studied, hedgerows harboured assemblages of species that complement those found in pine forests and they are particularly important for rare and forest species, suggesting that hedgerows provide complementary habitats and resources. We also found that changing landscape composition, through increasing amount of broadleaved habitats, modified community composition for several taxa. Because converting pine plantations to broadleaved stands is often impractical, our results suggest that establishing broadleaved hedgerows offers a promising management strategy to enhance biodiversity in conifer plantation landscapes. The near absence of a connectivity effect indicates that hedgerows could be planted anywhere in the landscape, with their biodiversity benefits being modulated by the resulting amount of broadleaved habitats, which should be preserved as well. Finally, preserving ancient hedgerows and planting new hedgerows can be seen as a Nature-based Solution (Johnson et al., 2022 ) as they benefit biodiversity and increase pest control, since they play a role in reducing a major pest of pine plantations (Plat, et al., 2025 a; Plat, et al., 2025 b). These interstitial habitats should therefore be used more widely to protect and restore biodiversity and ecosystem functioning in heavily modified forest ecosystems, such as conifer plantation landscapes. Future research should examine how hedgerows mitigate abiotic risks, such as wildfires and windstorms, to fully assess their potential for promoting multifunctionality in forest plantation landscapes. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose Funding This research was supported by the SUPERB project funded by the European Union Horizon 2020 research and innovation programme under grant agreement no. 101036849 and by the project Horizon Europe eco2adapt (grant agreement No. 101059498) Author Contribution HJ, IVH and NP designed the study, and wrote the first draft. JBR, OB, TB, JD and SJ collected the data. SJ and OB processed the data relating to spiders. YC, MS and IGC processed the data relating to birds. EA, NP, HJ and IVH analysed the data. All authors reviewed the manuscript. Acknowledgement We thank Thomas Ribot, Yannick Mellerin, Aurelien Kohler and Sylvain Piry for their help in collecting the samples and Alex Bush for providing audio recorders. Data Availability The datasets generated during the current study are available from the corresponding author on reasonable request. References Allan E, Bossdorf O, Dormann CF, Prati D, Gossner MM, Tscharntke T, Blüthgen N, Bellach M, Birkhofer K, Boch S, Böhm S, Börschig C, Chatzinotas A, Christ S, Daniel R, Diekötter T, Fischer C, Friedl T, Glaser K, Fischer M (2014) Interannual variation in land-use intensity enhances grassland multidiversity. 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Supplementary Files SupplementarymaterialBiodiversityHedgerowsLandscape.docx Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Biodiversity and Conservation → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 28 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 15 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Bern","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Allan","suffix":""},{"id":523711466,"identity":"e68068ae-9052-4dec-8be5-f64375058cec","order_by":2,"name":"Severin Jouveau","email":"","orcid":"","institution":"University of Bordeaux, INRAE, BIOGECO","correspondingAuthor":false,"prefix":"","firstName":"Severin","middleName":"","lastName":"Jouveau","suffix":""},{"id":523711468,"identity":"00d6d911-8c7b-417c-beca-ea5a41615739","order_by":3,"name":"Olivier Bonnard","email":"","orcid":"","institution":"University of Bordeaux, INRAE, BIOGECO","correspondingAuthor":false,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Bonnard","suffix":""},{"id":523711470,"identity":"ab148db1-bb63-40b4-b255-1dde97510738","order_by":4,"name":"Jean-Baptiste Rivoal","email":"","orcid":"","institution":"University of Bordeaux, INRAE, 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Oiseaux","correspondingAuthor":false,"prefix":"","firstName":"Yohan","middleName":"","lastName":"Charbonnier","suffix":""},{"id":523711476,"identity":"d9e7cf5a-f6c1-4187-b4a6-d44866f64e3a","order_by":8,"name":"Mathieu Sannier","email":"","orcid":"","institution":"Ligue pour la Protection des Oiseaux","correspondingAuthor":false,"prefix":"","firstName":"Mathieu","middleName":"","lastName":"Sannier","suffix":""},{"id":523711478,"identity":"03969eee-815e-4e22-b4ed-dd208958eede","order_by":9,"name":"Irene Garcia-Celada","email":"","orcid":"","institution":"Ligue pour la Protection des Oiseaux","correspondingAuthor":false,"prefix":"","firstName":"Irene","middleName":"","lastName":"Garcia-Celada","suffix":""},{"id":523711481,"identity":"7b89fc35-fa85-4019-815c-cfb8fd8072ec","order_by":10,"name":"Inge Halder","email":"","orcid":"","institution":"University of Bordeaux, INRAE, 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11:52:47","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":329508,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/0561d8784240fc7bbfecf963.html"},{"id":92651155,"identity":"0a3e87c7-831b-4aa7-82d4-f7e7ca378f1f","added_by":"auto","created_at":"2025-10-02 11:52:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91945,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design showing the three types of habitat (isolated pine edge, isolated hedgerow and connected hedgerow to a broadleaved stand) equally distributed between landscapes with low or high broadleaved cover (based on a 500m buffer area centered on the 100m linear of the pine edge/hedgerow), resulting in six modalities. Each modality was replicated six times resulting in n=36 study sites.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/1b3437ac4b974ad7c644e4fb.png"},{"id":92651153,"identity":"cb2b91f0-e31d-4136-9e1b-942be3998eea","added_by":"auto","created_at":"2025-10-02 11:52:47","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244885,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the effect of habitat (P=pine border, IH= Isolated Hedgerow and CH= Connected Hedgerow) and broadleaved cover (Low vs High) on the species richness of (A) understorey vegetation, (B) butterflies, (C) carabids, (D) spiders , (E) birds and (F) reptiles. Thick horizontal lines represent the median, white dots indicate the mean value. The sample size was n=36 sites. Black dots show extreme values. Stars show significant differences between broadleaved cover modalities. Capital letters show significant differences between habitat types according to Tukey’s post-hoc test (Table 1).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/c981b7acbc2339f0be1c662c.jpeg"},{"id":92651154,"identity":"f02f787a-b838-4537-88c3-defd080f06cb","added_by":"auto","created_at":"2025-10-02 11:52:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":198205,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the effect of habitat (P=pine border, IH= Isolated Hedgerow and CH= Connected Hedgerow) and broadleaved cover (Low vs High) on the multidiversity of (A) all groups and all species, (B) regionally dominant species, (C) locally dominant species, (D) rare species, (E) forest species. Reptiles were excluded from multidiversity of local dominance and multidiversity of forest species as no reptile species were classified in these groups. Thick horizontal lines represent the median, white dots indicate the mean value. The sample size was n=36 for each figure. Black dots show extreme values. Stars show significant differences between broadleaved cover modalities. Capital letters show significant differences between habitat modalities, while lowercase letters show significant differences between habitat x broadleaved cover (significant interaction) according to Tukey’s post-hoc tests (Table 1).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/62999aae7cebee122b19919e.jpeg"},{"id":92651162,"identity":"91bfb6e4-30fb-4f92-ab91-1e64ccbd498c","added_by":"auto","created_at":"2025-10-02 11:52:47","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365755,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4 part 1. Venn diagrams showing species presence in each habitat type and PCoA showing differences between study sites communities depending on habitat type (P= pine edge, IH= isolated hedgerow, CH= connected hedgerow) and for (A) understorey vegetation, (B) butterflies, (C) carabids, (D) spiders, (E) birds and (F) reptiles. Significant differences in community composition were tested using PerMANOVAs. Communities are based on Bray Curtis dissimilarity matrix with Hellinger transformation. Sample size was n=36 sites.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/64e1f8e2a36014efb39f5237.jpeg"},{"id":92651665,"identity":"1628f531-8b85-43ed-a0a7-7bfbe4b1e546","added_by":"auto","created_at":"2025-10-02 12:00:47","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":317015,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4 part 2. Venn diagrams showing species presence in each habitat type and PCoA showing differences between study sites communities depending on habitat type (P= pine edge, IH= isolated hedgerow, CH= connected hedgerow) and for (A) understorey vegetation, (B) butterflies, (C) carabids, (D) spiders, (E) birds and (F) reptiles. Significant differences in community composition were tested using PerMANOVAs. Communities are based on Bray Curtis dissimilarity matrix with Hellinger transformation. Sample size was n=36 sites.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/86049f14d7fe775a39ef83b5.jpeg"},{"id":102785882,"identity":"a1129b00-506d-4893-b8ab-2cbb46cc97c2","added_by":"auto","created_at":"2026-02-16 16:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2684630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/6ebc9dca-e4a8-416b-831d-86a10b2c499b.pdf"},{"id":92651163,"identity":"22c0a783-2cca-43b1-949f-9b5ef65d6fb6","added_by":"auto","created_at":"2025-10-02 11:52:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3402278,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialBiodiversityHedgerowsLandscape.docx","url":"https://assets-eu.researchsquare.com/files/rs-7131998/v1/c156de28e53350d66ab379d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of broadleaved hedgerows and landscape composition for biodiversity conservation in a pine plantation context","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBiodiversity loss is increasing worldwide due to multiple anthropogenic factors such as land-use change, resource over-exploitation, pollution, climate change and biological invasions, which ultimately lead to the degradation of ecosystem functions and services (Balvanera et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cardinale et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Christian, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jaureguiberry et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Forests represent the most species-rich habitat on the planet, particularly in the tropics, and are therefore essential for the conservation of global biodiversity (Brockerhoff et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gibson et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Muys et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of global change, which is increasing the frequency of abiotic and biotic disturbances (Forzieri et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Patacca et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), protecting forest ecosystems is therefore crucial to preserving biodiversity.\u003c/p\u003e\u003cp\u003eMonospecific forests are particularly vulnerable to disturbances, provide fewer ecosystem services and host a lower biodiversity than mixed forests (Feng et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gamfeldt et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jactel et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Increasing tree species diversity can enhance both α-diversity and β‐diversity of associated taxa at the stand level (Ampoorter et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Brockerhoff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kremer et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and landscape level (Duflot et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Heinrichs et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Muys et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to community ecology theory, these differences in local biodiversity patterns may emerge from a sequence of filters acting on the regional species pool (Cadotte \u0026amp; Tucker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Germain et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Weiher \u0026amp; Keddy, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). First, dispersal limitations determine which species can reach and establish in suitable habitats. Second, abiotic conditions (e.g., temperature, precipitation) select species able to occupy and survive in the habitat based on their physiological tolerances and traits (Keddy, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Woodward \u0026amp; Diament, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Third, biotic interactions, such as trophic interactions, competition or facilitation, further shape community composition even among proximate sites with similar abiotic conditions (Kraft et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marteinsdóttir \u0026amp; Eriksson, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandscape heterogeneity depends on landscape configuration (the spatial arrangement of patches) and composition (the relative proportion of habitat types) (Duflot et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Marini et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and can strongly influence the dispersal filter. Indeed, species colonisation and individual movement depend on the connectivity between habitat patches (e.g., the distance separating them) and the permeability of the intervening matrix, which allows for a certain degree of dispersal (Dunning et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Hodgson et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, as it is well established that larger habitat patches can support more species and individuals (i.e.: \"species area relationship\"; Gleason, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1922\u003c/span\u003e), theoretical studies have suggested that the amount of habitat in the surrounding landscape may be the most relevant metric to explain species richness, as it incorporates both patch size and connectivity (the “habitat amount hypothesis”; Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, landscape heterogeneity promotes β-diversity, and favours habitat complementation (i.e. species that need resources from different habitats throughout their life cycle (Dunning et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Priyadarshana et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the well-documented benefits of these landscape ecology concepts, increasing landscape heterogeneity in production forests can be challenging because it implies increasing the diversity, in space and time, of stand composition (e.g. diversity of tree species and understorey vegetation), structure (e.g. diversity of tree age and diameter distribution) and management (e.g. rotation length, deadwood retention) (Duflot et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muys et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These changes (e.g. replace production plantations with semi-natural forest to promote biodiversity) can be complex to implement in the absence of governance systems or incentives, particularly in areas composed of small private holdings (Jactel et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Löfroth et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tiebel et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Instead of altering the land use of existing patches, one can establish a new habitat at their interface, often referred to as interstitial habitats, linear elements, or boundary habitats (Hinsley \u0026amp; Bellamy, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Holland, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Examples include hedgerows, flower strips and grassy margins in agricultural landscapes or riparian forests and firebreaks in forest landscapes (Arroyo-Rodríguez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Holland, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van Halder et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Hedgerows between crop fields (sometimes referred to as “bocage landscapes”; Boinot et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have been widely studied (Burel, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Montgomery et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and are considered a nature-based solution (Johnson et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) due to their role in conserving biodiversity, and more particularly natural enemies, which contribute to pest control in adjacent crops (Ferrante et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Holland, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Hedgerows constitute a suitable habitat for many plant and animal species (Boutin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pywell 2005, de Zwaan 2024) and enhance connectivity by providing effective dispersal corridors for animal species (Boinot et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yet the potential of hedgerows in homogeneous monospecific forest landscapes remains underexplored.\u003c/p\u003e\u003cp\u003eMost studies aiming to describe habitat or landscape effects on biodiversity have focused on a single taxonomic group using standard metrics (species richness, abundance and composition). Nevertheless, adopting a multi-taxonomic approach is particularly necessary to capture landscape-level processes that vary between and within taxa, depending on species’ home ranges and dispersal abilities (Allan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, taxa often respond differently to landscape characteristics, so that studying one particular taxon does not allow to predict the responses of others (Barbaro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). For example in forest plantation landscapes, Barbaro et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), found that bird species richness responded positively to landscape heterogeneity, whereas carabid species richness was negatively affected. Additionally few studies have examined habitat and landscape effects with both a multi-taxonomic perspective and a focus on dominant vs rare species or generalist vs specialist species (Rajaonarimalala et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soliveres et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Dominant species, i.e. species that are abundant relative to others, exert proportionate effects on environmental conditions, community diversity and ecosystem functions (Avolio et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, rare species, which are both geographically limited and have small populations, are particularly threatened by human activities or environmental disturbances, but often possess particular functional traits linked with unique ecosystem functions (Avolio et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dee et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While a previous study found contrasting effect of landscape composition on dominant and rare weed species (Dornelas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), a gap remains in the literature regarding the effect of landscape heterogeneity on dominant and rare species, especially with a multi-taxa perspective. Additionally, accounting for generalist and specialist species is particularly relevant, as numerous studies have highlighted the widespread replacement of specialist species by generalist ones due to habitat loss, disturbances, or climate change (Clavel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), leading to functional homogenization. While increasing the amount of habitat for specialist species is crucial for their conservation (Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), few studies have explored the contrasting effects of implementing interstitial habitats on generalist versus specialist species (e.g., Batáry et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for birds; Litza et al., (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for plants).\u003c/p\u003e\u003cp\u003eThe Landes de Gascogne Forest - the largest planted forest in Europe - offers an ideal setting to answer these questions. This forest is dominated by pure, even-aged maritime pine stands (\u003cem\u003ePinus pinaster\u003c/em\u003e Ait). Within this pine plantation matrix, broadleaved remnants and riparian forests (subject to low-intensity management) still persist and serve as crucial refuges for biodiversity, particularly for forest specialist species that may not find suitable habitat in young pine plantations (Barbaro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Brockerhoff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; van Halder et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). As the creation of new broadleaved stands is often undesirable for forest owners or logistically unfeasible, the establishment of broadleaved hedgerows (hereafter referred to as “hedgerows”) could represent a practical alternative to increase the overall amount of broadleaved habitat, enhance connectivity among existing broadleaved remnants, and offer complementary habitats for biodiversity. In the Landes de Gascogne forest, old broadleaved hedgerows are interspersed throughout the landscape. These hedgerows typically occur along the edge of pine plantations, adjacent to roads, forest tracks, or drainage ditches. They have been conserved for different reasons such as property boundaries, hunting, mushroom picking, firewood collection, or difficulties associated with harvesting wood along ditches (Plat et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb). However, their role in biodiversity conservation has not yet been investigated in such a forest landscape context.\u003c/p\u003e\u003cp\u003eIn this study, we surveyed six different taxa and adopted a multi-taxonomic approach to assess biodiversity. We chose to compare biodiversity in broadleaved hedgerows with biodiversity in pine edges (i.e., the first lines of trees along a forest road) because: (1) they represent the part of the forest stand that has been replaced by a hedgerow, and (2) biodiversity often differs substantially between forest edges and interiors (Deconchat, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Terraube et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, to disentangle the effects of habitat amount and distance to the nearest neighbouring patch on biodiversity, we surveyed biodiversity in hedgerows that were either connected to or isolated from a broadleaved stand, while also accounting for the broadleaved stand cover in the surrounding landscape (Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Haddad et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As community composition and species richness are known to vary between forest types (e.g., coniferous vs broadleaved stands; Ampoorter et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), shifts in dominant and rare species, as well as between generalist and forest species, may occur between hedgerows and pine edges. In addition, rare species, as well as forest species, may be more affected by intensive management of pine plantations and, conversely, favoured by the non-management of hedgerows. At the landscape scale, given that increased landscape heterogeneity tends to promote species turnover and supports greater diversity of locally adapted species (Dornelas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fahrig et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we expect a shift in community composition with increasing landscape heterogeneity, from communities with regionally dominant species associated with pine stands to more locally dominant species. Greater landscape heterogeneity may also benefit rare species and forest species (Dornelas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, van Halder 2007). Finally, although European forest ecosystems are considered the last refuge for many endangered species, further measures are needed to investigate how remnants of sub-natural woodlands such as broadleaved hedgerows and stands can contribute to their protection (Muys et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMore particularly, by surveying the biodiversity of hedgerows and pine edges, accounting for their isolation and for the broadleaved cover in their surrounding landscape, we tested the following hypotheses:\u003c/p\u003e\u003cp\u003eH1) The species richness of each taxon and the multi-taxonomic diversity are higher in hedgerows than in pine edges particularly when hedgerows are connected to a broadleaved stand (i.e. isolated vs connected hedgerows). Additionally, biodiversity is higher in sites located in a landscape with a high cover of broadleaved stands compared to those with a low broadleaved cover.\u003c/p\u003e\u003cp\u003eH2) Community composition of the studied taxa differs between pine edges, isolated hedgerows and connected hedgerows, as well as between landscapes with high and low broadleaved cover.\u003c/p\u003e\u003cp\u003eH3) Considering all taxonomic groups, rare species, locally dominant and forest species occur more frequently in hedgerows than in pine edges, and more frequently in sites located in a landscape with a high cover of broadleaved stands compared to those with a low broadleaved cover. The opposing results should be observed for the regionally dominant species (i.e. they are expected to be more frequent at pine stand edges and in landscapes with a low broadleaved cover).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy region\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLocated in south-western France, the Landes de Gascogne Forest covers approximately 1.16\u0026nbsp;million hectares and is predominantly composed of pure stands of maritime pine (\u003cem\u003eP. pinaster\u003c/em\u003e) (Barbaro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These plantations are intensively managed, with soil preparation and thinning operations before clear-cut harvesting at 30 to 40 years, creating temporary open areas across the landscape. Within this homogeneous landscape matrix, a limited number of unmanaged riparian forests (dominated by \u003cem\u003eAlnus glutinosa\u003c/em\u003e L. and \u003cem\u003eQuercus robur\u003c/em\u003e L.) as well as low-intensity managed stands of native oaks (primarily \u003cem\u003eQ. robur\u003c/em\u003e and \u003cem\u003eQ. pyrenaica\u003c/em\u003e Willd.) persist (Mora et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperimental design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the purpose of the study, the Living Lab “Forest Bocage” (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.plantedforests.org/fr/infrastructures/superb-bocage-forestier/\u003c/span\u003e\u003cspan address=\"https://www.plantedforests.org/fr/infrastructures/superb-bocage-forestier/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was created in the Gironde district, in the Landes de Gascogne forest (50,000 ha area, barycenter coordinates: X: −0.776865; Y: 44.560623). In the Living Lab area, we used high-resolution infrared colour orthophotographs (IGN; 20 cm pixel resolution, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://geoservices.ign.fr/documentation/donnees/ortho/bdortho\u003c/span\u003e\u003cspan address=\"https://geoservices.ign.fr/documentation/donnees/ortho/bdortho\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) analysed at a 1:2500 scale in a GIS environment to identify (1) broadleaved hedgerows and (2) broadleaved and mixed forest stands. Then, we selected 36 study sites according to factorial design with two factors following the recommendation from Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e. Factors included (1) the type of habitat with three modalities (pine plantation edge, connected hedgerow or isolated hedgerow), (2) landscape composition with two modalities (low or high amount of broadleaved stands in the landscape). This resulted in 6 combinations with 6 replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSampled hedgerows and pine edges were 100 to 325 m in length. Pine edges were at least 6 m in height and adjacent to a forest road, representing the control treatment. Hedgerows were defined based on field observations as linear formations of broadleaved trees at least 8 meters in height, typically one or two trees wide, with continuous canopies, and dominated by native oak species (\u003cem\u003eQ. robur\u003c/em\u003e and \u003cem\u003eQ. pyrenaica\u003c/em\u003e). Only hedgerows in between two pine plantations were sampled, with an average distance of 26 m between the two pine stands. Connected hedgerows were directly adjacent at one end to a broadleaved stand (i.e., distance to the nearest natural habitat patch = 0 m). Isolated hedgerows and pine edges with no hedgerow were at least 100 m distant from a broadleaved stand (i.e., distance to the nearest natural habitat patch \u0026gt; 100 m) and 50 m from other hedgerows. These distance thresholds were selected to assess the influence of habitat connectivity on multi-taxonomic biodiversity, particularly for organisms with limited dispersal abilities such as ground beetles and spiders. However, they may represent relatively short distances for taxa with greater dispersal capacity, such as birds (Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Slagsvold et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nevertheless, these distances were chosen as a compromise, given the difficulty in identifying truly isolated hedgerows in landscapes with high broadleaved cover due to (1) the high density of hedgerows, and (2) the limited availability of landscapes with high broadleaved cover. The percentage of broadleaved forest cover in the surrounding landscape (hereafter “broadleaved cover\") was quantified within a 500 m buffer around the sampled hedgerows or pine stand edges, at each study site. Two classes of broadleaved cover were selected: low (0–6%; n = 18) and high (14–36%; n = 18).\u003c/p\u003e\u003cp\u003eThe 36 sampled sites were evenly distributed across the study area, with an average inter-site distance of 1.7 km (range: 0.7–3.9 km) to minimize spatial autocorrelation (see the map of the Living Lab: Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-taxonomic biodiversity survey\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe selected six different taxonomic groups, ranging from primary producers to secondary predators, in order to cover a wide range of dispersal abilities and biotic interactions. Additionally, all the groups studied are known to be sensitive either to forest tree composition, landscape composition or to hedgerows in agricultural landscapes (Ampoorter et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Montgomery et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnderstorey vegetation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll vascular plant species were recorded along a 50 × 1.5 m transect positioned within the hedgerow or the first line of trees for pine edges. All species present in the transect were classified into two vertical strata: the herbaceous layer, comprising herbaceous species and ligneous species under 0.5 m in height, and the shrub layer, including only ligneous species and climbers (vines) between 0.5 and 5 m in height (Canullo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). To estimate plant species cover, three 5 × 1.5 m quadrats were placed within this the transect. Two pairs of trained observers (JD/TB and IVH/NP) independently assessed species cover within each stratum using the Domin scale (Rich et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Understorey vegetation was assessed in June 2023 to capture nearly the entire local species pool. Species identification was conducted using a taxonomic guide (Rameau et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and complex identifications were verified in the laboratory under a binocular magnifier. Following Corcket et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Domin scale values were converted to mean percentage cover, and these values were then averaged across the three quadrats to obtain a cover value per species per site. For ligneous species present in both strata, cover values were summed. Species observed within the transect but absent from the quadrats were assigned the lowest mean cover value (0.033%). Seedlings and saplings from \u003cem\u003eQ.robur\u003c/em\u003e, \u003cem\u003eQ.pyrenaica\u003c/em\u003e and \u003cem\u003eP.pinaster\u003c/em\u003e were removed from analysis as study sites were selected to be dominated by trees of these species.\u003c/p\u003e\u003cp\u003e\u003cb\u003eButterflies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eButterflies were surveyed using the line-transect method (Pollard \u0026amp; Yates, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). At each site, one trained observer (IVH) walked along a 100 m transect (either along the hedgerow or the pine edge), recording all butterflies observed within 2.5 m on either side of the transect line and up to 5 m ahead. Individuals were identified visually or, when necessary, captured with a net and released after identification. The transect was walked in both directions, but only the maximum number of individuals per species observed over one 100 m transect was retained for analysis to avoid double counting. Each of the 36 study sites was visited three times, once a month, between May and July 2023, which allowed observing different individuals and species. Surveys were carried out between 10:00 and 18:00, and only under suitable weather conditions (temperature \u0026gt; 20°C, low cloud cover, and wind speed \u0026lt; 30 km/h). The order of site visits was randomized across the three survey periods. For each site, the total number of individuals per species was summed across the three visits and used in statistical analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCarabid beetles and ground dwelling spiders\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCarabid beetles (here after “carabids”) and ground-dwelling spiders (hereafter “spiders”) were sampled using pitfall traps (Brown \u0026amp; Matthews, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Each trap consisted of a glass jar (90 mm in diameter, 100 mm in height, 445 mL volume) filled with a mixture of propylene glycol and water, and covered with a plastic rain guard. At each site, three traps were placed along the hedgerow: one at the center and two others located 25 m away on either side. Traps were active during three sampling periods: April, June, and July 2023, each lasting two weeks, for a total of 42 trapping days. Collected arthropods were stored in a deep freezer prior to identification.\u003c/p\u003e\u003cp\u003eCarabids were identified to the species level by an expert (SJ), using external morphological discriminating characters as well as genitalia and aedeagus examinations if necessary (Janovska et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Reference books were those for the French carabids fauna (Coulon et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jeannel, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1941\u003c/span\u003e), and the European fauna (Hurka, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Trautner \u0026amp; Geigenmüller, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Spiders were identified to the species level by two experts (OB and SJ), using one taxonomic book (Roberts, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) and two taxonomic websites for spiders for France and Belgium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arachno.piwigo.com/\u003c/span\u003e\u003cspan address=\"https://arachno.piwigo.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and for Europe (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://araneae.nmbe.ch/\u003c/span\u003e\u003cspan address=\"https://araneae.nmbe.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Juvenile spiders were excluded from the analysis due to identification difficulties. Captures from the three seasons were pooled to obtain a species richness value per site. Although the number of individuals captured per species represents an activity density metric, it was used as an acceptable proxy of abundance (Chaladze, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for codominance analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBirds\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBird communities were surveyed using passive acoustic monitoring, combined with expert identification of species-specific calls and songs (Schillé et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In April 2023, one Song Meter Mini Bat recorder (Wildlife Acoustics), equipped with an omnidirectional microphone, was installed in each of the 36 study sites. Devices were installed at a height of 2 meters on pine trees located along the pine edge, either adjacent or not to hedgerows. Recorders operated for five consecutive days, recording one-minute audio file every three minutes from sunrise to sunset. Within a period of two days with no rain and low wind speed (\u0026lt; 30 km/h), a subset of five one-minute recordings was extracted between 08:30 and 08:45 am. This time period was selected as it corresponds to 1.5 hour after sunrise, matching the peak bird vocal activity. This resulted in a total of 360 audio files of one-minute (5 recordings × 2 days × 36 sites), corresponding to six hours of acoustic data. Expert ornithologists (YC, MS, and IGC) listened at each recording to identify all bird species present based on their vocal activity. For each site, the number of files in which a given species was detected (ranging from 0 to 10) was used as a proxy for bird vocal activity (Schillé et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although individual birds may have been recorded multiple times, vocal activity was considered a reliable indicator of relative abundance and used for subsequent codominance analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReptiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003eReptiles were surveyed using a combination of active visual search and artificial refuge methods to optimize detection efficiency (Michael et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pottier, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In January 2023, four artificial refuges (asphalt roofing sheets) were installed at 20 m intervals along a 100 m transect, placed either along the hedgerow or the pine edge. Two pairs of trained observers (JBR/TB and OB/NP) conducted visual surveys along the transect, recording all reptiles observed within the hedgerow or pine edge, including individuals located on or beneath the artificial refuges. Each of the 36 study sites was surveyed five times, once per month from April to October 2023, excluding July and August due to excessively high temperatures. Surveys were conducted between 09:00 and 13:00, which corresponds to the period during which reptiles remain in the sun for thermoregulation. Rainy days and those with wind speed exceeding 30 km/h were avoided. The order of site visits was randomized across the five survey periods. As individuals were not marked, for each site, the maximum number of individuals per species observed during one visit was used as an abundance metric in codominance analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDominant and rare species evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNumerous metrics have been developed to identify dominant and rare species, but most were designed for plant communities (Avolio et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dee et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To our knowledge, only two studies have investigated species dominance and rarity using a multi-taxonomic framework (Allan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Soliveres et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, both relied on arbitrary thresholds to define dominant (e.g., top 10% most abundant) and rare species (e.g., bottom 50–90% less abundant). In contrast, we applied the codominance approach proposed by Gray et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which (1) more accurately captures dominance patterns by identifying, for each taxon and community, the optimal number of codominant species based on their relative abundances, and (2) can be applied across taxa regardless of differences in abundance metrics. The approach selects subsets of the most abundant species and calculates a harmonic mean of their relative abundances, favouring subsets where species have similar abundances. This shared abundance is then contrasted with that of the next most abundant species to compute a codominance index. The optimal codominant subset is the one that maximizes both internal similarity and contrast with subordinate species, ultimately yielding the number of codominant species in the community (see Gray et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e for details). This method also allowed to detect mono-dominated communities, when the ratio between the first and second species in terms of abundance is higher than 3. The codominance metric was calculated for communities of each taxonomic group at two spatial scales: (i) at the local level, for each of the 36 study sites independently, and (ii) at the regional level, by aggregating species abundances across all 36 sites. This method allowed us to classify species within each taxonomic group into 3 categories: (1) locally dominant species, defined as codominant in at least one site, (2) regionally dominant species, codominant in the entire dataset and (3) rare species, never identified as codominant at the local or regional level. We assessed local dominance only in communities with more than 20 individuals. Consequently, no reptile species were classified as locally abundant. At the local level, in a few cases, no clear pattern of codominance emerged; in these cases, no species were classified as locally dominant. The complete classification of species according to dominance status is provided in Table S2. Notably, between 1 and 5 species per group were classified as regionally dominant, and these species were always locally dominant in at least one site. This approach also allowed the classification of between 5 and 16 species as locally dominant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eForest specialist species identification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor each taxonomic group, we identified species considered as forest specialists at the French or European scale. As classification methods differ among groups, we applied group-specific criteria designed to consistently exclude generalist species and retain only those with a strong forest affinity (hereafter referred to as “forest species”).\u003c/p\u003e\u003cp\u003eFor understorey vegetation, we used the European Forest Plant Species List (Heinken et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Using data specific to the Atlantic region of France and following the same methodology as Litza et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we selected species classified in the first two categories: (1.1) taxa primarily found in closed forest habitats, and (1.2) taxa typically associated with forest edges and open forest conditions. Forest butterflies were identified using the European classification of butterfly species by biotope (van Swaay et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For carabid beetles, we adopted the classification method used by Jouveau et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the same study region, classifying species into three habitat classes: forest specialists, generalists, and open-habitat specialists. For species not covered in Jouveau et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we supplemented the habitat information using the publications of Murdoch (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) and Jaskula \u0026amp; Soszyńska-Maj (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Spiders were classified using the World Spider Trait database (Pekár et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), focusing on the \"Light 2\" trait, which categorizes species based on their light preference (Buchar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Values for this trait range from 1 (species of open habitats) to 5 (species of dark habitats). Species with a score \u0026gt; 3.0, corresponding to a preference for shaded or dark environments, were classified as forest dwellers. When a species had multiple habitat associations in the database, we used the mean score above 3 as threshold for classifying it as forest-associated. Forest bird species were identified based on the STOC report (Fontaine et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This French national monitoring program classifies birds as forest species when they occur significantly more often in 1 km² areas dominated by forest cover compared to areas dominated by agricultural or urban land uses, based on 30 years of national survey data. Reptiles were excluded from the analysis, as only five species were present in the dataset, all being classified as generalist species (INPN; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://inpn.mnhn.fr\u003c/span\u003e\u003cspan address=\"https://inpn.mnhn.fr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRed List species identification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe conservation status at the European level was checked for every species according to the IUCN Red list (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 04-2025). 47 understorey plant species were classified as NA as they were either data deficient species (n = 39), invasive species (n = 5) or species identified at genus level only (n = 3). As spiders are not included in the IUCN European Red list, we used the red List of French spiders (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uicn.fr/wp-content/uploads/2023/03/liste-rouge-araignees-de-france-metropolitaine.pdf\u003c/span\u003e\u003cspan address=\"https://uicn.fr/wp-content/uploads/2023/03/liste-rouge-araignees-de-france-metropolitaine.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). There was no red list available for carabids at the European or French level.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eTo integrate the responses of all taxonomic groups into a single biodiversity metric, we computed a multidiversity index following the method proposed by Allan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This index is used to weigh taxa so they have the same importance. It was calculated by scaling the species richness of each group by its maximum observed value across the 36 study sites. The standardized richness values were then averaged across all six taxonomic groups to obtain one multidiversity value per site.\u003c/p\u003e\u003cp\u003eTo evaluate the effect of broadleaved cover on biodiversity, since the spatial scale at which landscape composition influences the presence of species can vary depending on both species' movement ranges and the ecological context (Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), we assessed the relevance of the 500 m buffer size through a model-based approach. Specifically, we constructed 10 linear models using multidiversity as a composite biodiversity response variable, with broadleaved cover measured at buffer distances ranging from 100 m to 1000 m in 100 m increments. We then plotted the R² values of these models against buffer size. The highest R² values were observed for buffers between 400 m and 1000 m. Moreover, broadleaved cover estimates were highly correlated across this range, supporting the choice of a 500 m buffer as a representative and ecologically meaningful scale (Fig S2, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In all analyses and in line with our study design, broadleaved cover was treated as a quantitative variable with two classes (low or high), rather than as a continuous gradient, because its range in our study area was limited (0–36%) and values were not evenly distributed along the gradient.\u003c/p\u003e\u003cp\u003eWe used the following statistical models to test our hypotheses:\u003c/p\u003e\u003cp\u003eH1: To assess the effects of habitat type and broadleaved cover on multi-taxonomic biodiversity, we first used the species richness of each taxonomic group separately using Generalized Linear Models (GLMs) with a Poisson distribution, appropriate for count data. Second, multidiversity was used as a response variable in a Linear Model (LM).\u003c/p\u003e\u003cp\u003eH2: To examine the influence of habitat type and broadleaved cover on community composition of each taxonomic group, we performed Permutational Multivariate Analysis of Variance (PerMANOVA) using a Bray–Curtis dissimilarity matrix, appropriate for ecological community data. Species abundance data were Hellinger-transformed to reduce the disproportionate influence of dominant species (Legendre \u0026amp; Gallagher, 2001). PerMANOVAs were performed on the full dataset for each taxonomic group to assess differences between landscapes with low and high broadleaved cover categories, and on sub-datasets including only two of the three habitat types (pine edge, connected hedgerows and isolated hedgerows), to compare community composition between each pair of habitat types. Principal Coordinate Analysis (PCoA) was used to visualize patterns in species composition. For reptiles, the results must be interpreted with caution due to the low number of species and individuals.\u003c/p\u003e\u003cp\u003eH3: To test the effect of habitat type and broadleaved cover on dominant species (local or regional level), rare species, and forest species, we calculated additional multidiversity indexes based on different subsets of the dataset (i.e. multidiversity of local dominance, multidiversity of regional dominance, multidiversity of rare species and multidiversity of forest species). Reptiles were excluded from both the local dominance and forest analyses, as no species in this group met the respective criteria. Multidiversity indexes served as response variables in separate LMs.\u003c/p\u003e\u003cp\u003eFor GLMs and LMs, the interaction between habitat and broadleaved cover was tested and retained only when statistically significant. Model assumptions of residual normality and homoscedasticity were assessed graphically. We checked for spatial autocorrelation of the residuals of each model using Moran’s I (4-nearest neighbours) across the 36 sites and found no significant effect. Post hoc comparisons between habitat types were conducted using Tukey’s HSD tests. All statistical analyses were conducted in R version 4.4.0 (R Core Team, 2016), using the following packages: \u003cem\u003elmerTest\u003c/em\u003e for GLMs and LMs, \u003cem\u003evegan\u003c/em\u003e (function \u003cem\u003eadonis2\u003c/em\u003e) for PerMANOVA, \u003cem\u003eemmeans\u003c/em\u003e for post hoc comparisons and \u003cem\u003espdep\u003c/em\u003e for Moran’s I values.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBased on the multi-taxonomic surveys of 6 taxonomic groups in the 36 study sites, we identified a total of 279 species including 79 understorey plants (28%), 108 spiders (39%), 41 birds (14%), 30 butterflies (11%), 16 carabids (6%) and 5 reptiles (2%) (Species list available in Table S2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eH1) Effect of habitat and broadleaved cover on species richness and multidiversity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 6 taxonomic groups surveyed, understorey plants were the only one to respond to habitat and landscape modalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, R\u0026sup2;=0.47). Species richness of understorey plants was significantly higher in connected hedgerows and isolated hedgerows than in pine edges. In addition, the species richness of understorey plants was significantly higher in sites in a landscape with a low broadleaved cover compared with high broadleaved cover. The species richness of the five other groups showed no significant difference between habitat or landscape modalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of GLMs and LMs testing the effect of habitat and broadleaved cover in the landscape on the species richness of each group (H1) and on multidiversity indexes (H1-H3-H4). For each model, low broadleaved cover in the landscape is the reference modality compared to high broadleaved cover. When the habitat variable was significant, a Tukey\u0026rsquo;s post-Hoc was applied to show differences between modalities (P\u0026thinsp;=\u0026thinsp;pine edge, IH\u0026thinsp;=\u0026thinsp;isolated hedgerow, CH\u0026thinsp;=\u0026thinsp;connected hedgerow). For each model, interactions were removed because not significant, except for H3 with multidiversity of local dominance (Tukey\u0026rsquo;s post-Hoc results are visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant variables are indicated in bold. Sample size was n\u0026thinsp;=\u0026thinsp;36 sites.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTukey\u0026rsquo;s post-Hoc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoeff. \u0026plusmn; SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eR\u0026sup2; adj\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies Richness of understorey vegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHabitat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP - IH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHigh broadleaved cover\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies Richness of butterflies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies Richness of carabids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies Richness of spiders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies Richness of birds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies Richness of reptiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultidiversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHabitat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP - IH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHigh broadleaved cover\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultidiversity of local dominance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultidiversity of regional dominance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultidiversity of rare species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHabitat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP - IH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultidiversity of forest species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eHabitat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP - IH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh broadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen all groups were combined into one index, multidiversity was significantly higher in connected hedgerows and isolated hedgerows than in pine edges. Multidiversity was also significantly higher in low broadleaved cover landscapes than in high broadleaved cover landscapes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, R\u0026sup2;=0.29).\u003c/p\u003e\u003cp\u003e\u003cb\u003eH2) Effect of habitat and broadleaved cover on community composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach of the three habitat types (i.e. pine edge, isolated hedgerow and connected hedgerow) hosted unique species of the different taxonomic groups. This was especially visible for understorey plants with 24 species identified only in connected hedgerows, 8 in isolated hedgerows and 5 in pine edges (see Venn diagrams in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e for details). Habitat type had a taxon-dependent effect on community composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For understorey vegetation and spiders, pine edges hosted distinct communities compared to both isolated and connected hedgerows, while communities in the two types of hedgerows were similar. For butterflies and birds, only the communities in connected hedgerows differed significantly from those in pine edges, while communities in isolated hedgerows did not significantly differ from either. Carabid and reptile communities were not significantly affected by the type of habitat.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of PerMANOVA testing the effect of habitat and the broadleaved cover in the landscape on the community composition of each group. Community are based on a Bray Curtis dissimilarity matrix with Hellinger transformation. For broadleaved cover, the test was applied to the complete dataset (n\u0026thinsp;=\u0026thinsp;36). For habitat, the test applied for each pair of modalities (n\u0026thinsp;=\u0026thinsp;24; P\u0026thinsp;=\u0026thinsp;pine edge, IH\u0026thinsp;=\u0026thinsp;isolated hedgerow, CH\u0026thinsp;=\u0026thinsp;connected hedgerow). Significant variables are indicated in bold.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModalities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026sup2; adj\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderstorey vegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP - IH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBroadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLow - High\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eButterflies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP - IH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBroadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLow - High\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarabids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP - IH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP- CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBroadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow - High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP - IH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBroadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow - High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP - IH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP- CH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBroadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow - High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReptiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHabitat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP - IH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP- CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIH - CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBroadleaved cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLow - High\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eStatements and Declarations\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhile the number of species exclusively present in sites within landscapes of low versus high broadleaved cover was similar, these two types of sites hosted significantly different communities for understorey vegetation, butterflies, and reptiles, whereas spider, carabid, and bird communities showed no significant differences (Fig. S3, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eH3) Effect of habitat and broadleaved cover on dominant, rare and forest species\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs expected, multidiversity (including all species) was positively correlated with the multidiversity of regionally -dominant species (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; R\u0026sup2; = 0.10), the multidiversity of locally dominant species (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u0026sup2; = 0.38), and the multidiversity of rare species (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R\u0026sup2; = 0.64; Pearson correlations, data not shown), as these metrics were calculated from subsets of the overall biodiversity data. The multidiversity of rare species was significantly higher in hedgerows than in pine edges, whereas broadleaved cover did not influence this metric. Neither habitat type nor broadleaved cover had a significant effect on the multidiversity of regionally -dominant or locally dominant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong the 279 species identified in our study, our method enabled us to classify 60 of them as forest species: 18/89 plant species (20%), 5/30 butterfly species (17%), 7/16 carabid species (44%), 17/108 spider species (16%) and 13/41 bird species (32%). Multidiversity of forest species was significantly higher in connected hedgerows and isolated hedgerows than in pine edges. Multidiversity of forest species was also slightly higher in high broadleaved cover landscapes than in low broadleaved cover landscapes although this difference was not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, R\u0026sup2;=0.48). No clear relationship was observed between forest species and their dominance classification, as 4/14 (28%) were regionally dominant, 12/58 (21%) were locally dominant and 36/207 (17%) were rare (Table S2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRed list species\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOne endangered spider species (\u003cem\u003eDysdera fuscipes\u003c/em\u003e) was identified in two sites that were both hedgerows in a high broadleaved cover landscape. Three vulnerable species were identified. One bird species, the European turtledove - \u003cem\u003eStreptopelia turtur\u003c/em\u003e, was present in one site, a pine edge within a high broadleaved cover landscape. One butterfly species, the small skipper - \u003cem\u003eThymelicus sylvestris\u003c/em\u003e, was present in two sites, one hedgerow in high broadleaved cover landscape and one pine edge in a low broadleaved cover landscape. One reptile species, the aspic viper - \u003cem\u003eVipera aspis\u003c/em\u003e, was present in one site, a hedgerow in a low broadleaved cover landscape (Table S2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy sampling species belonging to six different taxa in 36 sites in pine plantation landscapes, we found that broadleaved hedgerows harbour greater multi-taxonomic biodiversity than the edges of pine stands. Community composition in hedgerows differed markedly from that in pine edges across most taxonomic groups, highlighting the complementary role of hedgerows in preserving biodiversity. Especially, we demonstrated that hedgerows are particularly relevant for the conservation of forest specialist species and rare species. In contrast, the connectivity of hedgerows to broadleaved stands had almost no effect on overall multi-taxonomic biodiversity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of habitat type on biodiversity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur results demonstrate that hedgerows, which are rare and small sized interstitial habitats in the landscape, support a higher multidiversity than pine plantation edges and harbour distinct communities for most taxa compared to pine plantation edges, which are much more abundant in the forest landscape. These differences in biodiversity between the two habitat types can be explained by biotic and abiotic factors associated with broadleaved or coniferous trees and by the disturbance regime resulting from the intensive management of pine plantations compared with unmanaged hedgerows (Brockerhoff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kennedy \u0026amp; Southwood, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Willson \u0026amp; Comet, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). It has been shown that broadleaved trees harbour more species of herbivorous insects than conifers, which in turn can increase the diversity of predators (Br\u0026auml;ndle \u0026amp; Brandl, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kennedy \u0026amp; Southwood, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Tree species composition, particularly the contrast between broadleaved and coniferous species, also affects the quantity and quality of leaf litter, which in turn influences the soil chemical properties and decomposition rates. This has an impact on organisms that are dependent on soil and litter properties (plants, ground dwelling beetles and spiders ; Augusto et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pywell et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Scherer-Lorenzen et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Differences in canopy structure driven by tree composition also modify the microclimatic conditions of the understory, including light availability, temperature, and humidity, which affect the diversity of understorey plants (Christiansen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Corcket et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, pine plantations are subject to intensive silvicultural practices, such as mechanical weeding stand thinning every 10 years, and clear-cutting every 30\u0026ndash;40 years (Mora et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which can significantly alter community composition compared to unmanaged hedgerows (Brockerhoff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additionally, hedgerows may act as refuges for species following disturbances in pine stands, as has been observed for several arthropod groups in agricultural landscapes after crop harvesting (Montgomery et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pywell et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, multidiversity was significantly higher in hedgerows compared to pine edges. Since each taxonomic group was weighted equally in the multidiversity index, this approach allowed us to aggregate the effect of hedgerows across all groups, which was always positive for each group taken individually, even if sometimes small and not statistically significant. This confirms an overall greater biodiversity in hedgerow habitats than in pine plantations. In contrast, a previous study using the same index in forest environments found no effect of tree composition on multidiversity, mainly due to opposing (positive and negative) responses among the different groups studied (Leidinger et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This finding highlights the interest of multi-taxa approaches to identify habitat conditions that optimize overall biodiversity (Allan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ampoorter et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe weaker response of some groups could be explained by their position in the food chain. Indeed, differences in tree composition and management between habitat type result in forest stands with different structures, which have been shown to directly influence biodiversity of lower trophic levels (i.e., primary producers and primary consumers), as these groups are more closely associated with the trees (Ampoorter et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Scherber et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This was supported by our results, as understory vegetation was the only taxonomic group to exhibit both higher species richness in hedgerows and a distinct community composition compared to pine edges. Following a similar pattern, butterfly communities were also influenced by habitat type, likely due to their dependence on understory vegetation for host plants and floral resources (Ouin \u0026amp; Burel, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; van Halder et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In contrast, the effect of habitat on secondary consumers (i.e. predators) varied, with no consistent pattern across groups. The absence of a significant response of carabids to habitat type is consistent with previous studies in the same study region, which found no significant effect of tree species composition on carabid richness, as most are generalist predators and mobile species (Barbaro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jouveau et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, hunting spider communities, known to benefit from hedgerows and riparian forests in agricultural landscapes (Buddle et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kratschmer et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), were strongly affected by habitat type. This may be attributed to the greater food availability in hedgerows and the lower dispersal capacity of ground dwelling spiders compared to carabids (Feber et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kennedy \u0026amp; Southwood, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Although bird species richness did not differ significantly between hedgerows and pine edges, bird community composition differed between the two habitat types, in line with previous studies comparing coniferous and broadleaved stands (Willson \u0026amp; Comet, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Similar results have been observed in oil palm plantations where interstitial riparian forests shelter a different bird community (Mitchell et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, the lack of a clear response from reptiles to hedgerows, in contrast to findings in agricultural landscapes (Lourdais et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), could be due to their preference for forest edges that provide higher light exposure (Duchesne et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the richness and abundance of reptile species are low in the study area, requiring a longer period of study to observe sufficient numbers of individuals. Overall, hedgerows benefited several taxonomic groups and consistently maintained diversity levels at least as high as those observed along pine edges, highlighting their positive contribution to biodiversity.\u003c/p\u003e\u003cp\u003eOur results not only demonstrated that community composition differed between habitat types, but also highlighted the capacity of hedgerows to shelter more forest species across all taxonomic groups, in contrast to pine plantation edges. A similar pattern was observed in Madagascar, where the effect of tree species richness was stronger on endemic multidiversity than on general multidiversity (Rajaonarimalala et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings are also consistent with those of van Halder et al. (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who worked in the same region than us, and reported that butterfly communities differed between pine and broadleaved stands, with a greater number of forest butterfly species found in broadleaved stands. In general, forest specialists typically benefit from the conditions found in older forests, which are characterized by different abiotic conditions as well as greater accumulation of deadwood, more diverse microhabitats and a well-developed vertical structure (Brockerhoff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Muys et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As such, unmanaged hedgerows likely offer more favorable conditions for these forest species than intensively managed pine forests. Although our study did not include broadleaved forest stands as a sampled habitat, it is reasonable to infer that hedgerows may shelter a subset of species typically found in these forests, since hedgerows are interstitial habitats that cannot fully reproduce abiotic conditions of forest interiors (Collard et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Litza \u0026amp; Diekmann, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, van Halder et al. (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) recorded 34 butterfly species (including five forest specialists) in broadleaved stands within the same region. Of these, 22 species (65%) were also observed in the hedgerows that we sampled, including four of the five forest specialists (\u003cem\u003eArgynnis paphia, Limenitis reducta, Pararge aegeria, Quercusia quercus\u003c/em\u003e), as well as one additional forest specialist (\u003cem\u003eSatyrium ilicis\u003c/em\u003e) not previously recorded in the broadleaved stands. Future studies should consider hedgerow characteristics such as age, structure, and volume, as these variables have been shown to enhance the capacity of hedgerows to replicate forest interior conditions and support forest species in agricultural landscapes (Kratschmer et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Litza et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy partitioning our species data into dominant and rare species, we found that rare species were more numerous in hedgerows than along pine edges, consistent with the pattern observed for overall multidiversity. In contrast, dominant species (both at the regional and local level) were not significantly affected by the presence of hedgerows. Given the well-established sensitivity of rare species to land-use intensity in agricultural systems (Allan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), this effect is likely related to the intensive management of pine stands compared to the lack of management in hedgerows. Moreover, broadleaved hedgerows probably offer more ecological niches for biodiversity than coniferous plantations (Br\u0026auml;ndle \u0026amp; Brandl, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), resulting in a greater capacity to preserve rare species. Future studies in forests should examine the respective contributions of rare and dominant species across several taxa to ecosystem functioning, as it has been shown only for birds that rare species contribute the most to functional diversity (dos Anjos et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leit\u0026atilde;o et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These findings also suggest that rare species may serve as useful indicators of forest management intensity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of landscape on biodiversity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLandscape-scale variables showed contrasting effects on multi-taxonomic diversity. Notably, change in landscape composition, through the increase in broadleaved cover in the landscape, was associated with a decrease in multidiversity. This finding contrasts with predictions from the habitat amount hypothesis, which suggests that increasing the amount of suitable habitat should enhance species richness (Fahrig, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It also contradicts recent global meta-analyses conducted in agricultural landscapes, which found that both semi-natural habitat cover and crop heterogeneity positively influence biodiversity across most taxa (Priyadarshana et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While literature reviews have proposed that these concepts can also apply to forest ecosystems, through variation in tree species composition and forest management regimes (Duflot et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muys et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), our findings suggest that the relationship may be more complex. One explanation, as proposed by Fahrig (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), is that the habitat amount hypothesis is most relevant when considering species specialized on a single habitat type, rather than total biodiversity across a range of taxa. Consistent with this prediction, when we restricted the multidiversity to forest species only, we observed higher values in landscapes with a higher broadleaved cover although this difference was not statistically significant. The general absence of landscape-level effects may be due to the nature of the surrounding matrix, as pine plantations form a suitable habitat for a certain number of species and represent a more permeable matrix for species movement and colonization than crops do in agricultural landscapes (Brockerhoff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, the range of broadleaved cover considered \u0026ldquo;high\u0026rdquo; in our study (14\u0026ndash;36%) remains below the 40% threshold recommended in the review from Arroyo-Rodr\u0026iacute;guez et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for maintaining forest-dwelling species in the landscape. This limitation could have constrained our ability to detect stronger landscape effects. Finally, the unexpected negative relationship between broadleaved cover and multidiversity may be explained by the functional role of hedgerows. As hedgerows represent a sub-optimal habitat compared to broadleaved stands, particularly for forest species, it is possible that species rely on hedgerows in landscapes with low broadleaved cover, but shift their use toward more suitable forest habitats when these are available in greater quantity. A similar pattern has been reported for birds in agricultural landscapes, where hedgerows serve as substitute habitats in more simplified landscapes but become less critical in more forested ones (Hinsley \u0026amp; Bellamy, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNevertheless, three out of the six taxonomic groups examined (understory vegetation, butterflies and reptiles) showed differences in community composition between landscapes with low vs high broadleaved cover. This aligns with general trends observed in agricultural landscapes, where increasing landscape heterogeneity through the addition of semi-natural habitats often leads to changes in species composition (e.g., plants: Cursach et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; butterflies: Perović et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2015\u003c/span\u003e. 2015; carabids: Vanbergen et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In contrast, the spatial connectivity of hedgerows to the nearest broadleaved forest stand, measured as the distance to the nearest neighbour, had no significant effect on multidiversity or on community composition. This result is consistent with a meta-analysis on the habitat amount hypothesis by Martin (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which reported that patch isolation tends to have neutral or weakly negative effects on species richness once habitat amount is accounted for. Similarly, other studies have emphasized the importance of considering landscape configuration, including both composition and spatial arrangement, rather than focusing solely on habitat amount or isolation, as it can significantly influence biodiversity patterns (Arroyo-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Haddad et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finally, at the landscape level, hedgerows may play an important role in habitat supplementation and complementation processes. As previously discussed, hedgerows can supplement broadleaved stands or riparian forests in the area. They can also provide complementary habitats or food for animal species that need different types of resources to complete their life cycle as it has been illustrated in our study area in two case studies involving birds. The Eurasian hoopoe (\u003cem\u003eUpupa epops\u003c/em\u003e) and the great tit (\u003cem\u003eParus major\u003c/em\u003e) both use broadleaved hedgerows and stands for nesting in tree cavities, while foraging along pine stand edges to prey on the major native pest insect species of the area (the pine processionary moth; \u003cem\u003eThaumetopoea pityocampa\u003c/em\u003e) (Barbaro et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Plat et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandscape composition, as reflected by the amount of broadleaved cover, had no significant effect on either dominant or rare species. This contrasts with the findings of Dornelas et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) for weed communities, which highlighted a strong response of dominant weed species to landscape heterogeneity in agricultural lands, while no response was observed for rare species. Although we hypothesize that regionally dominant species in our study were primarily associated with the homogeneous pine plantations matrix, the absence of a replacement of these generalist species by a combination of locally dominant and rarer species could be due to: (1) differing responses among taxonomic groups depending on their dispersal capacity, and (2) the amount of broadleaved cover, which may have been too low to detect an effect on the composition of dominant species.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo our knowledge, our study is the first to demonstrate the value of broadleaved hedgerows for the conservation of multi-taxonomic biodiversity in conifer plantation landscapes. For the majority of the six taxa studied, hedgerows harboured assemblages of species that complement those found in pine forests and they are particularly important for rare and forest species, suggesting that hedgerows provide complementary habitats and resources. We also found that changing landscape composition, through increasing amount of broadleaved habitats, modified community composition for several taxa.\u003c/p\u003e\u003cp\u003eBecause converting pine plantations to broadleaved stands is often impractical, our results suggest that establishing broadleaved hedgerows offers a promising management strategy to enhance biodiversity in conifer plantation landscapes. The near absence of a connectivity effect indicates that hedgerows could be planted anywhere in the landscape, with their biodiversity benefits being modulated by the resulting amount of broadleaved habitats, which should be preserved as well.\u003c/p\u003e\u003cp\u003eFinally, preserving ancient hedgerows and planting new hedgerows can be seen as a Nature-based Solution (Johnson et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as they benefit biodiversity and increase pest control, since they play a role in reducing a major pest of pine plantations (Plat, et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea; Plat, et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb). These interstitial habitats should therefore be used more widely to protect and restore biodiversity and ecosystem functioning in heavily modified forest ecosystems, such as conifer plantation landscapes. Future research should examine how hedgerows mitigate abiotic risks, such as wildfires and windstorms, to fully assess their potential for promoting multifunctionality in forest plantation landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the SUPERB project funded by the European Union Horizon 2020 research and innovation programme under grant agreement no. 101036849 and by the project Horizon Europe eco2adapt (grant agreement No. 101059498)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHJ, IVH and NP designed the study, and wrote the first draft. JBR, OB, TB, JD and SJ collected the data. SJ and OB processed the data relating to spiders. YC, MS and IGC processed the data relating to birds. EA, NP, HJ and IVH analysed the data. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Thomas Ribot, Yannick Mellerin, Aurelien Kohler and Sylvain Piry for their help in collecting the samples and Alex Bush for providing audio recorders.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllan E, Bossdorf O, Dormann CF, Prati D, Gossner MM, Tscharntke T, Bl\u0026uuml;thgen N, Bellach M, Birkhofer K, Boch S, B\u0026ouml;hm S, B\u0026ouml;rschig C, Chatzinotas A, Christ S, Daniel R, Diek\u0026ouml;tter T, Fischer C, Friedl T, Glaser K, Fischer M (2014) Interannual variation in land-use intensity enhances grassland multidiversity. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(1), 308\u0026ndash;313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1312213111\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1312213111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmpoorter E, Barbaro L, Jactel H, Baeten L, Boberg J, Carnol M, Castagneyrol B, Charbonnier Y, Dawud SM, Deconchat M, Smedt PD, Wandeler HD, Guyot V, H\u0026auml;ttenschwiler S, Joly F, Koricheva J, Milligan H, Muys B, Nguyen D, Allan E (2020) Tree diversity is key for promoting the diversity and abundance of forest-associated taxa in Europe. 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Funct Ecol 5(2):202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2389258\u003c/span\u003e\u003cspan address=\"10.2307/2389258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multidiversity, Plants, Birds, Arthropods, Reptiles","lastPublishedDoi":"10.21203/rs.3.rs-7131998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7131998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn forested landscapes, compositional and configurational heterogeneity have been shown to enhance biodiversity. However, changing the type of land cover to improve landscape heterogeneity remains a logistical challenge for forest managers. While hedgerows and forest patches have been widely studied for their role in promoting biodiversity in agricultural landscapes (i.e., \u0026ldquo;bocage\u0026rdquo;), it remains unclear to what extent increasing the share of these interstitial elements would enhance the diversity of different taxonomic groups in plantation landscapes. To address this question, we conducted our study in a homogeneous and monospecific pine plantation landscape in southwestern France, where we compared the diversity of six taxonomic groups in broadleaved hedgerows vs pine stand edges. We also analysed the effect of the connectivity of hedgerows to broadleaved stands and the proportion of broadleaved stands in the landscapes. Beyond species richness and community composition of each taxon, we calculated multidiversity indexes across all groups (using dominant, rare, or forest specialist species). Multidiversity was significantly higher in hedgerows than in pine stand edges. Hedgerows were home to communities with a distinct composition, including a greater abundance of rare species and forest specialist species. Increasing broadleaved cover in the landscape had a negative effect on multidiversity but altered community composition in three out of six groups. The connectivity of hedgerows to broadleaved stands had no significant effect on biodiversity. Preserving or planting broadleaved hedgerows therefore emerges as an effective and practical management method for enhancing biodiversity, particularly of forest specialist species, in pine plantation landscapes.\u003c/p\u003e","manuscriptTitle":"The role of broadleaved hedgerows and landscape composition for biodiversity conservation in a pine plantation context","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-02 11:52:42","doi":"10.21203/rs.3.rs-7131998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T08:42:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T17:54:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43742167273277722594680821936763065927","date":"2025-11-07T20:56:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42248041844997547872761994534579056650","date":"2025-11-07T20:12:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T18:25:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T10:12:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238632087386326048035925702288343352970","date":"2025-09-26T14:26:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43976124805339630485276138702323635094","date":"2025-09-24T16:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T08:46:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T11:44:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T13:02:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2025-07-15T14:59:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"161b23c3-699c-49ce-ba0b-71ee5560214e","owner":[],"postedDate":"October 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:07:46+00:00","versionOfRecord":{"articleIdentity":"rs-7131998","link":"https://doi.org/10.1007/s10531-026-03273-4","journal":{"identity":"biodiversity-and-conservation","isVorOnly":false,"title":"Biodiversity and Conservation"},"publishedOn":"2026-02-13 15:57:23","publishedOnDateReadable":"February 13th, 2026"},"versionCreatedAt":"2025-10-02 11:52:42","video":"","vorDoi":"10.1007/s10531-026-03273-4","vorDoiUrl":"https://doi.org/10.1007/s10531-026-03273-4","workflowStages":[]},"version":"v1","identity":"rs-7131998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7131998","identity":"rs-7131998","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}