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Using process-based indicator species to evaluate ecological corridors in fragmented landscapes
Biological Conservation 201 (2016) 152–159
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Using process-based indicator species to evaluate ecological corridors in fragmented landscapes Déborah Closset-Kopp ⁎, Safaa Wasof, Guillaume Decocq Université de Picardie Jules Verne, Unité de Recherche “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN, FRE 3498 CNRS-UPJV), 1 rue des Louvels, F-80037 Amiens Cedex 1, France
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Article history: Received 22 March 2016 Received in revised form 6 June 2016 Accepted 27 June 2016 Available online xxxx Keywords: Colonization credit Connectivity Dispersal traits Forest herb species Hedgerow Socio-ecological groups
a b s t r a c t Increasing connectivity among habitat patches is assumed to mitigate the effects of fragmentation on biodiversity, leading to the emergence of green and blue infrastructures in public policies as corridors facilitating movements of species within fragmented landscapes. But still, the scientific knowledge and tools for identifying critical features that make a corridor efficient for biodiversity conservation, and for guiding their establishment and management are still scarce. Here, we define three types of indicator species based on their ecological requirements and dispersal traits in the context of forest metacommunities embedded into agricultural landscapes in N France. We then evaluate whether hedgerows can act as corridors for forest herb species and if this can be predicted from the occurrence of the selected indicator species. We show that among each socio-ecological group of forest herb species, (i) the best disperser (“scout” species) indicates habitat suitability for the other species of the group, hence predicts a colonization credit; (ii) the species with intermediate dispersal traits (“median” species) indicates forest species richness and composition of a given hedgerow. In contrast, the worst dispersers (“focal” species) have a low indicator power, as a probable consequence of extinction events occurring in hedgerows, where ecological conditions are suboptimal with respect of forest species. The quality of the corridor increases with the width, height and age of hedgerows, but decreases with increasing land use intensity in adjacent lands. We conclude that process-based indicator species can be valuable tools to assess the efficiency of ecological corridors in fragmented landscapes. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction The increasing fragmentation of natural and semi-natural habitats together with the intensification of landscape management are widely acknowledged as major threats to biodiversity and associated ecosystem services (Saunders et al., 1991). Fragmentation can magnify the effect of climate change on biodiversity by impeding dispersal and other movements of species across landscapes (Bertrand et al., 2011). Increasing connectivity among habitat patches is assumed to promote species movement among patches, hence to reduce the deleterious effects of patch isolation on biodiversity (Niemelä, 2001; Bennett et al., 2006). In this context, the concept of green and blue infrastructures (GBIs) has emerged as a mean for facilitating movements of species within fragmented landscapes. GBIs are ecological networks composed of one type of habitat with a spatially coherent structure which facilitates species movements, hence promoting the conservation or the enhancement of biodiversity, ecosystem functions, and services delivered to human populations (Benedict and McMahon, 2006; Opdam et al., 2006). Typically, an ecological network is composed of core areas connected by corridors. These GBIs may have various spatial structures ⁎ Corresponding author. E-mail address: [email protected] (D. Closset-Kopp).
http://dx.doi.org/10.1016/j.biocon.2016.06.030 0006-3207/© 2016 Elsevier Ltd. All rights reserved.
but often consist of linear corridors more or less interconnected and interacting with the landscape matrix into which they are embedded. Surprisingly, whilst GBIs have progressively permeated public policies in many countries around the world, the scientific knowledge and tools for identifying the critical features of GBIs, for evaluating their functioning and efficacy for biodiversity conservation, and for guiding their establishment and management are still scarce. Designing functional connections is complex since connectivity is a species-specific attribute of landscapes with respect of species' dispersal capacities. It is thus challenging to group species sharing similar habitat requirements and dispersal characteristics and determine indicator species (i.e. a species whose requirements and response to environmental changes encapsulate those of many additional species; Landres et al., 1988), for which the landscape connectivity can be determined and extrapolated to the other species of the group. Indicator species have been widely used to assess environmental conditions, to detect environmental changes or to indicate the diversity of other taxa (Lawton and Gaston, 2001; Halme et al., 2009). For example, umbrella species are species requiring a large area of habitat whose conservation thus confers a protective umbrella to co-occurring species (Murphy and Wilcox, 1986). This concept was further extended by Lambeck (1997) who defined focal species as a subset of the total pool of species in a given landscape with the most demanding survival
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requirements for several factors threatened by anthropogenic stressors. Though this “surrogate species” scheme is a popular conservation strategy, empirical validation is rare and hardly provides support to their superiority over randomly selected species (Andelman and Fagan, 2000; Fleishman et al., 2001a; Roberge and Angelstam, 2004). Surprisingly, indicator species have been rarely used to diagnose ecosystem functioning and forecast future changes. A notable exception is Bani et al. (2002) who used focal bird and mammal species to design woodland ecological networks in an Italian lowland area. Forest patches are important habitats for the maintenance of species diversity within lowlands (Benton et al., 2003) and the delivering of services to populations, given the increasing urbanization, construction of transport infrastructures, and agriculture intensification. It has been suggested that hedgerows can act as ecological corridors between forest patches, allowing forest species to migrate along them (Corbit et al., 1999; Jamoneau et al., 2011), but also as refuges for some forest species (Peterken and Game, 1984; McCollin et al., 2000). However, the role of hedgerows as corridors for forest species is still controversial, with several studies that failed to find forest specialists in hedgerow habitats (e.g. Fritz and Merriam, 1993; French and Cummins, 2001; Wehling and Diekmann, 2008). Two hypotheses may explain these conflicting results. First, due to their dispersal limitation, forest species may miss from hedgerows that are too young, simply because they did not have sufficient time to reach the corridor. Comparative studies of recent vs. ancient forests (reviewed in Flinn and Vellend, 2005) indeed revealed that the latter contain more forest habitat specialists than the former, a pattern mainly explained by the low dispersal capacities of many forest herb species (Peterken and Game, 1984; Grashof-Bokdam, 1997). Good dispersers (e.g. wind- and bird-dispersed species) are over-represented relative to weak dispersers (e.g. gravity- and ant-dispersed species) in the species composition of recent forests (Flinn and Vellend, 2005). Therefore, higher species richness in older sites is typically thought to be a result of the accumulation of weaker dispersers over time (Verheyen et al., 2003). Such dispersal limitations are widely documented for recent forest patches (Hermy and Verheyen, 2007), but whether they apply to linear woody habitat such as hedgerows remains unexplored. Second, due to their recruitment limitation, forest species may be unable to establish or to persist in the hedgerow due to low habitat quality, since high nutrient levels in the soil, especially phosphates, combined to highlight levels on the floor, stimulate the growth of highly competitive species such as Rubus fruticosus coll. and Urtica dioica L. (Verheyen et al., 2003; Van der Veken et al., 2004). Moreover, since they are embedded in more or less intensively managed farmlands, hedgerows are exposed to the drift of agrochemicals, especially biocides (Kleijn and Snoeijing, 1997). Plant species can be categorized into socio-ecological groups, when they share similar habitat requirements, a tenet of phytosociology which groups species according to their observed co-occurrence over ecologically homogeneous land portions (van der Maarel, 2004). Species of a same socio-ecological group thus have similar requirements in terms of light, soil pH, soil nutrient content, soil moisture, light, etc. They can also be classified according to their shared life-history traits into plant functional types (Lavorel et al., 1997). Whenever a species from a given socio-ecological group is found in a given habitat, then it predicts that all species of the same group can be found as well (i.e. the habitat is suitable for them). However, due to dispersal limitation, species that are good dispersers have a higher probability to be found than bad dispersers. In other words, the least dispersal-limited species of a given socio-ecological group is expected to colonize a suitable habitat the first, thereby acting as a “scout” species, a species which predicts that other species of the same socio-ecological group and belonging to the regional species pool will be able to establish later on. In contrast, the most dispersal-limited species of the group is expected to establish the last, and thus predicts the co-occurrence of all other species of the same group. If the habitat is not suitable for a given socio-ecological group, then all species of this group should be absent. However, a
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suitable habitat may become unsuitable over time, under the pressure of surrounding agricultural disturbances (e.g. eutrophication, soil trampling or ploughing, herbicides); in this case, newly dispersed species may become recruitment limited and/or established species may go extinct over time, deterministically or randomly (i.e. independently from their traits; McCune and Vellend, 2015). The overall objective of this study is to identify a set of indicator species, which can help in determining whether a linear woody habitat can act as an efficient corridor within a forest metacommunity, with respect of forest vascular plant species. More precisely, we aim at testing the following hypotheses: H1. All forest plant species can live in hedgerows, as long as they can disperse into them. If this is true, then all species of the local forest species pool will be found in hedgerows, and their frequency therein will increase with their frequency within the forest metacommunity, due to greater diaspore pressure (mass effect). H2. Within a given socio-ecological group, the least dispersal-limited species (hereafter scout species), whenever it occurs in a given hedgerow, indicates habitat suitability with respect to species of this group. If this is true, then a scout species will be the most frequent species of its group and whenever it is missing, all other species of the group will also be absent. The number of scout species found in a hedgerow will correlate with but underestimate total species richness. H3. Within a given socio-ecological group, a species exhibiting intermediate dispersal capacities (hereafter median species), whenever it occurs in a given hedgerow, is a good surrogate for the number and identity of co-occurring species from the same group. If this holds true, then median species will be good predictors of total species richness and species composition of a hedgerow. H4. Within a given socio-ecological group, the most dispersal-limited species (hereafter focal species), whenever it occurs in a hedgerow, predicts the presence of all other species of its group. If this holds true, then a focal species will be the least frequent species of its group, and hedgerows hosting focal species will be more species-rich than those missing them. H5. Habitat quality in hedgerows, as defined by hedgerow's features and adjacent land use intensity, explains the difference between observed and predicted species richness. If this is true, then the observed richness will be lower (greater) than the predicted one whenever the hedgerow is narrow (wide), without (with) a tree layer (cf. species– area relationship; Rosenzweig, 1995), young (old) (cf. species–time relationship; Rosenzweig, 1995), exposed (not exposed) to intensive adjacent land use.
2. Material and methods 2.1. Study area We first selected two 5 × 5 km windows in two contrasted landscapes of the north-eastern part of department Aisne, North France (centres: N 49°50′32; E 3°34′19 and N 49°56′61 E 3°54′58), where the climate is sub-oceanic (mean temperature and annual rainfall of 9.8 °C and 800 mm, respectively) and the dominant geological substrate is Cretaceous chalk, usually covered by Quaternary loess. The first landscape consisted of open fields that were intensively cultivated for cereals, sugar beet and rapeseed, hence comprising few forest fragments and hedgerows; the second one was mostly composed of pastures, meadows and small crop fields with a dense hedgerow network and a number of small forest fragments. This first study area was used to identify indicator species. Then, we sampled a number of hedgerows across departments Somme and Oise (N 49°34′–50°20′; E 1°43′–2°40′) where the climate
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was oceanic to sub-oceanic (mean temperature and annual rainfall of 10.0 °C and 700 mm, respectively) and the substrate more diverse (mostly Quaternary loess, Cretaceous chalk, Thanetian sand, Cretaceous clay and Jurassic limestone). This second study area was chosen because it was spatially distant from the first one but shared the same regional habitat species pool (with very few exceptions), and thus could provide an “external” data set.
2.2. Field surveys In the two windows of the first study area, we exhaustively recorded all vascular plant species occurring in all forest patches (using the flora of Lambinon et al., 2004) (n = 91; see Jamoneau et al., 2011 for details) in order to get a full picture of the forest herb species occurring in the study area (i.e. habitat species pool), including species occurring only (specialists) or mainly (generalists) in the forest herb layer (Appendix S1). We then randomly selected 77 hedgerows in the same landscape windows, as far as possible with contrasted vertical structures (only shrubs, only trees, or shrubs together with trees), historical continuity (from one decade to several centuries old) and adjacent land use (croplands, grasslands or paths). All hedgerows were surveyed for forest plant species by walking along the whole length and on both sides, and checking for the presence of each species of the list previously established from the patch survey. This provided a first species × hedgerow data matrix (hereafter the training matrix). In the second study area, we followed a stratified sampling design. Using old maps and cadastres, we first categorized hedgerows according to their age (i.e. date of first appearance). At the time of the field survey, some hedgerows were excluded because they were either destroyed or strongly degraded. This led to a total of 68 hedgerows, distributed among five age classes: appeared between 1750 and 1800 (9 hedgerows), 1800–1850 (10), 1850–1900 (23), 1950–2000 (18), and after 2000 (8). All were surveyed for forest plant species following the same procedure as for the first study area, to obtain an external data set (hereafter the validation matrix). At the same time, we recorded the following characteristics for each hedgerow: length (m), height (m), width (m; measured between the lateral limits of vegetation strip expansion), age (years; estimated as the median of the time interval between the first map representing the hedgerow and the map immediately before), adjacent land use (cropland, grassland, lane, road), distance to the nearest forest patch (m; set to 0 whenever the hedgerow was connected to a woodland), and cumulative forest area within a radius of 500 m around the hedgerow (m2). To quantify adjacent land use intensity we affected an index to each land use type (from the least to the most intensive, lane: 1; road: 2; meadow: 4; crop field: 5) and then added the two indices corresponding to the land use type on each side of the hedgerow (e.g. for a hedgerow between a meadow and a cultivated field: 4 + 5 = 9).
2.3. Comparison of the ground flora between forest patches and hedgerows To test H1, we listed separately all herbaceous plant species found in the forest patches of the two 5 × 5 km landscape windows, and all herbaceous plant species found in hedgerows of both study areas. A first crude comparison between the two lists aimed at identifying species that missed from hedgerows. Then, for the two 5 × 5 km landscape windows only, we regressed the frequency of all common species into hedgerows against their frequency in patches. Frequencies were computed by dividing the number of patch/hedgerow into which a species occurred and the total number of patches/hedgerows. Prior to analyses, we checked for data normality using a Shapiro–Wilk test.
2.4. Selection of indicator species To group forest species according to their shared habitat requirements, we distributed all species recorded in at least 10% of the 91 forest patches (n = 78) among socio-ecological groups, using Baseflor database (Julve, 1998) as a reference. In order to obtain groups that were as balanced as possible with respect to species number, we considered different synsystematic levels (i.e. alliance, order or class) and merged some closely related units (Appendix S1). This led to seven socioecological groups, for each of which we subsequently built a species × traits data matrix by extracting a suite of five traits relevant for dispersal using LEDA database (Knevel et al., 2003), namely dispersal mode, seed production, seed mass, clonality, total height. In order to visualize how dispersal traits co-varied and how species were arranged along dispersal gradients, the seven resulting matrices were submitted to a Principal Component Analysis (PCA). In all cases, the PCA returned a strong gradient of dispersal capacities. The output PCA diagrams were thus used to identify the three indicator species per socio-ecological group, as follow (see also Appendix S2 for details and an example): scout species was the species with the highest score on the PCA axes accounting for increasing dispersal capacities; the median species was selected among species with a medium score on these axes; and the focal species (sensu Lambeck, 1997) was the species located at the lowest extreme of the PCA axes. When two or more species had similar scores and were thus potential candidates for being a particular indicator species, we selected the one with the most medium frequency in the data set. 2.5. Indicator species testing For each socio-ecological group separately, we computed the number of correct and false predictions by scout and focal species: the absence of a scout species predicts the absence of all species of its group (H2); whilst the presence of a focal species predicts the co-occurrence of all species of its group (H4). We tested whether observed occurrences departed from predicted ones using a binomial test and the binom.test function in R (R Development Core Team 2013). Then, for each hedgerow, we counted the number of scout, median and focal species. This number was regressed against total species richness, after checking for data normality (Shapiro–Wilk test). Because a significant relationship can be obtained with indicator species taken at random (Vellend et al., 2008), we tested the performance of our approach by computing a similar regression analysis but using the number of occurring species among a random subset of the full species list instead of the scout/median/focal species. We repeated the procedure 99 times to derive a 95% confidence interval. For median species only, we further ran Detrended Correspondence Analyses (DCA) of both median species and total species matrices, and extracted hedgerow scores on the first DCA axis. To test whether the species composition of a given hedgerow can be predicted from its median species composition (H3), we regressed hedgerow scores on the first axis of the two DCA, following Vellend et al. (2008). This approach was first applied to the training matrix, to test whether indicator species had significant prediction value within the region where they were defined. Then, to test whether the derived indicator species were relevant over a broader geographic scale, we applied the same testing approach to the external data set (i.e. the validation matrix). To test our fifth hypothesis, we ran a Generalized Linear Model (GLM) with maximum-likelihood estimation and the difference between the median species-based predicted (as derived from the equation of the regression curve) and observed species richness as the dependent variable (Y). Since the latter was a continuous variable with either negative (underestimation) or positive (overestimation) values, we used Gaussian error distributions with an identity link. We incorporated hedgerow age, length, height and width, adjacent land
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use intensity, distance to the nearest forest patch, and the cumulated forest area within a radius of 500 m around the hedgerow, as independent variables (X1, X2, …, Xn). As the last two variables were highly correlated (Spearman rank correlations: rho = − 0.785, P b 0.001), they were never incorporated in a same model. We found no strong correlations (i.e. with a rho N 0.5) among other variables. To determine which factors matter the most to explain the observed difference (Y), we set up a model selection strategy based on candidate models into which each independent variable, alone and in combinations (including interaction terms whenever relevant), were included. To compare candidate models among each other, we used the corrected Akaike's information criterion (AICc) which measures the relative support for each candidate model by simultaneously accounting for model fit (likelihood), model complexity (number of parameters), and sample size (number of observation) and by increasing the relative penalty for model complexity with small data sets (Burnham and Anderson, 2002). Models with a ΔAICc value between 0 and 2 were considered as performing equally well. We used PC-Ord® v. 5 for multivariate analyses, R software and “forecast”, “moments” and “car” packages for regression analyses and the randomization test (Appendix S3), and SPSS® v.11 for GLMs.
3. Results Of the 78 herb species found in the 91 forest patches with a frequency of at least 10%, 74 were also found in hedgerows, indicating that with few exceptions (Orchis purpurea, Platanthera bifolia, Ornithogalum umbellatum and Sanicula europaea) all forest herb species can colonize these linear woody habitats. However, their frequency in hedgerows was always lower than in forest patches except for 8 species, which were all edge species rather than true forest species (Aegopodium podagraria, Anthriscus sylvestris, Cruciata laevipes, Chaerophylum temulum, Galium aparine, Moehringia trinervia, U. dioica, Veronica chamaedrys) (Fig. 1). Remarkably, the frequency of a given forest species in a hedgerow increased with its frequency in forest patches, suggesting a mass effect.
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The 74 species could be distributed among seven socio-ecological groups (Table 1). The PCA of the species × trait matrices allowed identifying scout, median and focal species for each of these groups. The three types of species tended to group towards the top right part, the centre, and the bottom left part of the regression curve, respectively (Fig. 1). This indicates that scout species are very frequent and focal species uncommon, whilst median species show an intermediate frequency. Regression analyses returned a significant relationship between the number of indicator species and the total species richness of a hedgerow, with both training and validation data sets (Fig. 2). This means that the indicator species defined at the sub-region level applied to a broad spatial scale, even with greater R2 values. As expected, scout species richness underestimated the total number of species, the regression line being far below the one obtained from a random set of species. Median species richness tended to be a better predictor of total species richness than a subset of randomly chosen species (Fig. 3), especially for species-poor hedgerows. In contrast, the strength of the relationship between focal and total species richness was very low (R2 = 0.06 and 0.17 for the training and validation dataset, respectively) and focal species overestimated the total number of species in the area where they have been defined. We also found a significant relationship between DCA scores of the median species and total species composition matrices (Fig. 4), for both datasets, indicating that the median species composition also predicted hedgerow species composition. For all but one socio-ecological group (Alliarion; binomial test, P = 1), scout species predict habitat suitability for species of the corresponding group (P b 0.05): when a given hedgerow missed a given scout species, species belonging to the same group were also missing. In contrast, focal species had no significant prediction power (P N 0.05 for all 7 groups): of the total number of species whose presence was predicted, only 40% on average actually co-occurred with the focal species. Differences between median species-based predicted species richness and observed species richness were significantly explained by hedgerow features and adjacent land use intensity (Table 2). Two models had a similar AICc value. Both include the interaction between hedgerow height and width, which had a positive effect on the response variable. The best model further includes hedgerow age (positive effect, i.e. median species underestimate species richness in older hedgerows), whilst the alternative model included land use intensity (negative effect, i.e. median species overestimate species richness of hedgerows in intensively managed landscapes). The other hedgerow characteristics (i.e. length, distance to the nearest forest patch or cumulated forest cover around the hedgerow) had no significant effect. 4. Discussion Here we provide a framework to define three types of indicator species that can be used to evaluate the functionality and conservation value of ecological corridors in complex landscapes. Scout species are early colonizers indicating habitat suitability for a set of species sharing the same ecological requirements. Median species are species with intermediate dispersal traits and good estimators of the actual species richness and composition. In contrast, late colonizers used as focal species failed to predict the most species-rich hedgerows, suggesting that the latter, as linear forest habitats, are more vulnerable to habitat degradation than patches and may undergo greater species extinction rates, lowering the predictive power of these indicator species. Hereafter, we discuss the performance and relevance of each of these indicator species.
Fig. 1. Regression curve (solid line) between the frequency of herb species in forest patches (n = 91) and their frequency in hedgerows (n = 77). The dashed line is the 1:1 line. Dots are species with special symbols for each type of indicator species: stars = scout species, black squares = median species, crosses = focal species. See Appendix S1 for abbreviations of species.
4.1. Hedgerows are suitable habitats for the vast majority of forest herbs We confirm H1: with very few exceptions, all forest herb species found in forest patches were retrieved in hedgerows. This result is in
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Table 1 The seven socio-ecological groups of forest species commonly found in hedgerows with their indicator species as retained in the study. n gives the number of species per group (see also Appendix S1). Socio-ecological group (Julve 1998)
Habitats
Scout species
Median species
Focal species
Anemono nemorosae–Caricetea sylvaticae Luzuletalia sylvaticae
Forest specialists with wide amplitude (n = 11)
Poa nemoralis
Millium effusum
Galium odoratum
Acidophilous forest specialists (n = 10) Calcareous forest specialists (n = 13)
Hyacinthoides non-scripta Viola reichenbachiana
Oxalis acetosella
Mercurialetalia perennis
Lonicera periclymenum Arum maculatum
Trifolio medii–Geranietea sanguinei Aegopodion podagrariae Alliarion petiolae
Calcareous edge species (n = 10) Eutrophic, meso-hydric, semi-heliophilous species (n = 8) Eutrophic, meso-hydric, shade-tolerant edge species (n =
Vicia sepium Anthriscus sylvestris Galium aparine
Potentilla sterilis Aegopodium podagraria Lapsana communis
Mercurialis perennis Viola hirta Veronica hederifoila Viola odorata
14) Eutrophic, hygrophilous edge species (n = 8)
Stachys sylvatica
Circea lutetiana
Veronica montana
Circaeo lutetianae–Stachyetalia sylvaticae
agreement with recent studies which reported 40% (Wehling and Diekmann, 2008), 77% (Wehling and Diekmann, 2009), and 83% (Roy and de Blois, 2006) of the species surveyed in forest patches that occurred also in hedgerows. Remarkably, species' frequency in hedgerows increases with its frequency in surrounding forest patches, suggesting that the occurrence of a given species in a hedgerow is primarily governed by propagule pressure, regardless of its dispersal mode. The positive relationship between propagule pressure and establishment success has been widely documented within the framework of biological invasions (e.g. Colautti et al., 2006), and the availability of propagules has been shown more limiting than the availability of suitable microsites (Ramula et al., 2015). At the metacommunity scale, this suggests a mass effect contributes to mediate species coexistence in hedgerows: the recurrent dispersal events from source habitats (i.e. forest patches) allow species to establish and persist even in habitats where they are poor competitors, according to a source-sink dynamics (Leibold et al., 2004). Interestingly, the frequency of forest specialists in hedgerows was overpredicted by the regression line, whilst the reverse trend was observed for forest generalists. This is consistent with the growing body of comparative studies between post-agricultural (“recent”) and ancient forests (reviewed in Flinn and Vellend, 2005; Hermy and Verheyen, 2007), which show that the latter contain few forest specialists. Many forest herb species are indeed dispersal-limited (Peterken and Game, 1984; Grashof-Bokdam, 1997), especially within fragmented, intensively managed landscapes (Brunet and von Oheimb, 1998). Furthermore, they also often are recruitment-limited where high nutrient levels in the soil, especially phosphorus (P), combined to high light arrival at the ground level, stimulate the growth of
generalist competitors such as R. fruticosus coll. and U. dioica (Verheyen et al., 2003; Hipps et al., 2005). These fast-colonizers increase their biomass more in response to high soil nutrient content, hence prevent the establishment of small-statured forest specialists (Baeten et al., 2011). Moreover, high P concentration in the soil can inhibit arbuscular mycorrhizal fungus (AMF) colonization of forest specialists' plant roots, thus reducing nutrient uptake and host benefit from AMF (Oehl et al., 2003). Consistently, the four species which were missing from hedgerows were rare in forest patches. Three of them were mycotrophic Monocots having their optimum in open habitats, among which two Orchids known for their low tolerance to P and strong recruitment limitation (Hejcman et al., 2010). The fourth species, S. europaea, is a forest specialist known for exhibiting strong recruitment limitations (Gustafsson and Erhlén, 2002). Our findings thus suggest that both dispersal and recruitment limitations, which have been widely documented for recent forest patches, also apply to hedgerows. As expected (H2) scout species were the most frequent species of their corresponding group and were good indicators of habitat suitability but underestimated total species richness. This indicates that together with species sorting, dispersal plays a major role in structuring hedgerow plant communities, and that the colonization process is mostly deterministic since it can be predicted by a combination of habitat requirements and dispersal traits. Only the scout species of the Alliarion socio-ecological group (i.e. semi-heliophilous, nitrogen-demanding edge species) had no predictive value. This group differs from the others in that all species share similar features such as adaptations for long-distance dispersal, a high seed production and a high frequency in forest patches. It is noteworthy that of the eight species that were more frequent in hedgerows than in forest patches, four belong
Fig. 2. Regression lines between the number of indicator species (or randomly chosen species) (X-axis) and the total herb species richness of hedgerows (Y-axis), using the training (left panel) and the validation (right panel) data sets. The 95% confidence interval is added for the regression line obtained with the random set of species.
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Fig. 3. Regression curves between the number of species predicted to occur in hedgerows (X-axis) and the number of species actually observed (Y-axis). Predicted species richness is calculated using the curve equation obtained with median species (solid line) or random species (dashed line) (see Fig. 2); the dotted line shown is the 1:1 line.
to this group (and three others to the closely related Aegopodion group) and most of them have been reported to perform better in hedgerows compared to ancient forests (Endels et al., 2004; Wehling and Diekmann, 2009). As indicators of habitat suitability, scout species can predict the “colonization credit” (i.e. the number of species of the local species pool likely to be gained over time; Jackson and Sax, 2010). A recent study focusing on the colonization of post-agricultural forests by forest plants indicates that several centuries are needed before the colonization credit is paid, a time lag which increases with patch isolation (Naaf and Kolk, 2015). In the case of hedgerows we expect a similar or even longer time lag, especially for forest specialists, due to sub-optimal environmental conditions. 4.2. Median species are better predictors than focal species Median species were better surrogates for species richness and composition of hedgerows than expected by chance (H3). Selecting subsets of species based on their habitat requirements and dispersal traits may thus significantly increase the predictive power of indicator species, thus optimizing conservation returns. This contrasts with some previous studies, which reported that umbrella species were no more effective than randomly selected species in predicting the most speciesrich habitats, but most of these studies dealt with animal taxa (e.g. birds and butterflies in Fleishman et al., 2001a, 2001b; birds,
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amphibians and mammals in Hager et al., 2006). Moreover, it has been argued that to be effective, an umbrella species should have a moderate occurrence rate within a given planning area (Fleishman et al., 2001b; Bried et al., 2007). Here, by choosing species exhibiting intermediate dispersal trait values within each socio-ecological group we retained species that also show intermediate frequencies (see Fig. 1). In contrast, we found no support for H4: focal species failed to predict the co-occurrence of all other species of their corresponding group. This may be due to the relative rarity of most of our focal species in hedgerows (b 10% for 8 of 10 focal species). Moreover, as linear woody elements embedded in a typically changing landscape, hedgerows are maintained in a non-equilibrium state by management-associated disturbances and the species they host are likely affected by ecological drift and random extinction according to stochastic processes (Chase and Myers, 2011). Some relatively good colonizers may go extinct whilst the worst colonizer is still present. A focal species which is found in a hedgerow in the absence of most other species of its group may thus indicate an “extinction debt”, that is despite unsuitable habitat conditions its local extinction is delayed for years to decades or even more, due to a long life span (Jackson and Sax, 2010). All but one (Veronica hederifolia) of the focal species were indeed clonal species known to be able to persist without sexual reproduction for many decades (Kolk and Naaf, 2015). 4.3. Linear forest habitats are vulnerable to habitat degradation We found support for H5: four factors explained the difference between median species — predicted and observed species richness of hedgerows, namely height, width, age and/or adjacent land use that often have been cited as important drivers of species richness in hedgerows (e.g. Corbit et al., 1999; Deckers et al., 2004; Roy and de Blois, 2008; Wehling and Diekmann, 2009). First, the non-significant effect of hedgerow length but positive impact of the interaction between height and width on species richness suggests that the microclimatic conditions created by canopies contribute to habitat quality and thus to the amount of suitable habitat for forest species. This amount matters more than the area per se (i.e. total area being equal, a short, wide hedgerow will host more forest species than a long, narrow one). Moreover, a tall hedgerow (i.e. with a tree layer) can compensate over a weak width and vice-versa. As linear strips of forest-like habitat, hedgerows receive more light from both sides, are more exposed to temperature fluctuations and dessication by winds and summer drought (Murcia, 1995). They thus offer suboptimal conditions for forest specialists, which are typically shade-tolerant perennials (Flinn and Vellend, 2005).
Fig. 4. Regression lines between the hedgerow scores on the first axis of the Detrended Correspondence Analysis (DCA) of the hedgerow × 7 median species data matrix and the hedgerow scores on the first DCA axis of the hedgerow × 74 species data matrix. Left panel: training data set. Right panel: validation data set.
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Table 2 Results of the best generalized linear models accounting for the difference between median species-based predicted and observed species richness in hedgerows. Model
AICc
D
ddl
Predictor
β
SE
z
95% CI
M1 Hedge structure and plus age
362.6
725.4
65
M2 Hedge structure and land-use intensity
364.1
742
65
Intercept H×W Age Intercept H×W Land-use intensity
−22.128 −0.092 0.013 −2.594 −0.096 0.598
9.505 0.0263 0.005 2.30 0.027 0.268
5.420 12.365 6.629 1.272 13.008 4.959
−40.758 −0.144 −0.003 −7.103 −0.148 0.072
P −3.499 −0.041 −0.023 1.914 −0.044 1.124
0.020 0.000 0.010 NS 0.000 0.026
H, height; W, width; ES, estimate; CI, confidence interval; SE, standard error, NS: non-significant (P N 0.05).
Second, the positive effect of hedgerow age indicates that ancient hedgerows were more species-rich than recent ones, consistent with the STR (Rosenzweig, 1995): older hedgerows had more time to accumulate dispersal-limited forest species. This indicates that the processes invoked for explaining species composition differences between recent and ancient forests (review in Flinn and Vellend, 2005; Hermy and Verheyen, 2007) apply to hedgerows. Older hedgerows may also provide forest plant species with more shade and a thicker litter layer on the ground, hence with greater habitat quality to retain them. Third, increased adjacent land use intensity decreased species richness. Species richness was the greatest in hedgerows bordering a lane, a positive effect already reported in the literature and again attributed to favourable microclimatic conditions and reduced disturbances from agricultural activities (Mercer et al., 1999; Wehling and Diekmann, 2008). At the opposite, hedgerows that were bordered by cultivated lands or intensively grazed meadows on both sides were the most species-poor. Mechanical thinning of hedgerows is often more intensive in arable lands to avoid overhanging the cropland by the outer branches of trees and shrubs bordering cultivated fields, minimize crop shading and allow agriculture vehicles to access the field outer limit. Ploughing near the basement, drift of agrochemicals such as herbicides may directly contribute to reduce species diversity in hedgerow bordering crop fields (Kleijn and Snoeijing, 1997). In contrast, along grasslands hedgerows are typically cantilevered, with an overhanging canopy of branches which shades the understorey (Murcia, 1995). Highly competitive species such as R. fruticosus coll. and U. dioica may be hampered from becoming dominant, hence space and resources are likely available for smaller-sized, less light-demanding species (Endels et al., 2004). However, domestic animals (mainly cattle in the study area) often intensively forage in the hedgerow and stay below cantilevered canopies for a long time per day, inducing local eutrophication and soil trampling that can eliminate forest species. Also, fertilizers are heavily used in both croplands and grasslands to increase productivity, and their drift to hedgerows is likely to favour competitive-ruderal species and hence to reduce species diversity (Kleijn and Snoeijing, 1997; Deckers et al., 2004).
given landscape. On the contrary, focal species selected among regionally rare species have limited interest in identifying the most species rich habitats, hence in assessing the functionality of woody corridors in intensively managed agricultural landscapes. Moreover, since the suitability of hedgerows as animal habitats is partly determined by their floristic features, plant species richness and composition are important indicators of general habitat quality (e.g. hedges rich in woody species tend to have relatively high bird species richness; Macdonald and Johnson, 1995). Creating new hedgerows in agricultural landscapes can be a suitable strategy for the maintenance of forest plant biodiversity, but will be efficient only on the long-term. Priority should thus be given to the conservation of existing hedgerows, especially the most ancient ones. Whenever the creation of new hedgerows is implemented, we recommend to plant wide stripes of woody species, including a tree layer. In all cases, a buffer zone along both sides may reduce the negative impact of some adjacent land management practices, such as ploughing, grazing, agrochemical inputs. Our method being transparent and repeatable, we believe it is easily applicable over large regions and provides a robust baseline assessment of species richness, species composition, distribution patterns, and habitat suitability that can guide land use planning and the design of ecological networks.
4.4. Concluding remarks
Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.biocon.2016.06.030.
Increasing habitat connectivity at the landscape-scale is an important component of conservation planning and GBI policies, and the use of habitat corridors as a strategy to mediate such effects is of increasing importance. In the context of global changes and biodiversity crisis, the conservation and restoration of functional ecological network can hardly be delayed until extensive data are available. Moreover, few conservation practitioners have the time, personnel and financial resources to conduct exhaustive inventories over large areas (e.g. Faith and Walker, 1996). There is thus a great interest for surveying only a subset of indicator species. Here, we have demonstrated that it is feasible for vascular forest plant species to identify potential indicator species according to objective ecological criteria, namely habitat requirements and dispersal capacities. More specifically, scout and median species have proven effectiveness in predicting suitability and species richness (and composition) of linear forest habitats, respectively. They are thus valuable tools to hierarchize and map ecological corridors within a
Acknowledgements This work has received a financial support from the project DIVA 3 — FORHAIE 12-MBGD-DIVA-4-CVS-029 (Continuités écologiques dans les territoires ruraux et leurs interfaces) funded by the French Ministère de l'Ecologie et du Développement Durable and was also framed within the ERA-Net BiodivERsA project small FOREST. We thank Jah Wild Skipper, Thomas Jazeix, Axel Fournier and Renaud Morellato for their help during fieldwork and Emilie Gallet-Moron for the GIS help. Appendix A. Supplementary data
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Using process-based indicator species to evaluate ecological corridors in fragmented landscapes Déborah Closset-Kopp ⁎, Safaa Wasof, Guillaume Decocq Université de Picardie Jules Verne, Unité de Recherche “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN, FRE 3498 CNRS-UPJV), 1 rue des Louvels, F-80037 Amiens Cedex 1, France
a r t i c l e
i n f o
Article history: Received 22 March 2016 Received in revised form 6 June 2016 Accepted 27 June 2016 Available online xxxx Keywords: Colonization credit Connectivity Dispersal traits Forest herb species Hedgerow Socio-ecological groups
a b s t r a c t Increasing connectivity among habitat patches is assumed to mitigate the effects of fragmentation on biodiversity, leading to the emergence of green and blue infrastructures in public policies as corridors facilitating movements of species within fragmented landscapes. But still, the scientific knowledge and tools for identifying critical features that make a corridor efficient for biodiversity conservation, and for guiding their establishment and management are still scarce. Here, we define three types of indicator species based on their ecological requirements and dispersal traits in the context of forest metacommunities embedded into agricultural landscapes in N France. We then evaluate whether hedgerows can act as corridors for forest herb species and if this can be predicted from the occurrence of the selected indicator species. We show that among each socio-ecological group of forest herb species, (i) the best disperser (“scout” species) indicates habitat suitability for the other species of the group, hence predicts a colonization credit; (ii) the species with intermediate dispersal traits (“median” species) indicates forest species richness and composition of a given hedgerow. In contrast, the worst dispersers (“focal” species) have a low indicator power, as a probable consequence of extinction events occurring in hedgerows, where ecological conditions are suboptimal with respect of forest species. The quality of the corridor increases with the width, height and age of hedgerows, but decreases with increasing land use intensity in adjacent lands. We conclude that process-based indicator species can be valuable tools to assess the efficiency of ecological corridors in fragmented landscapes. © 2016 Elsevier Ltd. All rights reserved.
1. Introduction The increasing fragmentation of natural and semi-natural habitats together with the intensification of landscape management are widely acknowledged as major threats to biodiversity and associated ecosystem services (Saunders et al., 1991). Fragmentation can magnify the effect of climate change on biodiversity by impeding dispersal and other movements of species across landscapes (Bertrand et al., 2011). Increasing connectivity among habitat patches is assumed to promote species movement among patches, hence to reduce the deleterious effects of patch isolation on biodiversity (Niemelä, 2001; Bennett et al., 2006). In this context, the concept of green and blue infrastructures (GBIs) has emerged as a mean for facilitating movements of species within fragmented landscapes. GBIs are ecological networks composed of one type of habitat with a spatially coherent structure which facilitates species movements, hence promoting the conservation or the enhancement of biodiversity, ecosystem functions, and services delivered to human populations (Benedict and McMahon, 2006; Opdam et al., 2006). Typically, an ecological network is composed of core areas connected by corridors. These GBIs may have various spatial structures ⁎ Corresponding author. E-mail address: [email protected] (D. Closset-Kopp).
http://dx.doi.org/10.1016/j.biocon.2016.06.030 0006-3207/© 2016 Elsevier Ltd. All rights reserved.
but often consist of linear corridors more or less interconnected and interacting with the landscape matrix into which they are embedded. Surprisingly, whilst GBIs have progressively permeated public policies in many countries around the world, the scientific knowledge and tools for identifying the critical features of GBIs, for evaluating their functioning and efficacy for biodiversity conservation, and for guiding their establishment and management are still scarce. Designing functional connections is complex since connectivity is a species-specific attribute of landscapes with respect of species' dispersal capacities. It is thus challenging to group species sharing similar habitat requirements and dispersal characteristics and determine indicator species (i.e. a species whose requirements and response to environmental changes encapsulate those of many additional species; Landres et al., 1988), for which the landscape connectivity can be determined and extrapolated to the other species of the group. Indicator species have been widely used to assess environmental conditions, to detect environmental changes or to indicate the diversity of other taxa (Lawton and Gaston, 2001; Halme et al., 2009). For example, umbrella species are species requiring a large area of habitat whose conservation thus confers a protective umbrella to co-occurring species (Murphy and Wilcox, 1986). This concept was further extended by Lambeck (1997) who defined focal species as a subset of the total pool of species in a given landscape with the most demanding survival
D. Closset-Kopp et al. / Biological Conservation 201 (2016) 152–159
requirements for several factors threatened by anthropogenic stressors. Though this “surrogate species” scheme is a popular conservation strategy, empirical validation is rare and hardly provides support to their superiority over randomly selected species (Andelman and Fagan, 2000; Fleishman et al., 2001a; Roberge and Angelstam, 2004). Surprisingly, indicator species have been rarely used to diagnose ecosystem functioning and forecast future changes. A notable exception is Bani et al. (2002) who used focal bird and mammal species to design woodland ecological networks in an Italian lowland area. Forest patches are important habitats for the maintenance of species diversity within lowlands (Benton et al., 2003) and the delivering of services to populations, given the increasing urbanization, construction of transport infrastructures, and agriculture intensification. It has been suggested that hedgerows can act as ecological corridors between forest patches, allowing forest species to migrate along them (Corbit et al., 1999; Jamoneau et al., 2011), but also as refuges for some forest species (Peterken and Game, 1984; McCollin et al., 2000). However, the role of hedgerows as corridors for forest species is still controversial, with several studies that failed to find forest specialists in hedgerow habitats (e.g. Fritz and Merriam, 1993; French and Cummins, 2001; Wehling and Diekmann, 2008). Two hypotheses may explain these conflicting results. First, due to their dispersal limitation, forest species may miss from hedgerows that are too young, simply because they did not have sufficient time to reach the corridor. Comparative studies of recent vs. ancient forests (reviewed in Flinn and Vellend, 2005) indeed revealed that the latter contain more forest habitat specialists than the former, a pattern mainly explained by the low dispersal capacities of many forest herb species (Peterken and Game, 1984; Grashof-Bokdam, 1997). Good dispersers (e.g. wind- and bird-dispersed species) are over-represented relative to weak dispersers (e.g. gravity- and ant-dispersed species) in the species composition of recent forests (Flinn and Vellend, 2005). Therefore, higher species richness in older sites is typically thought to be a result of the accumulation of weaker dispersers over time (Verheyen et al., 2003). Such dispersal limitations are widely documented for recent forest patches (Hermy and Verheyen, 2007), but whether they apply to linear woody habitat such as hedgerows remains unexplored. Second, due to their recruitment limitation, forest species may be unable to establish or to persist in the hedgerow due to low habitat quality, since high nutrient levels in the soil, especially phosphates, combined to highlight levels on the floor, stimulate the growth of highly competitive species such as Rubus fruticosus coll. and Urtica dioica L. (Verheyen et al., 2003; Van der Veken et al., 2004). Moreover, since they are embedded in more or less intensively managed farmlands, hedgerows are exposed to the drift of agrochemicals, especially biocides (Kleijn and Snoeijing, 1997). Plant species can be categorized into socio-ecological groups, when they share similar habitat requirements, a tenet of phytosociology which groups species according to their observed co-occurrence over ecologically homogeneous land portions (van der Maarel, 2004). Species of a same socio-ecological group thus have similar requirements in terms of light, soil pH, soil nutrient content, soil moisture, light, etc. They can also be classified according to their shared life-history traits into plant functional types (Lavorel et al., 1997). Whenever a species from a given socio-ecological group is found in a given habitat, then it predicts that all species of the same group can be found as well (i.e. the habitat is suitable for them). However, due to dispersal limitation, species that are good dispersers have a higher probability to be found than bad dispersers. In other words, the least dispersal-limited species of a given socio-ecological group is expected to colonize a suitable habitat the first, thereby acting as a “scout” species, a species which predicts that other species of the same socio-ecological group and belonging to the regional species pool will be able to establish later on. In contrast, the most dispersal-limited species of the group is expected to establish the last, and thus predicts the co-occurrence of all other species of the same group. If the habitat is not suitable for a given socio-ecological group, then all species of this group should be absent. However, a
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suitable habitat may become unsuitable over time, under the pressure of surrounding agricultural disturbances (e.g. eutrophication, soil trampling or ploughing, herbicides); in this case, newly dispersed species may become recruitment limited and/or established species may go extinct over time, deterministically or randomly (i.e. independently from their traits; McCune and Vellend, 2015). The overall objective of this study is to identify a set of indicator species, which can help in determining whether a linear woody habitat can act as an efficient corridor within a forest metacommunity, with respect of forest vascular plant species. More precisely, we aim at testing the following hypotheses: H1. All forest plant species can live in hedgerows, as long as they can disperse into them. If this is true, then all species of the local forest species pool will be found in hedgerows, and their frequency therein will increase with their frequency within the forest metacommunity, due to greater diaspore pressure (mass effect). H2. Within a given socio-ecological group, the least dispersal-limited species (hereafter scout species), whenever it occurs in a given hedgerow, indicates habitat suitability with respect to species of this group. If this is true, then a scout species will be the most frequent species of its group and whenever it is missing, all other species of the group will also be absent. The number of scout species found in a hedgerow will correlate with but underestimate total species richness. H3. Within a given socio-ecological group, a species exhibiting intermediate dispersal capacities (hereafter median species), whenever it occurs in a given hedgerow, is a good surrogate for the number and identity of co-occurring species from the same group. If this holds true, then median species will be good predictors of total species richness and species composition of a hedgerow. H4. Within a given socio-ecological group, the most dispersal-limited species (hereafter focal species), whenever it occurs in a hedgerow, predicts the presence of all other species of its group. If this holds true, then a focal species will be the least frequent species of its group, and hedgerows hosting focal species will be more species-rich than those missing them. H5. Habitat quality in hedgerows, as defined by hedgerow's features and adjacent land use intensity, explains the difference between observed and predicted species richness. If this is true, then the observed richness will be lower (greater) than the predicted one whenever the hedgerow is narrow (wide), without (with) a tree layer (cf. species– area relationship; Rosenzweig, 1995), young (old) (cf. species–time relationship; Rosenzweig, 1995), exposed (not exposed) to intensive adjacent land use.
2. Material and methods 2.1. Study area We first selected two 5 × 5 km windows in two contrasted landscapes of the north-eastern part of department Aisne, North France (centres: N 49°50′32; E 3°34′19 and N 49°56′61 E 3°54′58), where the climate is sub-oceanic (mean temperature and annual rainfall of 9.8 °C and 800 mm, respectively) and the dominant geological substrate is Cretaceous chalk, usually covered by Quaternary loess. The first landscape consisted of open fields that were intensively cultivated for cereals, sugar beet and rapeseed, hence comprising few forest fragments and hedgerows; the second one was mostly composed of pastures, meadows and small crop fields with a dense hedgerow network and a number of small forest fragments. This first study area was used to identify indicator species. Then, we sampled a number of hedgerows across departments Somme and Oise (N 49°34′–50°20′; E 1°43′–2°40′) where the climate
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was oceanic to sub-oceanic (mean temperature and annual rainfall of 10.0 °C and 700 mm, respectively) and the substrate more diverse (mostly Quaternary loess, Cretaceous chalk, Thanetian sand, Cretaceous clay and Jurassic limestone). This second study area was chosen because it was spatially distant from the first one but shared the same regional habitat species pool (with very few exceptions), and thus could provide an “external” data set.
2.2. Field surveys In the two windows of the first study area, we exhaustively recorded all vascular plant species occurring in all forest patches (using the flora of Lambinon et al., 2004) (n = 91; see Jamoneau et al., 2011 for details) in order to get a full picture of the forest herb species occurring in the study area (i.e. habitat species pool), including species occurring only (specialists) or mainly (generalists) in the forest herb layer (Appendix S1). We then randomly selected 77 hedgerows in the same landscape windows, as far as possible with contrasted vertical structures (only shrubs, only trees, or shrubs together with trees), historical continuity (from one decade to several centuries old) and adjacent land use (croplands, grasslands or paths). All hedgerows were surveyed for forest plant species by walking along the whole length and on both sides, and checking for the presence of each species of the list previously established from the patch survey. This provided a first species × hedgerow data matrix (hereafter the training matrix). In the second study area, we followed a stratified sampling design. Using old maps and cadastres, we first categorized hedgerows according to their age (i.e. date of first appearance). At the time of the field survey, some hedgerows were excluded because they were either destroyed or strongly degraded. This led to a total of 68 hedgerows, distributed among five age classes: appeared between 1750 and 1800 (9 hedgerows), 1800–1850 (10), 1850–1900 (23), 1950–2000 (18), and after 2000 (8). All were surveyed for forest plant species following the same procedure as for the first study area, to obtain an external data set (hereafter the validation matrix). At the same time, we recorded the following characteristics for each hedgerow: length (m), height (m), width (m; measured between the lateral limits of vegetation strip expansion), age (years; estimated as the median of the time interval between the first map representing the hedgerow and the map immediately before), adjacent land use (cropland, grassland, lane, road), distance to the nearest forest patch (m; set to 0 whenever the hedgerow was connected to a woodland), and cumulative forest area within a radius of 500 m around the hedgerow (m2). To quantify adjacent land use intensity we affected an index to each land use type (from the least to the most intensive, lane: 1; road: 2; meadow: 4; crop field: 5) and then added the two indices corresponding to the land use type on each side of the hedgerow (e.g. for a hedgerow between a meadow and a cultivated field: 4 + 5 = 9).
2.3. Comparison of the ground flora between forest patches and hedgerows To test H1, we listed separately all herbaceous plant species found in the forest patches of the two 5 × 5 km landscape windows, and all herbaceous plant species found in hedgerows of both study areas. A first crude comparison between the two lists aimed at identifying species that missed from hedgerows. Then, for the two 5 × 5 km landscape windows only, we regressed the frequency of all common species into hedgerows against their frequency in patches. Frequencies were computed by dividing the number of patch/hedgerow into which a species occurred and the total number of patches/hedgerows. Prior to analyses, we checked for data normality using a Shapiro–Wilk test.
2.4. Selection of indicator species To group forest species according to their shared habitat requirements, we distributed all species recorded in at least 10% of the 91 forest patches (n = 78) among socio-ecological groups, using Baseflor database (Julve, 1998) as a reference. In order to obtain groups that were as balanced as possible with respect to species number, we considered different synsystematic levels (i.e. alliance, order or class) and merged some closely related units (Appendix S1). This led to seven socioecological groups, for each of which we subsequently built a species × traits data matrix by extracting a suite of five traits relevant for dispersal using LEDA database (Knevel et al., 2003), namely dispersal mode, seed production, seed mass, clonality, total height. In order to visualize how dispersal traits co-varied and how species were arranged along dispersal gradients, the seven resulting matrices were submitted to a Principal Component Analysis (PCA). In all cases, the PCA returned a strong gradient of dispersal capacities. The output PCA diagrams were thus used to identify the three indicator species per socio-ecological group, as follow (see also Appendix S2 for details and an example): scout species was the species with the highest score on the PCA axes accounting for increasing dispersal capacities; the median species was selected among species with a medium score on these axes; and the focal species (sensu Lambeck, 1997) was the species located at the lowest extreme of the PCA axes. When two or more species had similar scores and were thus potential candidates for being a particular indicator species, we selected the one with the most medium frequency in the data set. 2.5. Indicator species testing For each socio-ecological group separately, we computed the number of correct and false predictions by scout and focal species: the absence of a scout species predicts the absence of all species of its group (H2); whilst the presence of a focal species predicts the co-occurrence of all species of its group (H4). We tested whether observed occurrences departed from predicted ones using a binomial test and the binom.test function in R (R Development Core Team 2013). Then, for each hedgerow, we counted the number of scout, median and focal species. This number was regressed against total species richness, after checking for data normality (Shapiro–Wilk test). Because a significant relationship can be obtained with indicator species taken at random (Vellend et al., 2008), we tested the performance of our approach by computing a similar regression analysis but using the number of occurring species among a random subset of the full species list instead of the scout/median/focal species. We repeated the procedure 99 times to derive a 95% confidence interval. For median species only, we further ran Detrended Correspondence Analyses (DCA) of both median species and total species matrices, and extracted hedgerow scores on the first DCA axis. To test whether the species composition of a given hedgerow can be predicted from its median species composition (H3), we regressed hedgerow scores on the first axis of the two DCA, following Vellend et al. (2008). This approach was first applied to the training matrix, to test whether indicator species had significant prediction value within the region where they were defined. Then, to test whether the derived indicator species were relevant over a broader geographic scale, we applied the same testing approach to the external data set (i.e. the validation matrix). To test our fifth hypothesis, we ran a Generalized Linear Model (GLM) with maximum-likelihood estimation and the difference between the median species-based predicted (as derived from the equation of the regression curve) and observed species richness as the dependent variable (Y). Since the latter was a continuous variable with either negative (underestimation) or positive (overestimation) values, we used Gaussian error distributions with an identity link. We incorporated hedgerow age, length, height and width, adjacent land
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use intensity, distance to the nearest forest patch, and the cumulated forest area within a radius of 500 m around the hedgerow, as independent variables (X1, X2, …, Xn). As the last two variables were highly correlated (Spearman rank correlations: rho = − 0.785, P b 0.001), they were never incorporated in a same model. We found no strong correlations (i.e. with a rho N 0.5) among other variables. To determine which factors matter the most to explain the observed difference (Y), we set up a model selection strategy based on candidate models into which each independent variable, alone and in combinations (including interaction terms whenever relevant), were included. To compare candidate models among each other, we used the corrected Akaike's information criterion (AICc) which measures the relative support for each candidate model by simultaneously accounting for model fit (likelihood), model complexity (number of parameters), and sample size (number of observation) and by increasing the relative penalty for model complexity with small data sets (Burnham and Anderson, 2002). Models with a ΔAICc value between 0 and 2 were considered as performing equally well. We used PC-Ord® v. 5 for multivariate analyses, R software and “forecast”, “moments” and “car” packages for regression analyses and the randomization test (Appendix S3), and SPSS® v.11 for GLMs.
3. Results Of the 78 herb species found in the 91 forest patches with a frequency of at least 10%, 74 were also found in hedgerows, indicating that with few exceptions (Orchis purpurea, Platanthera bifolia, Ornithogalum umbellatum and Sanicula europaea) all forest herb species can colonize these linear woody habitats. However, their frequency in hedgerows was always lower than in forest patches except for 8 species, which were all edge species rather than true forest species (Aegopodium podagraria, Anthriscus sylvestris, Cruciata laevipes, Chaerophylum temulum, Galium aparine, Moehringia trinervia, U. dioica, Veronica chamaedrys) (Fig. 1). Remarkably, the frequency of a given forest species in a hedgerow increased with its frequency in forest patches, suggesting a mass effect.
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The 74 species could be distributed among seven socio-ecological groups (Table 1). The PCA of the species × trait matrices allowed identifying scout, median and focal species for each of these groups. The three types of species tended to group towards the top right part, the centre, and the bottom left part of the regression curve, respectively (Fig. 1). This indicates that scout species are very frequent and focal species uncommon, whilst median species show an intermediate frequency. Regression analyses returned a significant relationship between the number of indicator species and the total species richness of a hedgerow, with both training and validation data sets (Fig. 2). This means that the indicator species defined at the sub-region level applied to a broad spatial scale, even with greater R2 values. As expected, scout species richness underestimated the total number of species, the regression line being far below the one obtained from a random set of species. Median species richness tended to be a better predictor of total species richness than a subset of randomly chosen species (Fig. 3), especially for species-poor hedgerows. In contrast, the strength of the relationship between focal and total species richness was very low (R2 = 0.06 and 0.17 for the training and validation dataset, respectively) and focal species overestimated the total number of species in the area where they have been defined. We also found a significant relationship between DCA scores of the median species and total species composition matrices (Fig. 4), for both datasets, indicating that the median species composition also predicted hedgerow species composition. For all but one socio-ecological group (Alliarion; binomial test, P = 1), scout species predict habitat suitability for species of the corresponding group (P b 0.05): when a given hedgerow missed a given scout species, species belonging to the same group were also missing. In contrast, focal species had no significant prediction power (P N 0.05 for all 7 groups): of the total number of species whose presence was predicted, only 40% on average actually co-occurred with the focal species. Differences between median species-based predicted species richness and observed species richness were significantly explained by hedgerow features and adjacent land use intensity (Table 2). Two models had a similar AICc value. Both include the interaction between hedgerow height and width, which had a positive effect on the response variable. The best model further includes hedgerow age (positive effect, i.e. median species underestimate species richness in older hedgerows), whilst the alternative model included land use intensity (negative effect, i.e. median species overestimate species richness of hedgerows in intensively managed landscapes). The other hedgerow characteristics (i.e. length, distance to the nearest forest patch or cumulated forest cover around the hedgerow) had no significant effect. 4. Discussion Here we provide a framework to define three types of indicator species that can be used to evaluate the functionality and conservation value of ecological corridors in complex landscapes. Scout species are early colonizers indicating habitat suitability for a set of species sharing the same ecological requirements. Median species are species with intermediate dispersal traits and good estimators of the actual species richness and composition. In contrast, late colonizers used as focal species failed to predict the most species-rich hedgerows, suggesting that the latter, as linear forest habitats, are more vulnerable to habitat degradation than patches and may undergo greater species extinction rates, lowering the predictive power of these indicator species. Hereafter, we discuss the performance and relevance of each of these indicator species.
Fig. 1. Regression curve (solid line) between the frequency of herb species in forest patches (n = 91) and their frequency in hedgerows (n = 77). The dashed line is the 1:1 line. Dots are species with special symbols for each type of indicator species: stars = scout species, black squares = median species, crosses = focal species. See Appendix S1 for abbreviations of species.
4.1. Hedgerows are suitable habitats for the vast majority of forest herbs We confirm H1: with very few exceptions, all forest herb species found in forest patches were retrieved in hedgerows. This result is in
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Table 1 The seven socio-ecological groups of forest species commonly found in hedgerows with their indicator species as retained in the study. n gives the number of species per group (see also Appendix S1). Socio-ecological group (Julve 1998)
Habitats
Scout species
Median species
Focal species
Anemono nemorosae–Caricetea sylvaticae Luzuletalia sylvaticae
Forest specialists with wide amplitude (n = 11)
Poa nemoralis
Millium effusum
Galium odoratum
Acidophilous forest specialists (n = 10) Calcareous forest specialists (n = 13)
Hyacinthoides non-scripta Viola reichenbachiana
Oxalis acetosella
Mercurialetalia perennis
Lonicera periclymenum Arum maculatum
Trifolio medii–Geranietea sanguinei Aegopodion podagrariae Alliarion petiolae
Calcareous edge species (n = 10) Eutrophic, meso-hydric, semi-heliophilous species (n = 8) Eutrophic, meso-hydric, shade-tolerant edge species (n =
Vicia sepium Anthriscus sylvestris Galium aparine
Potentilla sterilis Aegopodium podagraria Lapsana communis
Mercurialis perennis Viola hirta Veronica hederifoila Viola odorata
14) Eutrophic, hygrophilous edge species (n = 8)
Stachys sylvatica
Circea lutetiana
Veronica montana
Circaeo lutetianae–Stachyetalia sylvaticae
agreement with recent studies which reported 40% (Wehling and Diekmann, 2008), 77% (Wehling and Diekmann, 2009), and 83% (Roy and de Blois, 2006) of the species surveyed in forest patches that occurred also in hedgerows. Remarkably, species' frequency in hedgerows increases with its frequency in surrounding forest patches, suggesting that the occurrence of a given species in a hedgerow is primarily governed by propagule pressure, regardless of its dispersal mode. The positive relationship between propagule pressure and establishment success has been widely documented within the framework of biological invasions (e.g. Colautti et al., 2006), and the availability of propagules has been shown more limiting than the availability of suitable microsites (Ramula et al., 2015). At the metacommunity scale, this suggests a mass effect contributes to mediate species coexistence in hedgerows: the recurrent dispersal events from source habitats (i.e. forest patches) allow species to establish and persist even in habitats where they are poor competitors, according to a source-sink dynamics (Leibold et al., 2004). Interestingly, the frequency of forest specialists in hedgerows was overpredicted by the regression line, whilst the reverse trend was observed for forest generalists. This is consistent with the growing body of comparative studies between post-agricultural (“recent”) and ancient forests (reviewed in Flinn and Vellend, 2005; Hermy and Verheyen, 2007), which show that the latter contain few forest specialists. Many forest herb species are indeed dispersal-limited (Peterken and Game, 1984; Grashof-Bokdam, 1997), especially within fragmented, intensively managed landscapes (Brunet and von Oheimb, 1998). Furthermore, they also often are recruitment-limited where high nutrient levels in the soil, especially phosphorus (P), combined to high light arrival at the ground level, stimulate the growth of
generalist competitors such as R. fruticosus coll. and U. dioica (Verheyen et al., 2003; Hipps et al., 2005). These fast-colonizers increase their biomass more in response to high soil nutrient content, hence prevent the establishment of small-statured forest specialists (Baeten et al., 2011). Moreover, high P concentration in the soil can inhibit arbuscular mycorrhizal fungus (AMF) colonization of forest specialists' plant roots, thus reducing nutrient uptake and host benefit from AMF (Oehl et al., 2003). Consistently, the four species which were missing from hedgerows were rare in forest patches. Three of them were mycotrophic Monocots having their optimum in open habitats, among which two Orchids known for their low tolerance to P and strong recruitment limitation (Hejcman et al., 2010). The fourth species, S. europaea, is a forest specialist known for exhibiting strong recruitment limitations (Gustafsson and Erhlén, 2002). Our findings thus suggest that both dispersal and recruitment limitations, which have been widely documented for recent forest patches, also apply to hedgerows. As expected (H2) scout species were the most frequent species of their corresponding group and were good indicators of habitat suitability but underestimated total species richness. This indicates that together with species sorting, dispersal plays a major role in structuring hedgerow plant communities, and that the colonization process is mostly deterministic since it can be predicted by a combination of habitat requirements and dispersal traits. Only the scout species of the Alliarion socio-ecological group (i.e. semi-heliophilous, nitrogen-demanding edge species) had no predictive value. This group differs from the others in that all species share similar features such as adaptations for long-distance dispersal, a high seed production and a high frequency in forest patches. It is noteworthy that of the eight species that were more frequent in hedgerows than in forest patches, four belong
Fig. 2. Regression lines between the number of indicator species (or randomly chosen species) (X-axis) and the total herb species richness of hedgerows (Y-axis), using the training (left panel) and the validation (right panel) data sets. The 95% confidence interval is added for the regression line obtained with the random set of species.
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Fig. 3. Regression curves between the number of species predicted to occur in hedgerows (X-axis) and the number of species actually observed (Y-axis). Predicted species richness is calculated using the curve equation obtained with median species (solid line) or random species (dashed line) (see Fig. 2); the dotted line shown is the 1:1 line.
to this group (and three others to the closely related Aegopodion group) and most of them have been reported to perform better in hedgerows compared to ancient forests (Endels et al., 2004; Wehling and Diekmann, 2009). As indicators of habitat suitability, scout species can predict the “colonization credit” (i.e. the number of species of the local species pool likely to be gained over time; Jackson and Sax, 2010). A recent study focusing on the colonization of post-agricultural forests by forest plants indicates that several centuries are needed before the colonization credit is paid, a time lag which increases with patch isolation (Naaf and Kolk, 2015). In the case of hedgerows we expect a similar or even longer time lag, especially for forest specialists, due to sub-optimal environmental conditions. 4.2. Median species are better predictors than focal species Median species were better surrogates for species richness and composition of hedgerows than expected by chance (H3). Selecting subsets of species based on their habitat requirements and dispersal traits may thus significantly increase the predictive power of indicator species, thus optimizing conservation returns. This contrasts with some previous studies, which reported that umbrella species were no more effective than randomly selected species in predicting the most speciesrich habitats, but most of these studies dealt with animal taxa (e.g. birds and butterflies in Fleishman et al., 2001a, 2001b; birds,
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amphibians and mammals in Hager et al., 2006). Moreover, it has been argued that to be effective, an umbrella species should have a moderate occurrence rate within a given planning area (Fleishman et al., 2001b; Bried et al., 2007). Here, by choosing species exhibiting intermediate dispersal trait values within each socio-ecological group we retained species that also show intermediate frequencies (see Fig. 1). In contrast, we found no support for H4: focal species failed to predict the co-occurrence of all other species of their corresponding group. This may be due to the relative rarity of most of our focal species in hedgerows (b 10% for 8 of 10 focal species). Moreover, as linear woody elements embedded in a typically changing landscape, hedgerows are maintained in a non-equilibrium state by management-associated disturbances and the species they host are likely affected by ecological drift and random extinction according to stochastic processes (Chase and Myers, 2011). Some relatively good colonizers may go extinct whilst the worst colonizer is still present. A focal species which is found in a hedgerow in the absence of most other species of its group may thus indicate an “extinction debt”, that is despite unsuitable habitat conditions its local extinction is delayed for years to decades or even more, due to a long life span (Jackson and Sax, 2010). All but one (Veronica hederifolia) of the focal species were indeed clonal species known to be able to persist without sexual reproduction for many decades (Kolk and Naaf, 2015). 4.3. Linear forest habitats are vulnerable to habitat degradation We found support for H5: four factors explained the difference between median species — predicted and observed species richness of hedgerows, namely height, width, age and/or adjacent land use that often have been cited as important drivers of species richness in hedgerows (e.g. Corbit et al., 1999; Deckers et al., 2004; Roy and de Blois, 2008; Wehling and Diekmann, 2009). First, the non-significant effect of hedgerow length but positive impact of the interaction between height and width on species richness suggests that the microclimatic conditions created by canopies contribute to habitat quality and thus to the amount of suitable habitat for forest species. This amount matters more than the area per se (i.e. total area being equal, a short, wide hedgerow will host more forest species than a long, narrow one). Moreover, a tall hedgerow (i.e. with a tree layer) can compensate over a weak width and vice-versa. As linear strips of forest-like habitat, hedgerows receive more light from both sides, are more exposed to temperature fluctuations and dessication by winds and summer drought (Murcia, 1995). They thus offer suboptimal conditions for forest specialists, which are typically shade-tolerant perennials (Flinn and Vellend, 2005).
Fig. 4. Regression lines between the hedgerow scores on the first axis of the Detrended Correspondence Analysis (DCA) of the hedgerow × 7 median species data matrix and the hedgerow scores on the first DCA axis of the hedgerow × 74 species data matrix. Left panel: training data set. Right panel: validation data set.
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Table 2 Results of the best generalized linear models accounting for the difference between median species-based predicted and observed species richness in hedgerows. Model
AICc
D
ddl
Predictor
β
SE
z
95% CI
M1 Hedge structure and plus age
362.6
725.4
65
M2 Hedge structure and land-use intensity
364.1
742
65
Intercept H×W Age Intercept H×W Land-use intensity
−22.128 −0.092 0.013 −2.594 −0.096 0.598
9.505 0.0263 0.005 2.30 0.027 0.268
5.420 12.365 6.629 1.272 13.008 4.959
−40.758 −0.144 −0.003 −7.103 −0.148 0.072
P −3.499 −0.041 −0.023 1.914 −0.044 1.124
0.020 0.000 0.010 NS 0.000 0.026
H, height; W, width; ES, estimate; CI, confidence interval; SE, standard error, NS: non-significant (P N 0.05).
Second, the positive effect of hedgerow age indicates that ancient hedgerows were more species-rich than recent ones, consistent with the STR (Rosenzweig, 1995): older hedgerows had more time to accumulate dispersal-limited forest species. This indicates that the processes invoked for explaining species composition differences between recent and ancient forests (review in Flinn and Vellend, 2005; Hermy and Verheyen, 2007) apply to hedgerows. Older hedgerows may also provide forest plant species with more shade and a thicker litter layer on the ground, hence with greater habitat quality to retain them. Third, increased adjacent land use intensity decreased species richness. Species richness was the greatest in hedgerows bordering a lane, a positive effect already reported in the literature and again attributed to favourable microclimatic conditions and reduced disturbances from agricultural activities (Mercer et al., 1999; Wehling and Diekmann, 2008). At the opposite, hedgerows that were bordered by cultivated lands or intensively grazed meadows on both sides were the most species-poor. Mechanical thinning of hedgerows is often more intensive in arable lands to avoid overhanging the cropland by the outer branches of trees and shrubs bordering cultivated fields, minimize crop shading and allow agriculture vehicles to access the field outer limit. Ploughing near the basement, drift of agrochemicals such as herbicides may directly contribute to reduce species diversity in hedgerow bordering crop fields (Kleijn and Snoeijing, 1997). In contrast, along grasslands hedgerows are typically cantilevered, with an overhanging canopy of branches which shades the understorey (Murcia, 1995). Highly competitive species such as R. fruticosus coll. and U. dioica may be hampered from becoming dominant, hence space and resources are likely available for smaller-sized, less light-demanding species (Endels et al., 2004). However, domestic animals (mainly cattle in the study area) often intensively forage in the hedgerow and stay below cantilevered canopies for a long time per day, inducing local eutrophication and soil trampling that can eliminate forest species. Also, fertilizers are heavily used in both croplands and grasslands to increase productivity, and their drift to hedgerows is likely to favour competitive-ruderal species and hence to reduce species diversity (Kleijn and Snoeijing, 1997; Deckers et al., 2004).
given landscape. On the contrary, focal species selected among regionally rare species have limited interest in identifying the most species rich habitats, hence in assessing the functionality of woody corridors in intensively managed agricultural landscapes. Moreover, since the suitability of hedgerows as animal habitats is partly determined by their floristic features, plant species richness and composition are important indicators of general habitat quality (e.g. hedges rich in woody species tend to have relatively high bird species richness; Macdonald and Johnson, 1995). Creating new hedgerows in agricultural landscapes can be a suitable strategy for the maintenance of forest plant biodiversity, but will be efficient only on the long-term. Priority should thus be given to the conservation of existing hedgerows, especially the most ancient ones. Whenever the creation of new hedgerows is implemented, we recommend to plant wide stripes of woody species, including a tree layer. In all cases, a buffer zone along both sides may reduce the negative impact of some adjacent land management practices, such as ploughing, grazing, agrochemical inputs. Our method being transparent and repeatable, we believe it is easily applicable over large regions and provides a robust baseline assessment of species richness, species composition, distribution patterns, and habitat suitability that can guide land use planning and the design of ecological networks.
4.4. Concluding remarks
Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.biocon.2016.06.030.
Increasing habitat connectivity at the landscape-scale is an important component of conservation planning and GBI policies, and the use of habitat corridors as a strategy to mediate such effects is of increasing importance. In the context of global changes and biodiversity crisis, the conservation and restoration of functional ecological network can hardly be delayed until extensive data are available. Moreover, few conservation practitioners have the time, personnel and financial resources to conduct exhaustive inventories over large areas (e.g. Faith and Walker, 1996). There is thus a great interest for surveying only a subset of indicator species. Here, we have demonstrated that it is feasible for vascular forest plant species to identify potential indicator species according to objective ecological criteria, namely habitat requirements and dispersal capacities. More specifically, scout and median species have proven effectiveness in predicting suitability and species richness (and composition) of linear forest habitats, respectively. They are thus valuable tools to hierarchize and map ecological corridors within a
Acknowledgements This work has received a financial support from the project DIVA 3 — FORHAIE 12-MBGD-DIVA-4-CVS-029 (Continuités écologiques dans les territoires ruraux et leurs interfaces) funded by the French Ministère de l'Ecologie et du Développement Durable and was also framed within the ERA-Net BiodivERsA project small FOREST. We thank Jah Wild Skipper, Thomas Jazeix, Axel Fournier and Renaud Morellato for their help during fieldwork and Emilie Gallet-Moron for the GIS help. Appendix A. Supplementary data
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