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Forest loss and fragmentation effects on woody plant species richness in Great Britain
Forest Ecology and Management 260 (2010) 472–479
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Forest loss and fragmentation effects on woody plant species richness in Great Britain Fábio Suzart de Albuquerque ∗ , Marta Rueda Department of Ecology, Faculty of Biology, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
a r t i c l e
i n f o
Article history: Received 11 February 2010 Received in revised form 26 April 2010 Accepted 2 May 2010 Keywords: Habitat loss Habitat fragmentation Biodiversity Species richness
a b s t r a c t Studies on the effect of forest loss and fragmentation on plant species richness at different spatial scales have yielded contradictory results that have been attributed to different ways to measure fragmentation. The main goal of this study was to investigate the independent and combined effects of forest loss and habitat fragmentation on woody species richness. Woody species were grouped according to their habitat requirements (forest specialists, forest generalists, non-forest and total woody species). We used regression models to investigate the effect of fragmentation and human occupation on woody species richness. The underlying factors were investigated by partitioning of the variation, i.e. decomposing the variation in species richness between the pure effects of forest loss, fragmentation and human occupation. The relationship between species richness and forest loss resulted extremely non-linear. The models for forest specialist, generalist and total woody species richness accounted for 35%, 31% and 33% of the total variance respectively. Model for non-forest species richness only accounted for 7% of the total variance. The largest fraction of variability in species richness was accounted by fragmentation variables for all groups (except for non-forest species). Results emphasize the larger independent effect of fragmentation over forest loss, suggesting that species variation is mainly conditioned by the spatial configuration of the habitat. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Habitat destruction is generally regarded as the single major cause of global biodiversity loss, a problem that has led ecologists to face the question of how much habitat is enough for a species to persist (Pimm and Raven, 2000; Balmford and Bond, 2005; Foley et al., 2005). An important, common consequence of habitat loss is fragmentation, a process that occurs when areas of continuous habitat are broken into smaller and discontinuous habitat patches (e.g. Fahrig, 2003; Cayuela, 2009). To what extent fragmentation affects the impact of habitat loss on diversity has been widely debated, probably because empirical studies appear to have failed when attempting to differentiate the consequences derived from each process (but see Bascompte and Rodríguez, 2001; also see Fahrig, 2003 for an in-deep review of this issue). In this regard, some authors notice that while loss of habitat area generally compromises the persistence of populations, both positive and negative effects of fragmentation are observed in empirical studies. This has fuelled the view that conservation efforts should be directed to maintain or increase the amount of habitat without paying much attention to the extent of its fragmentation (Fahrig, 2003).
∗ Corresponding author. Tel.: +34 605380608. E-mail address: [email protected] (F.S. de Albuquerque). 0378-1127/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2010.05.003
In contrast, other authors emphasize that inabilities to differentiate empirically effects of habitat loss that occur through fragmentation have precluded to show the true importance of the latter process, and predict that, once these technical shortcomings had been solved, fragmentation may appear to be a key driver of habitat loss-derived biodiversity declines, at least under some circumstances (Boutin and Hebert, 2002). Specifically, these authors predict that fragmentation will become of chief importance for species persistence once habitat loss has exceeded a certain threshold level which, for the case of forest habitats, is expected to be located between 50% and 20% of forest cover. In this regard, partial regression techniques allow partitioning the variation of a response variable into portions explained independently and in concert by a set of explanatory variables (Legendre and Legendre, 1998; Lobo et al., 2002), and we use them here to check this prediction as a mean to understand the extent at which forest amount and fragmentation determine Great Britain’s woody plant species richness. Among the conceptual frameworks that have driven habitat loss and fragmentation research, the theory of island biogeography (MacArthur and Wilson, 2001) has played a prominent role, generating many insights that constitute most of the basic knowledge of this field. According to this theory, species richness in habitat patches would be a function of patch size and the degree of isolation, so that smaller and more isolated patches will support fewer species (e.g. Collinge, 1995). However, this argument is likely to be
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more valid when we focus on habitat specialists; that is, on species that are closely linked to the shrinking habitat (Tilman et al., 1994). Whereas if we consider all the species found in a particular habitat, species richness may be unaffected, or even enhanced, by habitat loss and fragmentation. This is because smaller and more isolated habitat fragments may encourage shifts of competitive regimes (i.e. suboptimal habitat conditions for specialist species would cause them to be less effective when competing with less specialized species; see Tilman et al., 1994), and thus favour the establishment of early-successional and transient species, thus leading to an increase in richness in the landscape (Holt, 1997; Debinski and Holt, 2000; Langlois et al., 2001). An important issue regarding biodiversity loss is human activity. However, in this regard, the literature shows contrasting hypotheses. On the one hand, previous studies show that in a given area, human activity usually causes the extinction of native species (McKinney, 2002), or can reduce species richness in tree communities (Farwig et al., 2008). On the other hand, human activity may be positively related to plant richness (McKinney, 2002). The reasons for this are twofold. Firstly, people usually live in areas of high biodiversity (Cayuela et al., 2006). Secondly, human activity may cause habitat diversification (Kühn et al., 2004; Luck et al., 2004; Di Giulio et al., 2009). In the first case there is a spurious relationship mediated by climate (i.e. there are more people living in mild or warm areas than in cold regions), whereas in the second case there is a causal relationship. Additionally, the variables characterizing forest and human variables are intercorrelated, since forest loss and fragmentation are usually affected by human activities (i.e. deforestation by urbanization) (Cayuela et al., 2006). Yet, it is important to assess to what extent forest loss, forest fragmentation and human-related factors affect the geographical distribution of woody species. Here we use a novel approach to investigate the independent and combined effects of forest amount and fragmentation on native angiosperm woody plant species richness in Great Britain, one of the most highly deforested areas of Europe (see European Environment Agency, 2006). Initially, we describe the geographical distribution of the richness of four woody plant species groups, including forest specialists (exclusively found in forest habitats), forest generalists (found in other habitats as well as forests), nonforest species (found in non-forest habitats only), and all woody plant species combined. We also analyze the relationship between forest cover and woody species richness using general additive model. We then analyze the relationships of these variables with forest cover, fragmentation and human occupation predictors using variance partitioning techniques to identify combined and pure effects of these factors on species richness. According to the arguments posed above, we expect larger, independent effects of habitat fragmentation on the richness of forest generalists and, particularly, of forest specialists.
2. Methods 2.1. Plant data Distribution maps for the 144 native angiosperm woody species inhabiting mainland Great Britain were obtained from the New Atlas of the British and Irish Flora (Preston et al., 2002). These are presence/absence maps consisting in UTM grid cells of 10 km × 10 km each. Maps were digitized and processed in Arc GIS 9.1 to generate cell richness values for all woody species as well as for the three forest habitat specialization groups considered in this study: forest specialists (27 species), forest generalists (40 species), and non-forest plants (77 species) (Table 1). These species groups were based on the results of the Technical Report of Flora Distri-
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Table 1 Species list classified according to their habitat specialization: forest specialist (27 species), forest generalist (40 species) and non-forest (77 species). Genus
Species
Betula Betula Carpinus Crataegus Daphne Fagus Frangula Fraxinus Ilex Lonicera Pinus Prunus Prunus Quercus Ribes Rubus Salix Salix Sorbus Sorbus Sorbus Sorbus Taxus Tilia Tilia Ulmus Viburnum
pendula pubescens betulus laevigata mezereum sylvatica alnus excelsior aquifolium periclymenum sylvestris padus avium petraea rubrum idaeus caprea pentandra aria eminens subcuneata torminalis baccata cordata platyphyllos glabra opulus
Subspecies
Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists
Acer Alnus Arbutus Berberis Cornus Corylus Crataegus Euonymus Hedera Hedera Ligustrum Malus Populus Quercus Rhamnus Ribes Ribes Rosa Rosa Rosa Rosa Rosa Rosa Rosa Rubus Ruscus Salix Salix Salix Salix Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Tamus Ulmus Vaccinium Viburnum
campestre glutinosa unedo vulgaris sanguinea avellana monogyna europaeus helix helix vulgare sylvestris sens. lat. tremula robur cathartica alpinum spicatum caesia canina micrantha mollis obtusifolia stylosa tomentosa fruticosus agg. aculeatus aurita cinerea cinerea cinerea wilmottiana devoniensis hibernica aucuparia bristoliensis vexans communis minor vitis-idaea lantana
Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists
Andromeda Arctium Arctostaphylos Arctostaphylos Atriplex
polifolia minus sens. lat. alpinus uva-ursi portulacoides
subsp. helix
subsp. caesia
subsp. cinerea subsp. oleifolia
Habitat specialization
Non-forest Non-forest Non-forest Non-forest Non-forest
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Table 1 (Continued ) Genus
Species
Betula Calluna Cytisus Daboecia Dryas Empetrum Empetrum Erica Erica Erica Erica Erica Erica Genista Genista Genista Genista Helianthemum Helianthemum Helianthemum Hippophae Hypericum Juniperus Juniperus Juniperus Lavatera Loiseleuria Myrica Ononis Pedicularis Phyllodoce Populus Potentilla Prunus Pyrus Rosa Rosa Rosa Rosa Rubus Salix Salix Salix Salix Salix Salix Salix Salix Salix Salix Sambucus Sarcocornia Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Suaeda Ulex Ulex Ulex Ulmus Ulmus Vaccinium Vaccinium Vaccinium Vaccinium Viscum
nana vulgaris scoparius cantabrica octopetala nigrum nigrum vagans ciliaris cinerea erigena mackaiana tetralix anglica pilosa tinctoria tinctoria apenninum nummularium oelandicum rhamnoides androsaemum communis communis communis arborea procumbens gale repens sylvatica caerulea nigra fruticosa spinosa cordata agrestis arvensis pimpinellifolia rubiginosa caesius arbuscula herbacea lanata lapponum myrsinifolia myrsinites phylicifolia purpurea repens reticulata nigra perennis arranensis domestica lancastriensis leptophylla leyana minima porrigentiformis pseudofennica rupicola vera europaeus gallii minor plotii procera microcarpum myrtillus oxycoccos uliginosum album
Subspecies
subsp. maritimus
subsp. hermaphroditum
subsp. littoralis
subsp. communis subsp. hemisphaerica subsp. nana
subsp. hibernica subsp. betulifolia
Habitat specialization Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest
bution in Britain (Preston et al., 2003), which considers two forest habitat types (i.e. broadleaved forests and coniferous woodlands) and 20 additional non-forest habitats and the New Atlas of the British and Irish Flora (Preston et al., 2002). We classified as forest specialists and generalists those species for which the results of Preston et al. (2003) exclusively or partially were associated with both forest types, while non-forest species were those that in neither case were associated with forests. As for our analysis grid, this comprised all cells included in the original maps, except those covering less than 50% of land mass. This rendered a total of 2252 10 km × 10 km cells for analysis. 2.2. Forest loss and fragmentation variables We used the 100-m pixel resolution version of the CORINE Land Cover 2000 database (CLC2000) to investigate the effects of habitat loss and fragmentation on woody species richness. This classification has been elaborated jointly by the Research Centre of the European Commission and the European Environment Agency (EEA), and is available at http://natlan.eea.europa.eu/dataservice/metadetails.asp?id=822. CLC2000 provides consistent information on land cover across Europe and is divided into 44 classes. We focused on two of these classes describing the distribution of broadleaved and mixed forests, and identified as forest habitat each pixel pertaining to either of them. After, we generated one forest layer namely broadleaved (broadleaved plus mixed forest classes). Next, we superimposed the 10 km × 10 km grid to the obtained forest layer and we then calculated the variable proportion of forest remaining (PFR), a surrogate of forest loss, as the percent area of each grid cell covered by forest pixels (Fig. 1a). Additionally, we generated five fragmentation variables reflecting the spatial configuration of forests in the cells, namely the number of forest patches, the variety (coefficient of variation) of patch sizes, edge density, mean shape index and Area-Weighted Mean Shape Index (see Bascompte and Rodríguez, 2001; Rempel and Carr, 2003). 2.3. Human activity We used the human footprint as a surrogate of human activity. This variable consisted of cell averages of the biomenormalized footprint values generated by Sanderson et al. (2002) at 1-km resolution by combining global records of population density, land use, transport access (roads, rivers, etc), and electrical power infrastructure (data available at: http://www.ciesin.columbia.edu/wild areas/). Then, we superimposed the 10 km × 10 km grid to the human footprint map and we calculated the human activity in each grid cell (Fig. 1b). 2.4. Statistical analyses Species richness values for forest specialists, generalists, nonforest species and for all species combined were analyzed separately. We used GAM (general additive models) to evaluate the relationship between PFR and woody species richness. GAM is appropriate for evaluating curvilinear relationships and also should help to identify the neighbourhood within which threshold is located (Benedetti et al., 2009). The complexity and non-linearity of the response curve may be measured by the effective degree of freedom (edf), so that higher edf values indicate more non-linearity (Wood, 2006; Ficetola and Denoël, 2009). In addition, threshold position can be visually estimated in GAM when edf ≥ 2 (Potvin et al., 2005; Ficetola and Denoël, 2009). Forest cover, fragmentation, and human effects on woody species richness variables were investigated through OLS (Ordinary Least Square) multiple regressions combined with variation parti-
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ficients to establish their relative importance for richness variation. Complementarily, we used the variance partitioning technique to investigate the pure and shared effects of forest cover (PFR), fragmentation, and human activity on species richness. All statistical analyses were performed in R (R Development Core Team 2009) including its package ‘vegan’ (Oksanen et al., 2009), and Spatial Analysis in Macroecology (SAM, Rangel et al., 2006). 3. Results 3.1. Spatial patterns of angiosperm woody species richness variables Richness variation of all four groups of angiosperm woody plants across Great Britain is depicted in Fig. 2. Forest specialists, generalists and all species combined showed clear north-to-south gradients of increasing richness, with the lowest values clustering in Scotland (mainly in the Highlands), and the highest values in south-eastern areas, as well as around the eastern side of the border between England and Wales (Fig. 2). Conversely, species richness of non-forest species was more evenly distributed across the island, with some spots of high richness occurring particularly between the Scottish Highlands and Lowlands, as well as in Northern England (in and around the Lake District National Park), and low richness spots in the vicinity of The Wash estuary (northern margin of East Anglia). 3.2. PFR and species richness relationship Fig. 1. Forest (a) and human footprint (b) maps superimposed by the 10 km × 10 km grid cell in Great Britain. Human footprint was used as surrogate of human activity in each grid cell. Black values represent low human activity whereas white values represent high human activity.
tioning techniques (see below). However, given the high number of fragmentation variables (i.e. 5) and the potential multicollinearity problems that this might cause on multiple regression models, we first established the main trends of variation of fragmentation across Great Britain through a Varimax-rotated principal component analysis (VrPCA), a procedure that permits a clear identification of the major trends in the data, as well as to pinpoint the variables that best represent them (i.e. those showing higher loadings in the main rotated factors) (e.g. see Cattell, 1978). According to the ‘broken stick’ stopping criterion (Jackson, 1993), these major trends were captured by the first factor of the VrPCA, which jointly described 69.7% of the variance in fragmentation (Table 2). All forest fragmentation variables had correlations higher than |0.70| in this factor. In sum, for each species richness variable, we generated an OLS multiple regression model including PFR, the first PCA factor representing fragmentation effects, and human footprint as predictors, and used their respective standardized regression coefTable 2 Factor loadings (expressed as simple correlations) of the fragmentation predictors in the first axis of principal component analyses (PCA) performed on these predictors in Great Britain. Fragmentation variables
PCA fragmentation factor
Number of forest patches Coefficient of variation Edge density MSIa AWMSIb
−0.80 −0.79 −0.79 −0.82 −0.89
Cumulative variance (%)
69.7
a b
Mean shape index. Area-weighted mean shape index.
Forest specialist, forest generalist, non-forest and total species richness are plotted in Fig. 3 as a function of the percentage of forest remaining. As shown in the figure, relationships are strongly non-linear. In all cases, the effective degree of freedom (edf) was higher than 2. However, edf value was higher for forest specialist and generalist, than that for non-forest species. Visual inspection of scatterplots of species richness variables against the proportion of forest remaining (PFR) suggested the existence of PFR-thresholds in all cases, so that each richness variable tended to remain insensitive to PFR variation when the amount of forest was moderate to high, but declined sharply along with forest cover reduction when PFR values were around 20%. However, it should be noted that the high standard errors for the non-forest species figure (Fig. 3c), probably indicate a lack of relationship between PFR and non-forest species richness. 3.3. Effects of forest cover, fragmentation and human activity on richness OLS multiple regression models using forest cover, fragmentation (represented through the first PCA factor) and human activity as explanatory variables explained different portions of variation of species richness depending on the group considered (Table 3). In all cases (except non-forest species) fragmentation was the strongest Table 3 Standardized regression coefficients and the coefficient of determination of ordinary least-squares (OLS) regression for forest specialist species, forest generalist species, non-forest species and total species richness in Great Britain. Richness variable
Specialists Generalists Non-forest species All species
PCA Fragmentation
PFR
Human
R2
−0.44 −0.34 −0.11 −0.38
0.09 0.14 0.16 0.17
0.20 0.24 0.03 0.20
0.35 0.31 0.07 0.33
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Fig. 2. Geographical patterns of woody species richness in Great Britain. Data are represented for (a) forest specialist species, (b) forest generalist species, (c) non-forest species and (d) total woody species richness.
predictor for woody richness. Models also showed that the relative importance of human activity was higher than PFR for all groups, except non-forest species. The model explained a larger fraction of richness variation for specialists (35%) and generalists (31%) than for non-forest species (7%) (Table 3). Taking into account the pure effect of forest loss, fragmentation and human activity, the partitioning of the variance showed that fragmentation per se accounted for the largest fraction of variability for total woody richness. Likewise, fragmentation per se explained more variance of forest specialist species richness (13%), whereas PFR and human variables per se explained 0% and 4% of the total variance respectively (Fig. 4). Similarly, the largest fraction of variability in forest generalist species richness was accounted for by the fragmentation per se (7%), whereas again human variables per se explained more variance (6%) than PFR (1%). A strong joint effect of fragmentation and forest loss was also observed, explaining 12%,
11% and 14% of the total variance for forest specialist, generalist and total species respectively. Non-forest species richness showed very different patterns. The influence of forest cover, fragmentation and human activity on non-forest species richness was almost negligible. 4. Discussion 4.1. The effect of forest loss on woody species richness Our results confirm previous studies documenting that the decline in species richness as habitat loss increases is strongly non-linear (Bascompte and Rodríguez, 2001). It has been largely documented that habitat loss causes negative effects on biodiversity (Debinski and Holt, 2000; Boutin and Hebert, 2002; Fahrig, 2003). Smaller areas have smaller effective population size, higher
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Fig. 3. Scatterplots of the four woody plant species richness groups (S) and the variable proportion of forest remaining (PFR). The PFR axis is presented reversed and consequently the percentage of forest cover decreases towards the right. Also, the PFR was divided into 75 equal ranges and the average species richness (squares) and standard errors (lines) for each category were calculated. Data are represented for (a) forest specialist species, (b) forest generalist species, (c) non-forest species and (d) total woody species richness.
Fig. 4. Diagrams showing the variation in woody species richness explained by each descriptive variable: environment, percentage of forest remaining (PFR), fragmentation, and the total variance explained (overall R2 ). Data are represented for (a) forest specialist species, (b) forest generalist species, (c) non-forest species and (d) total woody species richness.
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rates of genetic drift and consequently maintain fewer species (Debinski and Holt, 2000). However, a more important aspect about the relationship between richness and habitat loss is finding out whether a threshold in patch size exists (Boutin and Hebert, 2002). For instance, in the case of forest habitats, the identification of this threshold has been proposed as a key research priority, as this knowledge would facilitate the improvement of forest management practices (Boutin and Hebert, 2002). Theoretical studies suggest that this threshold is significant when levels of habitat amount drop below 20–30% (Boutin and Hebert, 2002; Fahrig, 2003). In our study, we observed that below ca. 20% of forest remaining, woody species richness decreased significantly, which agrees with previous studies (With and Crist, 1995; Fahrig, 1998; Bascompte and Rodríguez, 2001; Boutin and Hebert, 2002; Flather and Bevers, 2002). This does not mean that 20% of forest remaining should be taken as a minimum amount of habitat required to maintain woody species, but this is the value that should be taken into account to avoid critical biodiversity losses. 4.2. Assessing the influence of PFR and fragmentation on woody species richness It is difficult to compare the influence of fragmentation and habitat loss because these variables are intercorrelated (Fahrig, 2003). In general, most empirical studies to date suggest that habitat loss has stronger and consistent effects on biodiversity than fragmentation per se (Meyer et al., 1998; Villard et al., 1999; Langlois et al., 2001; Caley et al., 2001; With et al., 2002). However, we found that the effects of fragmentation per se on the richness of all groups were greater than the effects of PFR per se. These results agree with earlier studies, which already showed that fragmentation per se explains more than habitat loss (Collins and Barret, 1997; Wolff et al., 1997) and adds a complementary perspective to the evidence pointing towards the effect of fragmentation and habitat loss on biodiversity. Besides, our study shows that habitat fragmentation per se affects woody species groups differentially. Even if the overall effect of fragmentation was similar for all woody plant richness groups, except for non-forest species, the spatial distribution of specialist species was better explained by fragmentation than did the less specialized woody species. This suggests that patchy habitats with more fragments and a higher variety of patch sizes support less specialist species than the same area composed only by one or two fragments, and has implications for potential SLOSS-like debates – whether a single large reserve will conserve more species than several small reserves – on the preservation of forest woody plant diversity in our study region (reviewed by Ovaskainen, 2002). The higher negative effect of fragmentation per se over habitat loss may be due to the strong subdivision of the habitat into a large number of smaller or medium sized fragments distributed in a larger area and relatively apart form each other. Fahrig (2002, 2003) sustain that since patches of forest habitat will be too small to sustain a local population, species may be confined to a large number of smaller patches, and as a consequence reduce the population size and the probability of persistence. Under a conservation perspective, to know the independent effect of habitat loss and fragmentation on biodiversity may be relevant for habitat preservation, given that the success of conservation strategies may differ depending on the most relevant local factor affecting woody richness. If habitat loss per se is the strongest correlate with richness, conservation efforts should focus on habitat preservation as a main driver in forest planning (Boutin and Hebert, 2002). However, if fragmentation per se is the strongest variable, conservation efforts should focus on actions that attempt to minimize habitat division (Fahrig, 2003). However, although in this paper we provide evidence that the effect of habitat loss per se is weak compared to fragmentation per se, this does not mean
that habitat loss is not important for describing the spatial pattern of woody species in Great Britain. Habitat fragmentation is a process that implies changes in habitat configuration, but also involves habitat loss (Fahrig, 2003). This is well illustrated by our results, which indicated a strong combined effect of both factors that explains an important amount of the variance in woody species richness. As habitat fragmentation is a process involving both habitat loss and fragmentation per se, there is a need to maintain habitat amount and at the same time take habitat configuration (patch size) into account. This leads us to conclude that, at broad scales, the interplay between habitat loss and fragmentation is the key component to explain the effect of fragmentation on biodiversity. The positive effect of human activity on richness is not a surprise. Probably, human density per se is not a primary determinant of species richness, but in human dominated landscapes (such as those of Great Britain), species richness may depend much more on other mechanisms (i.e. energy availability) that the actual number of people (Gaston, 2006). However, such as is pointed in Gaston (2006), this relationship may exist because early human populations established more readily in warmer and productive areas (i.e. areas with higher energy availability), and perhaps grew more rapidly there. Additionally, we should take into account that plants may respond differently to disturbance (Montoya et al., 2008; Farwig et al., 2008) and thus, if there are more species strongly sensitive to human disturbance, we should expect a more strong effect of human variables. In sum, our results emphasize the larger independent effect of fragmentation over habitat loss. Although our study was not designed to explore the cause of these differences, we can speculate that species variation is mainly conditioned by the spatial configuration of the habitat. Particularly this is a fundamental result to manage biodiversity, because action should be mainly focused to maintain the spatial structure of forest patches. Analyses that do not include the independent effects of forest fragmentation and habitat loss are missing a key component for explaining the broad-scale patterns of woody richness. Acknowledgements MR was supported by a postdoctoral grant from the University of Alcalá. We would like to acknowledge Miguel Á. Rodríguez and Luis Cayuela for their helpful comments to this manuscript. References Balmford, A., Bond, W., 2005. Trends in the state of nature and their implications for human well-being. Ecology Letters 8, 1218–1234. Bascompte, J., Rodríguez, M.Á., 2001. Habitat patchiness and plant species richness. Ecology Letters 4, 1–4. Benedetti, A., Abrahamowicz, M., Leffondré, K., Goldberg, M.S., Tamblyn, R., 2009. Using generalized additive models to detect and estimate threshold associations. The International Journal of Biostatistics 5, 1–24. Boutin, S., Hebert, D., 2002. Landscape ecology and forest management: developing an effective partnership. Ecological Applications 12, 390–397. Caley, M.J., Buckley, K.A., Jones, G.P., 2001. Separating ecological effects of habitat fragmentation, degradation, and loss on coral commensals. Ecology 82, 3435–3448. Cayuela, L., Benayas, J.M.R., Echeverría, C., 2006. Clearance and fragmentation of tropical montane forests in the Highlands of Chiapas, Mexico (1975–2000). Forest Ecology and Management 226, 208–218. Cayuela, L., 2009. Fragmentation. In: Gillespie, R., Clague, D.A. (Eds.), Encyclopedia of Islands. University of California Press, Berkeley, CA. Cattell, R.B., 1978. The Scientific Use of Factor Analysis in Behavioural and Life Sciences. Plenum Press, New York, 618 pp. Collinge, S.K., 1995. Spatial arrangement of patches and corridors in the landscape: consequences for biological diversity and implications for landscape architecture. Ph.D. dissertation. Harvard University, Cambridge, MA. Collins, S.K., Barret, G.W., 1997. Effects of habitat fragmentation on meadow vole (Microtus pennsylvanicus) population dynamics in experimental landscape patches. Journal of Animal Ecology 67, 460–471. Debinski, D.M., Holt, R.D., 2000. A survey and overview of habitat fragmentation experiments. Conservation Biology 14, 342–355.
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Forest loss and fragmentation effects on woody plant species richness in Great Britain Fábio Suzart de Albuquerque ∗ , Marta Rueda Department of Ecology, Faculty of Biology, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain
a r t i c l e
i n f o
Article history: Received 11 February 2010 Received in revised form 26 April 2010 Accepted 2 May 2010 Keywords: Habitat loss Habitat fragmentation Biodiversity Species richness
a b s t r a c t Studies on the effect of forest loss and fragmentation on plant species richness at different spatial scales have yielded contradictory results that have been attributed to different ways to measure fragmentation. The main goal of this study was to investigate the independent and combined effects of forest loss and habitat fragmentation on woody species richness. Woody species were grouped according to their habitat requirements (forest specialists, forest generalists, non-forest and total woody species). We used regression models to investigate the effect of fragmentation and human occupation on woody species richness. The underlying factors were investigated by partitioning of the variation, i.e. decomposing the variation in species richness between the pure effects of forest loss, fragmentation and human occupation. The relationship between species richness and forest loss resulted extremely non-linear. The models for forest specialist, generalist and total woody species richness accounted for 35%, 31% and 33% of the total variance respectively. Model for non-forest species richness only accounted for 7% of the total variance. The largest fraction of variability in species richness was accounted by fragmentation variables for all groups (except for non-forest species). Results emphasize the larger independent effect of fragmentation over forest loss, suggesting that species variation is mainly conditioned by the spatial configuration of the habitat. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Habitat destruction is generally regarded as the single major cause of global biodiversity loss, a problem that has led ecologists to face the question of how much habitat is enough for a species to persist (Pimm and Raven, 2000; Balmford and Bond, 2005; Foley et al., 2005). An important, common consequence of habitat loss is fragmentation, a process that occurs when areas of continuous habitat are broken into smaller and discontinuous habitat patches (e.g. Fahrig, 2003; Cayuela, 2009). To what extent fragmentation affects the impact of habitat loss on diversity has been widely debated, probably because empirical studies appear to have failed when attempting to differentiate the consequences derived from each process (but see Bascompte and Rodríguez, 2001; also see Fahrig, 2003 for an in-deep review of this issue). In this regard, some authors notice that while loss of habitat area generally compromises the persistence of populations, both positive and negative effects of fragmentation are observed in empirical studies. This has fuelled the view that conservation efforts should be directed to maintain or increase the amount of habitat without paying much attention to the extent of its fragmentation (Fahrig, 2003).
∗ Corresponding author. Tel.: +34 605380608. E-mail address: [email protected] (F.S. de Albuquerque). 0378-1127/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2010.05.003
In contrast, other authors emphasize that inabilities to differentiate empirically effects of habitat loss that occur through fragmentation have precluded to show the true importance of the latter process, and predict that, once these technical shortcomings had been solved, fragmentation may appear to be a key driver of habitat loss-derived biodiversity declines, at least under some circumstances (Boutin and Hebert, 2002). Specifically, these authors predict that fragmentation will become of chief importance for species persistence once habitat loss has exceeded a certain threshold level which, for the case of forest habitats, is expected to be located between 50% and 20% of forest cover. In this regard, partial regression techniques allow partitioning the variation of a response variable into portions explained independently and in concert by a set of explanatory variables (Legendre and Legendre, 1998; Lobo et al., 2002), and we use them here to check this prediction as a mean to understand the extent at which forest amount and fragmentation determine Great Britain’s woody plant species richness. Among the conceptual frameworks that have driven habitat loss and fragmentation research, the theory of island biogeography (MacArthur and Wilson, 2001) has played a prominent role, generating many insights that constitute most of the basic knowledge of this field. According to this theory, species richness in habitat patches would be a function of patch size and the degree of isolation, so that smaller and more isolated patches will support fewer species (e.g. Collinge, 1995). However, this argument is likely to be
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more valid when we focus on habitat specialists; that is, on species that are closely linked to the shrinking habitat (Tilman et al., 1994). Whereas if we consider all the species found in a particular habitat, species richness may be unaffected, or even enhanced, by habitat loss and fragmentation. This is because smaller and more isolated habitat fragments may encourage shifts of competitive regimes (i.e. suboptimal habitat conditions for specialist species would cause them to be less effective when competing with less specialized species; see Tilman et al., 1994), and thus favour the establishment of early-successional and transient species, thus leading to an increase in richness in the landscape (Holt, 1997; Debinski and Holt, 2000; Langlois et al., 2001). An important issue regarding biodiversity loss is human activity. However, in this regard, the literature shows contrasting hypotheses. On the one hand, previous studies show that in a given area, human activity usually causes the extinction of native species (McKinney, 2002), or can reduce species richness in tree communities (Farwig et al., 2008). On the other hand, human activity may be positively related to plant richness (McKinney, 2002). The reasons for this are twofold. Firstly, people usually live in areas of high biodiversity (Cayuela et al., 2006). Secondly, human activity may cause habitat diversification (Kühn et al., 2004; Luck et al., 2004; Di Giulio et al., 2009). In the first case there is a spurious relationship mediated by climate (i.e. there are more people living in mild or warm areas than in cold regions), whereas in the second case there is a causal relationship. Additionally, the variables characterizing forest and human variables are intercorrelated, since forest loss and fragmentation are usually affected by human activities (i.e. deforestation by urbanization) (Cayuela et al., 2006). Yet, it is important to assess to what extent forest loss, forest fragmentation and human-related factors affect the geographical distribution of woody species. Here we use a novel approach to investigate the independent and combined effects of forest amount and fragmentation on native angiosperm woody plant species richness in Great Britain, one of the most highly deforested areas of Europe (see European Environment Agency, 2006). Initially, we describe the geographical distribution of the richness of four woody plant species groups, including forest specialists (exclusively found in forest habitats), forest generalists (found in other habitats as well as forests), nonforest species (found in non-forest habitats only), and all woody plant species combined. We also analyze the relationship between forest cover and woody species richness using general additive model. We then analyze the relationships of these variables with forest cover, fragmentation and human occupation predictors using variance partitioning techniques to identify combined and pure effects of these factors on species richness. According to the arguments posed above, we expect larger, independent effects of habitat fragmentation on the richness of forest generalists and, particularly, of forest specialists.
2. Methods 2.1. Plant data Distribution maps for the 144 native angiosperm woody species inhabiting mainland Great Britain were obtained from the New Atlas of the British and Irish Flora (Preston et al., 2002). These are presence/absence maps consisting in UTM grid cells of 10 km × 10 km each. Maps were digitized and processed in Arc GIS 9.1 to generate cell richness values for all woody species as well as for the three forest habitat specialization groups considered in this study: forest specialists (27 species), forest generalists (40 species), and non-forest plants (77 species) (Table 1). These species groups were based on the results of the Technical Report of Flora Distri-
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Table 1 Species list classified according to their habitat specialization: forest specialist (27 species), forest generalist (40 species) and non-forest (77 species). Genus
Species
Betula Betula Carpinus Crataegus Daphne Fagus Frangula Fraxinus Ilex Lonicera Pinus Prunus Prunus Quercus Ribes Rubus Salix Salix Sorbus Sorbus Sorbus Sorbus Taxus Tilia Tilia Ulmus Viburnum
pendula pubescens betulus laevigata mezereum sylvatica alnus excelsior aquifolium periclymenum sylvestris padus avium petraea rubrum idaeus caprea pentandra aria eminens subcuneata torminalis baccata cordata platyphyllos glabra opulus
Subspecies
Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists Forest specialists
Acer Alnus Arbutus Berberis Cornus Corylus Crataegus Euonymus Hedera Hedera Ligustrum Malus Populus Quercus Rhamnus Ribes Ribes Rosa Rosa Rosa Rosa Rosa Rosa Rosa Rubus Ruscus Salix Salix Salix Salix Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Tamus Ulmus Vaccinium Viburnum
campestre glutinosa unedo vulgaris sanguinea avellana monogyna europaeus helix helix vulgare sylvestris sens. lat. tremula robur cathartica alpinum spicatum caesia canina micrantha mollis obtusifolia stylosa tomentosa fruticosus agg. aculeatus aurita cinerea cinerea cinerea wilmottiana devoniensis hibernica aucuparia bristoliensis vexans communis minor vitis-idaea lantana
Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists Forest generalists
Andromeda Arctium Arctostaphylos Arctostaphylos Atriplex
polifolia minus sens. lat. alpinus uva-ursi portulacoides
subsp. helix
subsp. caesia
subsp. cinerea subsp. oleifolia
Habitat specialization
Non-forest Non-forest Non-forest Non-forest Non-forest
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Table 1 (Continued ) Genus
Species
Betula Calluna Cytisus Daboecia Dryas Empetrum Empetrum Erica Erica Erica Erica Erica Erica Genista Genista Genista Genista Helianthemum Helianthemum Helianthemum Hippophae Hypericum Juniperus Juniperus Juniperus Lavatera Loiseleuria Myrica Ononis Pedicularis Phyllodoce Populus Potentilla Prunus Pyrus Rosa Rosa Rosa Rosa Rubus Salix Salix Salix Salix Salix Salix Salix Salix Salix Salix Sambucus Sarcocornia Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Sorbus Suaeda Ulex Ulex Ulex Ulmus Ulmus Vaccinium Vaccinium Vaccinium Vaccinium Viscum
nana vulgaris scoparius cantabrica octopetala nigrum nigrum vagans ciliaris cinerea erigena mackaiana tetralix anglica pilosa tinctoria tinctoria apenninum nummularium oelandicum rhamnoides androsaemum communis communis communis arborea procumbens gale repens sylvatica caerulea nigra fruticosa spinosa cordata agrestis arvensis pimpinellifolia rubiginosa caesius arbuscula herbacea lanata lapponum myrsinifolia myrsinites phylicifolia purpurea repens reticulata nigra perennis arranensis domestica lancastriensis leptophylla leyana minima porrigentiformis pseudofennica rupicola vera europaeus gallii minor plotii procera microcarpum myrtillus oxycoccos uliginosum album
Subspecies
subsp. maritimus
subsp. hermaphroditum
subsp. littoralis
subsp. communis subsp. hemisphaerica subsp. nana
subsp. hibernica subsp. betulifolia
Habitat specialization Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest Non-forest
bution in Britain (Preston et al., 2003), which considers two forest habitat types (i.e. broadleaved forests and coniferous woodlands) and 20 additional non-forest habitats and the New Atlas of the British and Irish Flora (Preston et al., 2002). We classified as forest specialists and generalists those species for which the results of Preston et al. (2003) exclusively or partially were associated with both forest types, while non-forest species were those that in neither case were associated with forests. As for our analysis grid, this comprised all cells included in the original maps, except those covering less than 50% of land mass. This rendered a total of 2252 10 km × 10 km cells for analysis. 2.2. Forest loss and fragmentation variables We used the 100-m pixel resolution version of the CORINE Land Cover 2000 database (CLC2000) to investigate the effects of habitat loss and fragmentation on woody species richness. This classification has been elaborated jointly by the Research Centre of the European Commission and the European Environment Agency (EEA), and is available at http://natlan.eea.europa.eu/dataservice/metadetails.asp?id=822. CLC2000 provides consistent information on land cover across Europe and is divided into 44 classes. We focused on two of these classes describing the distribution of broadleaved and mixed forests, and identified as forest habitat each pixel pertaining to either of them. After, we generated one forest layer namely broadleaved (broadleaved plus mixed forest classes). Next, we superimposed the 10 km × 10 km grid to the obtained forest layer and we then calculated the variable proportion of forest remaining (PFR), a surrogate of forest loss, as the percent area of each grid cell covered by forest pixels (Fig. 1a). Additionally, we generated five fragmentation variables reflecting the spatial configuration of forests in the cells, namely the number of forest patches, the variety (coefficient of variation) of patch sizes, edge density, mean shape index and Area-Weighted Mean Shape Index (see Bascompte and Rodríguez, 2001; Rempel and Carr, 2003). 2.3. Human activity We used the human footprint as a surrogate of human activity. This variable consisted of cell averages of the biomenormalized footprint values generated by Sanderson et al. (2002) at 1-km resolution by combining global records of population density, land use, transport access (roads, rivers, etc), and electrical power infrastructure (data available at: http://www.ciesin.columbia.edu/wild areas/). Then, we superimposed the 10 km × 10 km grid to the human footprint map and we calculated the human activity in each grid cell (Fig. 1b). 2.4. Statistical analyses Species richness values for forest specialists, generalists, nonforest species and for all species combined were analyzed separately. We used GAM (general additive models) to evaluate the relationship between PFR and woody species richness. GAM is appropriate for evaluating curvilinear relationships and also should help to identify the neighbourhood within which threshold is located (Benedetti et al., 2009). The complexity and non-linearity of the response curve may be measured by the effective degree of freedom (edf), so that higher edf values indicate more non-linearity (Wood, 2006; Ficetola and Denoël, 2009). In addition, threshold position can be visually estimated in GAM when edf ≥ 2 (Potvin et al., 2005; Ficetola and Denoël, 2009). Forest cover, fragmentation, and human effects on woody species richness variables were investigated through OLS (Ordinary Least Square) multiple regressions combined with variation parti-
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ficients to establish their relative importance for richness variation. Complementarily, we used the variance partitioning technique to investigate the pure and shared effects of forest cover (PFR), fragmentation, and human activity on species richness. All statistical analyses were performed in R (R Development Core Team 2009) including its package ‘vegan’ (Oksanen et al., 2009), and Spatial Analysis in Macroecology (SAM, Rangel et al., 2006). 3. Results 3.1. Spatial patterns of angiosperm woody species richness variables Richness variation of all four groups of angiosperm woody plants across Great Britain is depicted in Fig. 2. Forest specialists, generalists and all species combined showed clear north-to-south gradients of increasing richness, with the lowest values clustering in Scotland (mainly in the Highlands), and the highest values in south-eastern areas, as well as around the eastern side of the border between England and Wales (Fig. 2). Conversely, species richness of non-forest species was more evenly distributed across the island, with some spots of high richness occurring particularly between the Scottish Highlands and Lowlands, as well as in Northern England (in and around the Lake District National Park), and low richness spots in the vicinity of The Wash estuary (northern margin of East Anglia). 3.2. PFR and species richness relationship Fig. 1. Forest (a) and human footprint (b) maps superimposed by the 10 km × 10 km grid cell in Great Britain. Human footprint was used as surrogate of human activity in each grid cell. Black values represent low human activity whereas white values represent high human activity.
tioning techniques (see below). However, given the high number of fragmentation variables (i.e. 5) and the potential multicollinearity problems that this might cause on multiple regression models, we first established the main trends of variation of fragmentation across Great Britain through a Varimax-rotated principal component analysis (VrPCA), a procedure that permits a clear identification of the major trends in the data, as well as to pinpoint the variables that best represent them (i.e. those showing higher loadings in the main rotated factors) (e.g. see Cattell, 1978). According to the ‘broken stick’ stopping criterion (Jackson, 1993), these major trends were captured by the first factor of the VrPCA, which jointly described 69.7% of the variance in fragmentation (Table 2). All forest fragmentation variables had correlations higher than |0.70| in this factor. In sum, for each species richness variable, we generated an OLS multiple regression model including PFR, the first PCA factor representing fragmentation effects, and human footprint as predictors, and used their respective standardized regression coefTable 2 Factor loadings (expressed as simple correlations) of the fragmentation predictors in the first axis of principal component analyses (PCA) performed on these predictors in Great Britain. Fragmentation variables
PCA fragmentation factor
Number of forest patches Coefficient of variation Edge density MSIa AWMSIb
−0.80 −0.79 −0.79 −0.82 −0.89
Cumulative variance (%)
69.7
a b
Mean shape index. Area-weighted mean shape index.
Forest specialist, forest generalist, non-forest and total species richness are plotted in Fig. 3 as a function of the percentage of forest remaining. As shown in the figure, relationships are strongly non-linear. In all cases, the effective degree of freedom (edf) was higher than 2. However, edf value was higher for forest specialist and generalist, than that for non-forest species. Visual inspection of scatterplots of species richness variables against the proportion of forest remaining (PFR) suggested the existence of PFR-thresholds in all cases, so that each richness variable tended to remain insensitive to PFR variation when the amount of forest was moderate to high, but declined sharply along with forest cover reduction when PFR values were around 20%. However, it should be noted that the high standard errors for the non-forest species figure (Fig. 3c), probably indicate a lack of relationship between PFR and non-forest species richness. 3.3. Effects of forest cover, fragmentation and human activity on richness OLS multiple regression models using forest cover, fragmentation (represented through the first PCA factor) and human activity as explanatory variables explained different portions of variation of species richness depending on the group considered (Table 3). In all cases (except non-forest species) fragmentation was the strongest Table 3 Standardized regression coefficients and the coefficient of determination of ordinary least-squares (OLS) regression for forest specialist species, forest generalist species, non-forest species and total species richness in Great Britain. Richness variable
Specialists Generalists Non-forest species All species
PCA Fragmentation
PFR
Human
R2
−0.44 −0.34 −0.11 −0.38
0.09 0.14 0.16 0.17
0.20 0.24 0.03 0.20
0.35 0.31 0.07 0.33
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Fig. 2. Geographical patterns of woody species richness in Great Britain. Data are represented for (a) forest specialist species, (b) forest generalist species, (c) non-forest species and (d) total woody species richness.
predictor for woody richness. Models also showed that the relative importance of human activity was higher than PFR for all groups, except non-forest species. The model explained a larger fraction of richness variation for specialists (35%) and generalists (31%) than for non-forest species (7%) (Table 3). Taking into account the pure effect of forest loss, fragmentation and human activity, the partitioning of the variance showed that fragmentation per se accounted for the largest fraction of variability for total woody richness. Likewise, fragmentation per se explained more variance of forest specialist species richness (13%), whereas PFR and human variables per se explained 0% and 4% of the total variance respectively (Fig. 4). Similarly, the largest fraction of variability in forest generalist species richness was accounted for by the fragmentation per se (7%), whereas again human variables per se explained more variance (6%) than PFR (1%). A strong joint effect of fragmentation and forest loss was also observed, explaining 12%,
11% and 14% of the total variance for forest specialist, generalist and total species respectively. Non-forest species richness showed very different patterns. The influence of forest cover, fragmentation and human activity on non-forest species richness was almost negligible. 4. Discussion 4.1. The effect of forest loss on woody species richness Our results confirm previous studies documenting that the decline in species richness as habitat loss increases is strongly non-linear (Bascompte and Rodríguez, 2001). It has been largely documented that habitat loss causes negative effects on biodiversity (Debinski and Holt, 2000; Boutin and Hebert, 2002; Fahrig, 2003). Smaller areas have smaller effective population size, higher
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Fig. 3. Scatterplots of the four woody plant species richness groups (S) and the variable proportion of forest remaining (PFR). The PFR axis is presented reversed and consequently the percentage of forest cover decreases towards the right. Also, the PFR was divided into 75 equal ranges and the average species richness (squares) and standard errors (lines) for each category were calculated. Data are represented for (a) forest specialist species, (b) forest generalist species, (c) non-forest species and (d) total woody species richness.
Fig. 4. Diagrams showing the variation in woody species richness explained by each descriptive variable: environment, percentage of forest remaining (PFR), fragmentation, and the total variance explained (overall R2 ). Data are represented for (a) forest specialist species, (b) forest generalist species, (c) non-forest species and (d) total woody species richness.
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rates of genetic drift and consequently maintain fewer species (Debinski and Holt, 2000). However, a more important aspect about the relationship between richness and habitat loss is finding out whether a threshold in patch size exists (Boutin and Hebert, 2002). For instance, in the case of forest habitats, the identification of this threshold has been proposed as a key research priority, as this knowledge would facilitate the improvement of forest management practices (Boutin and Hebert, 2002). Theoretical studies suggest that this threshold is significant when levels of habitat amount drop below 20–30% (Boutin and Hebert, 2002; Fahrig, 2003). In our study, we observed that below ca. 20% of forest remaining, woody species richness decreased significantly, which agrees with previous studies (With and Crist, 1995; Fahrig, 1998; Bascompte and Rodríguez, 2001; Boutin and Hebert, 2002; Flather and Bevers, 2002). This does not mean that 20% of forest remaining should be taken as a minimum amount of habitat required to maintain woody species, but this is the value that should be taken into account to avoid critical biodiversity losses. 4.2. Assessing the influence of PFR and fragmentation on woody species richness It is difficult to compare the influence of fragmentation and habitat loss because these variables are intercorrelated (Fahrig, 2003). In general, most empirical studies to date suggest that habitat loss has stronger and consistent effects on biodiversity than fragmentation per se (Meyer et al., 1998; Villard et al., 1999; Langlois et al., 2001; Caley et al., 2001; With et al., 2002). However, we found that the effects of fragmentation per se on the richness of all groups were greater than the effects of PFR per se. These results agree with earlier studies, which already showed that fragmentation per se explains more than habitat loss (Collins and Barret, 1997; Wolff et al., 1997) and adds a complementary perspective to the evidence pointing towards the effect of fragmentation and habitat loss on biodiversity. Besides, our study shows that habitat fragmentation per se affects woody species groups differentially. Even if the overall effect of fragmentation was similar for all woody plant richness groups, except for non-forest species, the spatial distribution of specialist species was better explained by fragmentation than did the less specialized woody species. This suggests that patchy habitats with more fragments and a higher variety of patch sizes support less specialist species than the same area composed only by one or two fragments, and has implications for potential SLOSS-like debates – whether a single large reserve will conserve more species than several small reserves – on the preservation of forest woody plant diversity in our study region (reviewed by Ovaskainen, 2002). The higher negative effect of fragmentation per se over habitat loss may be due to the strong subdivision of the habitat into a large number of smaller or medium sized fragments distributed in a larger area and relatively apart form each other. Fahrig (2002, 2003) sustain that since patches of forest habitat will be too small to sustain a local population, species may be confined to a large number of smaller patches, and as a consequence reduce the population size and the probability of persistence. Under a conservation perspective, to know the independent effect of habitat loss and fragmentation on biodiversity may be relevant for habitat preservation, given that the success of conservation strategies may differ depending on the most relevant local factor affecting woody richness. If habitat loss per se is the strongest correlate with richness, conservation efforts should focus on habitat preservation as a main driver in forest planning (Boutin and Hebert, 2002). However, if fragmentation per se is the strongest variable, conservation efforts should focus on actions that attempt to minimize habitat division (Fahrig, 2003). However, although in this paper we provide evidence that the effect of habitat loss per se is weak compared to fragmentation per se, this does not mean
that habitat loss is not important for describing the spatial pattern of woody species in Great Britain. Habitat fragmentation is a process that implies changes in habitat configuration, but also involves habitat loss (Fahrig, 2003). This is well illustrated by our results, which indicated a strong combined effect of both factors that explains an important amount of the variance in woody species richness. As habitat fragmentation is a process involving both habitat loss and fragmentation per se, there is a need to maintain habitat amount and at the same time take habitat configuration (patch size) into account. This leads us to conclude that, at broad scales, the interplay between habitat loss and fragmentation is the key component to explain the effect of fragmentation on biodiversity. The positive effect of human activity on richness is not a surprise. Probably, human density per se is not a primary determinant of species richness, but in human dominated landscapes (such as those of Great Britain), species richness may depend much more on other mechanisms (i.e. energy availability) that the actual number of people (Gaston, 2006). However, such as is pointed in Gaston (2006), this relationship may exist because early human populations established more readily in warmer and productive areas (i.e. areas with higher energy availability), and perhaps grew more rapidly there. Additionally, we should take into account that plants may respond differently to disturbance (Montoya et al., 2008; Farwig et al., 2008) and thus, if there are more species strongly sensitive to human disturbance, we should expect a more strong effect of human variables. In sum, our results emphasize the larger independent effect of fragmentation over habitat loss. Although our study was not designed to explore the cause of these differences, we can speculate that species variation is mainly conditioned by the spatial configuration of the habitat. Particularly this is a fundamental result to manage biodiversity, because action should be mainly focused to maintain the spatial structure of forest patches. Analyses that do not include the independent effects of forest fragmentation and habitat loss are missing a key component for explaining the broad-scale patterns of woody richness. Acknowledgements MR was supported by a postdoctoral grant from the University of Alcalá. We would like to acknowledge Miguel Á. Rodríguez and Luis Cayuela for their helpful comments to this manuscript. References Balmford, A., Bond, W., 2005. Trends in the state of nature and their implications for human well-being. Ecology Letters 8, 1218–1234. Bascompte, J., Rodríguez, M.Á., 2001. Habitat patchiness and plant species richness. Ecology Letters 4, 1–4. Benedetti, A., Abrahamowicz, M., Leffondré, K., Goldberg, M.S., Tamblyn, R., 2009. Using generalized additive models to detect and estimate threshold associations. The International Journal of Biostatistics 5, 1–24. Boutin, S., Hebert, D., 2002. Landscape ecology and forest management: developing an effective partnership. Ecological Applications 12, 390–397. Caley, M.J., Buckley, K.A., Jones, G.P., 2001. Separating ecological effects of habitat fragmentation, degradation, and loss on coral commensals. Ecology 82, 3435–3448. Cayuela, L., Benayas, J.M.R., Echeverría, C., 2006. Clearance and fragmentation of tropical montane forests in the Highlands of Chiapas, Mexico (1975–2000). Forest Ecology and Management 226, 208–218. Cayuela, L., 2009. Fragmentation. In: Gillespie, R., Clague, D.A. (Eds.), Encyclopedia of Islands. University of California Press, Berkeley, CA. Cattell, R.B., 1978. The Scientific Use of Factor Analysis in Behavioural and Life Sciences. Plenum Press, New York, 618 pp. Collinge, S.K., 1995. Spatial arrangement of patches and corridors in the landscape: consequences for biological diversity and implications for landscape architecture. Ph.D. dissertation. Harvard University, Cambridge, MA. Collins, S.K., Barret, G.W., 1997. Effects of habitat fragmentation on meadow vole (Microtus pennsylvanicus) population dynamics in experimental landscape patches. Journal of Animal Ecology 67, 460–471. Debinski, D.M., Holt, R.D., 2000. A survey and overview of habitat fragmentation experiments. Conservation Biology 14, 342–355.
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