SCUOLA DOTTORALE IN BIOLOGIA
SEZIONE BIODIVERSITÀ E ANALISI DEGLI ECOSISTEMI
XXIII CICLO
Influence of landscape heterogeneity on vertebrate assemblages of fragmented woodland
Effetto dell’eterogeneità del paesaggio su comunità di vertebrati in frammenti boschivi
Livia Zapponi
A.A 2010/2011
with our Abrahamic concept of land. We abuse land because we regard it as a commodity belonging to us. When we see land as a community to which we belong, we may begin to use it with love and respect."
ABSTRACT ... 1
RIASSUNTO ... 4
1. INTRODUCTION... 7
1.1.Habitat fragmentation ... 7
1.2.Landscape ecology and matrix heterogeneity ... 8
1.3.Matrix and biodiversity ... 8
1.4.Scale of the study ... 10
1.5.Species selection ... 10
1.6.Aims and objectives ... 11
1.7.Structure of the work ... 13
2. RESULTS... 14
2.1.1. Patch and landscape structure... 14
2.2.Bird communities ... 16
2.2.1. Description ... 16
2.2.2. Communities and environmental variables ... 21
2.2.3. Community nestedness ... 26
2.2.4. Guilds structure ... 27
2.3.Hazel dormouse ... 31
2.1.European red squirrel ... 35
3. DISCUSSION ... 38
3.1.Bird communities ... 38
3.2.Bird guilds... 39
3.3.Arboreal mammals ... 40
3.4.Vertebrate assemblages and matrix heterogeneity ... 42
BIBLIOGRAPHY ... 47
Appendix 1: METHODS ... 58
Appendix 2: ENVIRONMENTAL VARIABLES ... 69
Appendix 3: BIRD SPECIES LIFE HISTORY TRAITS ... 71
Acknowledgments ... 73
List of figures and tables
Figure 1.1. Conceptual framework. ... 12Figure 2.1. Study area land cover types. ... 14
Figure 2.2. Bird species presence-abundance correlation. ... 16
Figure 2.3. Bird species abundance-patch area regression ... 21
Figure 2.4. Ward's dendrogram of the sample sites. ... 22
Figure 2.5. CA of PCA factors and sample sites. ... 23
Figure 2.6. CA of PCA factors and bird species ... 24
Figure 2.7. Nestedness Temperature Calculator results. ... 26
Figure 2.8. Amount of individuals belonging to the guilds. ... 28
Figure 2.9. Hair images ... 35
Figure A.1. Location of study area ... 58
Figure A.2. Study area land cover composition. ... 59
Figure A.3. Study area aerial photograph ... 60
Figure A.4. A nestbox and its positioning. ... 63
Figure A.5. A hair-tube. ... 64
Figure A.6. Scale patterns. ... 65
Table 2.1. PCA results. ... 15
Table 2.2. Recorded bird species included in the analyses. ... 17
Table 2.3. Recorded bird species not included in the analyses. ... 19
Table 2.4. Species diversity. ... 20
Table 2.8. Hazel dormouse regression models. ... 32
Table 2.9. Hazel dormouse PCA regression models. ... 33
Table 2.10. Hazel dormouse parameters ranking. ... 34
Table 2.11. Ranking of the variables. ... 34
Table 2.12. European red squirrel regression models. ... 36
Table 2.13. European red squirrel PCA regression models. ... 37
Table 2.14. European red squirrel parameters ranking ... 37
Table A.1. Number of point counts, hair-tubes and nestboxes. ... 61
Table A.2. List of life history traits. ... 62
Table A.3. Woodland metrics ... 66
Table A.4. Dead wood classification ... 67
Table A.5. Landscapes metrics. ... 67
Environmental fragmentation leads to the transformation of continuous habitats in several patches, separated from each other by a habitat different from the original that is generally defined matrix. To study its effect on the biodiversity of the residual patches, this process has initially being assimilated to the theories developed on insular fauna dynamics (e.g. MacArthur & Wilson). However, the reductionist approach of describing patches as islands, in a sea of non-habitat, does not allow the inclusion of the complexity of real landscapes.
The aim of the present study is therefore the investigation of the relationship existing between animal species distribution and landscape heterogeneity, to examine the influence of matrix composition and configuration, and patch structure on the vertebrate assemblages of residual woods.
The field part of the study took place in the Marche Region, in an area of 100,000 ha approximately, which encompasses the Chienti and Potenza River catchments. This area was selected since it offered the opportunity of comparing woodland fragments included in landscapes that have suffered a diverse human impact. In the area, 24 sample sites were selected, characterised by the presence of Turkey oak (Quercus cerris) and downy oak (Q. pubescens). In each site, the bird communities composition was described using 66 point counts. The presence and absence of two arboreal mammals was assessed, with 115 hair tubes for the European red squirrel (Sciurus vulgaris), and 132 nestboxes for what concerns the hazel dormouse (Muscardinus avellanarius). Each site spatial structure was also studied, through circular sample plots in which the vertical organization and composition of the herbaceous, shub and arboreal layers was described. Aerial photographs were used to describe and quantify landscape patterns. Four classes of land cover were identified (woodland, cropland, hedges, shrubs, grassland and anthropic areas) and mapped with GIS. Two software were employed to describe the landscape configuration according to the developed map, Patch Analyst and Image Analyzer. The extracted metrics were both structural and functional, and for every land cover class, percentage of occurrence, nearest neighbor, edge density, spatial aggregation and several more parameters were calculated.
To avoid multicollinearity a principal components analysis (PCA) was performed on the variables, divided in three subsets describing
used as explanatory variables for the analysis of the target species distributions.
The most abundant species present in the bird communities were also the most widespread. The consideration of the existing relationship between single species and environmental variables stressed how the whole community is influenced only by factors describing matrix composition and configuration, and in particular the lack of isolation of the patches, their connectivity and the presence of woods, grasslands and anthropic areas in the landscape. The factors associated to the patch features did not influence the observed pattern. The only feature of the patches that had an effect on the observed abundances was the size of the wood (R2 =0.435, p<0.001).
Considering species diversity, the smaller and poorer sites tended to contain subsets of the species present in bigger and richer woods, showing a significant nested pattern that mostly involved generalist species.
Using the species’ life history traits, the communities were subdivided in several different guilds, considering their breeding period diet, feeding technique, nest location, average laying date, clutch size and body mass. For each subgroup, the percentage of species and individuals present in each site was calculated, and included in multiple regression models. The fact that patch related factors, as the extent of the canopy cover, the canopy height and the average tree diameter, could not be disregarded in any of the models indicates the importance of the available internal components for species persistence. The contemporaneous consideration of landscape and patch analysis scales led to the emergence of different patterns, highlighting the presence of different driving forces, belonging to the two scales, contemporaneously shaping community structure.
The effect of landscape heterogeneity on the two considered mammal species was studied using logistic regressions. The resulting models were ranked using second order Aikaike Information Criterion (AICc and ∆AICc) and the importance of the included parameters was evaluated with Akaike weights (wi).
The use of three different sets of variables confirmed the importance of the amount of hedgerows in the landscapes surrounding the patches for the occurrence of both species. The convergence of the two arboreal mammals towards landscapes that offered a higher amount of hedges proved the critical role of this element to ensure species persistence through landscape structural connectivity. The relevance of other elements, as the amount of shrublands and
occurrence is may be linked to the contemporaneous exploitation of sub-optimal habitats, through a process defined “habitat compensation”.
The inclusion of species belonging to different taxa emphasised the matrix essential role, both in terms of composition and configuration, in determining the actual use of residual patches. In a heterogeneous landscape, structure heterogeneity matters in terms of connectivity and lack of isolation, and the presence and position of hedgerows and woods in the landscape were key determinants of species distribution. The contemporaneous consideration of parameters describing landscape and patch features showed how these two elements and their associated scales affect species in a different degree, and their lack of simultaneous consideration may lead to misleading conclusions.
The matrix therefore holds the capability to at least mitigate the effects of isolation and habitat loss, if its management, and hence its permeability, allows animal movement.
La frammentazione ambientale è un processo che comporta la suddivisione di un habitat esteso in una serie di frammenti, separati fra loro da un habitat diverso dall’originale, che viene generalmente definito matrice. Per lo studio dell’effetto di questo fenomeno sulla biodiversità dei frammenti, si è inizialmente ricorso alle teorie che descrivono le dinamiche delle faune insulari (e.g. MacArthur & Wilson). Tale approccio risulta tuttavia riduzionistico in questo contesto, in quanto non permettono l’inclusione delle dinamiche esistenti fra le varie componenti dei paesaggi reali.
Lo scopo del presente lavoro è quello di studiare il rapporto esistente fra la distribuzione di alcune specie animali e l’eterogeneità del paesaggio, ed in particolare di indagare l’effetto della composizione e configurazione della matrice e della struttura dei frammenti sulla composizione delle comunità di vertebrati dei boschi residui.
Lo studio è stato effettuato nelle Marche, in un’area di circa 100.000 ha compresa fra i bacini idrografici dei fiumi Chienti e Potenza. L’area è stata scelta in quanto offre l’opportunità di confrontare frammenti boschivi circondati da paesaggio che hanno subito un diverso grado di antropizzazione.
In quest’area sono stati selezionati 24 siti campione caratterizzati dalla presenza di cerro (Quercus cerris) e roverella (Q. pubescens), all’interno dei quali si è studiata la composizione delle comunità ornitiche presenti e la distribuzione di due specie di mammiferi. La presenza-assenza dello scoiattolo comune (Sciurus vulgaris) è stata analizzata grazie all’uso di 115 trappole per i peli (hair tubes) mentre per il moscardino (Muscardinus avellanarius) sono state adoperate 132 cassette nido. Per le comunità ornitiche, l’abbondanza delle specie è stata invece determinata grazie a 66 punti d’ascolto. All’interno dei frammenti sono stati inoltre svolti dei rilievi vegetazionali, per caratterizzare la composizione e struttura degli strati erbaceo, arbustivo ed arboreo.
Per lo studio della matrice si è ricorso all’utilizzo di sistemi informativi territoriali (GIS) grazie ai quali, con l’ausilio di ortofotocarte, è stato possibile costruire una cartografia di dettaglio con la quale quantificare abbondanza e disposizione di sei macro-tipologie: boschi, arbusteti, praterie, coltivi, siepi ed aree antropiche. Due
Le variabili sono state opportunamente trasformate e sottoposte all’analisi delle componenti principali (PCA) per ridurre i possibili effetti della multicollinearità. I sei fattori derivati sono stati utilizzati come variabili esplicative nelle analisi successive.
Per quanto riguarda le comunità ornitiche, le specie più abbondati sono risultate essere anche quelle più diffuse. L’analisi delle corrispondenze ha inoltre evidenziato che la comunità risponde nel suo insieme unicamente ai fattori legati alla composizione e configurazione della matrice, e in particolare la mancanza di isolamento dei frammenti, la connettività degli stessi e la presenza di boschi, praterie ed aree antropiche nella matrice. I fattori associati alla composizione e struttura dei frammenti sono risultati ininfluenti. L’unica caratteristica dei frammenti ad influire sull’abbondanza delle specie è l’estensione degli stessi (R2=0.435, p<0.001).
Il confronto delle diverse comunità ha portato a riscontrare come i siti meno estesi e più poveri di specie tendano a contenere sottoinsiemi delle specie presenti nei siti più estesi e ricchi. Questo pattern nidificato è risultato statisticamente significativo ed ha coinvolto principalmente le specie più generaliste.
Le comunità sono state suddivise in diverse guild, in base alla composizione della dieta nel periodo riproduttivo, alla tecnica di foraggiamento, alla posizione del nido, al periodo di deposizione, alla dimensione della covata ed alla massa corporea. Per ogni categoria, sono state calcolate le percentuali di specie e di individui presenti in ogni frammento, e queste grazie all’uso di regressioni lineari multiple hanno permesso di evidenziare l’importanza dei fattori legati al frammento, quali la chiusura della volta forestale, l’altezza dello strato emergente ed il diametro degli alberi. La mancata univocità delle risposte riscontrate per le varie guild porta a concludere che i fattori che hanno influenzato la struttura delle comunità siano molteplici, e che agiscano contemporaneamente a scala di frammento e di paesaggio.
L’effetto dell’eterogeneità ambientale sulle due specie di mammiferi è stata studiata per mezzo di regressioni lineari. Tramite il confronto fra i valori ottenuti dal test di verifica delle informazioni di Akaike (AICc, ∆AICc, Wi), sono stati evidenziati i parametri più idonei
a descrivere le distribuzioni osservate. L’utilizzo di tre distinti set ha confermato l’importanza della percentuale di siepi nei paesaggi circostanti i frammenti per entrambe le specie. L’influenza di quest’elemento sottolinea il valore della connettività strutturale dei boschi residui per garantire la persistenza delle popolazioni.
forestale impoverisce fortemente le risorse disponibili nei boschi, la persistenza delle specie sia legata al contemporaneo sfruttamento di più risorse, grazie al processo precedentemente definito “habitat compensation”.
L’inclusione di specie appartenenti a diversi taxa ha permesso pertanto di mettere in luce il ruolo chiave della matrice, sia in termini di composizione sia di struttura, nel determinare l’utilizzo dei boschi residui. Dal punto di vista strutturale è stata riscontrata l’importanza delle siepi e della vicinanza di altri boschi, degli elementi semi-naturali che in un paesaggio frammentato permettono alle specie di contrastare il crescente isolamento grazie alla connettività strutturale. Inoltre l’associazione dei parametri relativi al frammento ed al paesaggio nel determinare la presenza delle specie dimostra la criticità della contemporanea considerazione delle due scale d’analisi.
La matrice ha in se la potenzialità di mitigare gli effetti dell’isolamento e della perdita di habitat, se la sua gestione, e quindi la sua permeabilità, favoriscono il movimento degli animali.
1.
INTRODUCTION
1.1.
Habitat fragmentation
Habitat modification and fragmentation are globally recognised as two of the main causes of biodiversity loss (Fischer & Lindenmayer, 2007). Fragmentation is a process that involves the subdivision of an extensive habitat into several patches, which as a result have a smaller total area, and become isolated by a matrix of habitats different from the original (Wilcove et al., 1986). It is a process that therefore involves both the loss of the fragmented habitat and a change in its configuration (Long et al., 2010) and can affect biodiversity at several levels, altering species biology and behaviour and inter-specific relations (Fahrig, 2003).
One of the first interpretations, formulated by Forman (1995), described landscapes as a mosaics made of three elements: patches, corridors and matrices. Besides this initial assimilation of the residual patch dynamics to theories describing insular fauna (e.g. MacArthur & Wilson, Nested Subset), the influence of the matrix on the fragments has been recently highlighted as a major determinant of actual use (Tubelis et al., 2007). The functional resemblance between oceanic and terrestrial islands is too partial to focus the understanding of landscape fragmentation on the island biogeography theory. The study of the effect of woodland fragmentation on many wood dwelling species can no longer involve the simplistic dichotomy of habitat/non-habitat, of patches embedded in an homogenously adverse medium (Prugh et al., 2008). Patch area and isolation alone are weak determinants of species distribution, since the often human-biased concept of habitat suitability is not a black and white issue (Franklin & Lindenmayer, 2009).
Habitats exist as a part of a landscape mosaic. The properties of the elements of this mosaic are influenced by their context. These relationships are basically shaped by four attributes: the quality of the different patches, the edge effect, the surroundings of the patch and lastly its connectivity (Opdam & Wiens, 2002). The surroundings of the fragment, a sort of widest fragment or background of a landscape, is the matrix (Forman, 1995).
The matrix, originally considered a uniform non-habitat milieu, contains an amount of species-specific suitable habitats that depends on the typology and proportion of the elements that compose it. Furthermore, these elements have a mutual potential effect in terms of edge extent, isolation and connectivity. Hence, this landscape mosaic
can be described and studied considering two basic properties: its composition and configuration (Wagner & Fortin, 2005). The composition may be quantified in terms of number and relative amount of habitat types present, whereas the configuration deals with the spatial arrangement of these elements and can be considered a pattern resulting from both natural and anthropogenic causes (Bennett et al. 2006).
1.2.
Landscape ecology and matrix heterogeneity
As it was stated by Wiens et al. (1993) landscape ecology constitutes the means of studying environmental heterogeneity considering its spatial attributes. Instead of relying on the assumed homogeneity of the context, landscape ecology allows the inclusion of the elements that surround a patch and recognises the interactions that may arise between these elements (Wagner & Fortin, 2005).
Wu (2006) emphasised how landscape heterogeneity is both “a cause and consequence of diversity and complexity in both natural and social systems, and thus plays a key role in dealing with complexity in theory and practice”. Heterogeneity shapes landscapes in different ways: it can be actually observed in the patchiness of a landscape or in the gradual variations of its environmental gradients (Wu, 2007) but it may also be studied as a temporal variation in landscape structure (Kindlmann & Burel, 2008) or as a functional dynamism in the flows of energy and materials (Lindenmayer et al., 2007). It has been highlighted as one of the main factors determining species richness (Atauri & de Lucio, 2001) and it is perceived differently by different species and guilds, according to their requirements and vagility (Bennett et al., 2004).
Many metrics have been developed to investigate the relationship between spatial patterns and ecological processes, to analyse and quantify spatial heterogeneity, to “capture important aspects of landscape pattern in a few numbers” (O’Neill et al., 1988). These metrics have several known limits related to scale and statistics and ecological relevance, but remain an informative tool (Turner, 2005), if used to achieve a better understanding of ecological phenomena though the analysis of the relationship between patterns and processes (Li & Wu, 2004).
1.3.
Matrix and biodiversity
The negative effects of habitat fragmentation on biodiversity are mainly due to the edge effects that act on the residual patches, to the loss of total area per patch (Fahrig, 2003) and to the greater isolation
both in time and space of the residual patches (Ewers & Didham, 2006). The sharp edges that connect residual wood patches to the matrix can have a negative effect on woodland flora and fauna though the proliferation of exogenous predators and parasites (Storch et al., 2005; Wilcove et al., 1986) and microclimate alterations (Lovejoy et al., 1986) leading to the disappearance of many species (Ewers et al., 2007). The temporal component of landscape heterogeneity, its change through time, can further hamper a species diffusion and survival (Kindlemann & Burel, 2008). The persistence of a species may be compromised by a decrease in its metapopulation range, reaching the so called extinction threshold (With & King, 1999), where the loss of habitat and increase of matrix extent can dramatically unbalance the relationship between mortality and reproduction (Fahrig, 2002; Swift & Hannon, 2010), leading a species to a local extinction.
However, not all the effects of the matrix on the biodiversity of residual patches can be considered negative. Even if this term implies the presence of inhospitable environments (Ewers & Didham, 2006), the matrix can represent, for some species, a source of suitable habitats and of corridors that may actually reduce patches isolation and increase persistence (Lindenmayer & Fischer, 2006).
The matrix can favour dispersal if the contrast between matrix and patches and the degree of disturbance improve its permeability to animal movement (Kupfer et al., 2006). Its structure, together with the life traits of the considered species, promotes or inhibits movement, determining the connectivity of the landscape (Tischendorf & Fahrig, 2000). Conversely, the dispersal ability of a species is determined by its vagility, the distance between the residual patches and the nature of the matrix (Weyrauch & Grubb, 2004). Hence, the role of the matrix as a potential corridor is species-specific but as a general rule its structural similarity with the residual patches directly influences its contribution to functional connectivity (Prevedello & Vieira, 2010).
Norton et al. (2000) outlined how some species may use the matrix to offset habitat loss in a process called habitat compensation. It has been observed that the quality of agricultural habitat can increase the passerines (Brotons et al., 2005) and lepidopterous (Tscharntke et al., 2002) occupancy of residual natural habitats.
The matrix therefore holds the potential to at least mitigate the effects of isolation and habitat loss. However this opportunity is jeopardised by the growing impact of land use and changing land use practices on landscapes and species (Foley, 2005; Opdam & Wiens, 2002) highlighting how the management of these practices is crucial to preserve the surviving biodiversity of altered landscapes. Woods are
mainly reduced to patches, surrounded by crops, pastures and settlements and these extensive uses of land affect not only the matrix but also the inner quality of residual woods. Harvesting implies a continuous change of succession states and the vulnerability of pristine and older conditions (Schmiegelow & Mönkkönen, 2002).
1.4.
Scale of the study
The main scales used in fragmentation studies are focused on edge, patch and landscape (Stephens et al., 2003). This latter scale has been considered the most suitable for the present study, recognising that fragmentation is a landscape scale process and that therefore correct measurements should be taken at this level (McGarigal & Cushman, 2002), that the landscape scale allows to study the effect of environmental heterogeneity and gradients on the composition of the fauna (Pino et al., 2000) and that it consents to represent cultural landscapes, the result of the interaction between natural processes and human activities (Agnoletti, 2007; Schmitzt et al., 2007).
It has been recently highlighted by Prevedello and Vieira (2010) that the study of fragmentation effects should involve the inclusion of both patch and matrix level variables. Thus, the approach adopted in this study can be defined as patch-landscape, that according to McGarigal and Cushman (2002) entails the use of patches as experimental sample units and landscape metrics as explanatory variables.
1.5.
Species selection
Recognising that the perception of a landscape is species-specific (Lindenmayer & Fischer, 2007) and that the response to landscape alteration of different taxa tend therefore to be diverse (Croci et al., 2008), a multi-taxa approach was considered more suitable to tackle the matrix effect on residual woodland zoocoenosis.
Amphibians and reptiles
Amphibians were not considered among the target species of the study, since it has been reported that their distribution is influenced mainly by wetland variables, making woodlot and landscape features irrelevant (Weyrauch & Grubb, 2004; Gibbons et al., 2006). Furthermore the woodlots present in the area are generally arid, steep and far from ditches, representing a residual land use in an area dominated by agriculture. These aspects lead to the conclusion that the study area may be historically poor for what concerns amphibians and
reptiles species sensitive to landscape alteration and the sampling effort was thus focused on other groups.
This consideration was confirmed by the fact that in 620 hours of opportunistic sampling only two individuals belonging to two species of snakes were recorded, the green whip snake Hierophis viridiflavus and the asp viper Vipera aspis and the species distributions reported by the CKmap database (Ruffo & Stoch, 2005) are extremely patchy. Birds
Birds are considered good indicators because of their high detectability compared to other groups, their widespread presence and diversity in most terrestrial habitats (Bibby, 2002) and sensibility to natural and anthropogenic environmental alterations (Fleishman & Mac Nally, 2006). Given the high number of different species with different requirements that may be found in the considered environment, birds allow the study of the effect of landscape heterogeneity on community parameters and guilds, and the individuation of sensitive subgroups (Caprio et al., 2009; Devictor & Robert, 2009).
Mammals
Since arboreal mammals are excellent indicators of ecosystem health (Carey, 2000), proved suitable model-species of fragmentation studies (Koprowski 2005) and sensitive to landscape modification (Bright & Morris, 1996), the Eurasian red squirrel, Sciurus vulgaris (Linnaeus, 1758) and the hazel dormouse, Muscardinus avellanarius (Linnaeus, 1758) were included among the target species. Besides, the contemporaneous inclusion of two species with similar requirements and different dispersal abilities may allow the study of the influence of matrix composition and configuration on species vagility.
1.6.
Aims and objectives
The aim of the present study is the investigation of the relationship existing between animal species distribution and landscape heterogeneity and, in particular, to examine the potential influence of the matrix surrounding the patches. The contemporaneous effects of matrix composition and configuration on the distribution of the target species are studied, to test the independent and simultaneous effect of the landscape as a functional mosaic, made of natural, semi-natural and anthropic elements (Bennett et al. 2006) and of environmental heterogeneity gradients. Since wood structure and composition may be perceived differently by species and guilds (Cushman et al. 2008), a
third level of analysis is added: the wood patch, and metrics describing its internal features, are used as a further set of explanatory variables (Figure 1.1).
The underlying objectives of the present project are therefore:
To test if matrix configuration influences the residual patches species composition, and if fragments surrounded by a higher amount of woodland contain richer assemblages.
To determine if matrix organization affects species richness, and in particular if landscapes with a higher connectivity support more guilds and species sensitive to fragmentation sensu isolation.
To verify if matrix composition and configuration can determine the rise of habitat compensation phenomena.
To identify if there are features of the residual patches that are particularly linked to the presence of some species, and if the structural heterogeneity of the woods can explain the observed distributions.
To distinguish between the effect of the three considered factors, matrix composition and configuration and patch characteristics, on the vertebrate assemblages that occupy the fragments and evaluate which aspects may be more critical for species persistence and conservation.
1.7.
Structure of the work
The present study is divided in four chapters: Chapter 1, offers an overview of the subject; Chapter 2, presents the obtained results; in Chapter 3, the gathered data are analysed considering together the results obtained with the different methods and Chapter 4, summarises the conclusions and recommendations inferred.
Appendix 1 describes and justifies the methods used in the present study whereas Appendix 2 and 3 contain the supplementary materials.
2.
RESULTS
2.1.1.
Patch and landscape structure
Sixty-six circular plots were performed inside the sample patches. The woods were predominantly characterized by young individuals, a typical coppice structure, with a medium oak diameter of 13.47 cm. The most common tree species, apart from the downy oak (Quercus pubescens), were the manna ash (Fraxinus ornus) and the hop hornbeam (Ostrya carpinifolia). The understory consisted of several species of shrubs, the most widespread were the common juniper (Juniperus communis), the common dogwood (Cornus sanguinea) and the Eurasian smoketree (Cotinus coggygria). Eight metrics were estimated describing patch structure and composition (see Appendix II for a complete list). 0 10 20 30 40 50 60
Crop Wood Shrub Anthropic Hedge Grassland
%
Figure 2.1. Amount of the six considered land cover types present in the study area. Considering the whole study area, the matrix surrounding the wood fragments was principally composed by crops (52%) and woods (34%), with scattered patches of shrubs (5%), anthropic areas (4%), hedges (3%) and grassland (2%) (Figure 2.1). Nineteen variables were extracted from the maps developed with GIS.
To avoid multicollinearity, not all the developed metrics were included in the subsequent analysis, and those highly correlated were excluded (see Appendix II for correlation scores). As a result, a subset of 15 metrics was created and they were subdivided into three groups, related to wood patch
internal structure, matrix composition and matrix configuration. A Principal Components Analysis was then performed within each group, and a total of 6 factors were extracted.
According to Kaiser’s criterion, only the factors with an eigenvalue equal or greater than one were included in the following analyses. Three factors were extracted for what concerns patch composition and structure, that according to the loadings of the original variables (Table 2.1) can be described as being related to wood patch age (canopy height and tree diameter: FPA1), to upper structure (canopy cover, shrub cover (-) and dead wood amount: FPA2) and lower structure (herbaceous cover and dead wood amount (-): FPA3).
FPA1 FPA2 FPA3 FCM1 FCF1 FCF2 2.72 1.39 1.07 1.73 1.84 1.02 38.86 19.88 15.34 57.59 36.81 20.35 Canopy cover -0.04 0.82 -0.04 Canopy height 0.86 0.30 0.03 Tree diameter 0.94 -0.14 0.00 Dead wood 0.28 0.51 -0.60 Herbaceous cover 0.16 0.23 0.83 Shrub cover -0.09 -0.63 -0.16 Crop % -0.82 Grassland % 0.82 Anthropic % 0.62
Wood mean nearest neighbor -0.08 0.91
Wood-hedge shared perimeter 0.81 0.09
Crop mean nearest neighbor -0.73 -0.24
Polygon perimeter/area -0.34 -0.64
Eigenvalue
Percent of variance explained
Landscape composition PCA Patch PCA
Landscape configuration PCA
Table 2.1. Results of the three principal components analyses (Varimax rotation method with Kaiser normalization) involving respectively variables associated to patch composition and structure, landscape composition and landscape configuration. Factor scores of the rotated component matrix for the variables that explain a high percentage of the variance.
Only one factor, CM1, was extracted from the metrics associated to matrix composition, related to the amount of anthropic areas, grassland and crops (-). Two factors were extracted concerning matrix configuration: CF1 connected to
landscape connectivity (the woods and hedges shared perimeter and crop mean nearest neighbour(-)) and CF2 associated to patch lack of isolation (wood mean nearest neighbour).
The six factors were used to order the sample sites though a correspondence analysis. As it can be seen in Figure 2.5 the sites appear very diverse in their association to the environmental factors, showing both heterogeneous structure and context.
A third set of landscape descriptors was added to the analyses regarding hazel dormouse and European red squirrel distribution, derived from the 1:50,000 regional vegetation associations map (Catorci et al., 2007) and describing the dominant tree species of the woods present in the matrix.
2.2.
Bird communities
2.2.1.Description
A total of 58 species were recorded, belonging to 28 different families and 11 orders (taxonomy according to BirdLife International 2010). The 69% of the species belonged to the order Passeriformes and the second most abundant orders were Columbiformes and Piciformes with 7% of the species. Only the 40 species that actually sang from inside the patches were included in the analysis and are listed in Table 2.2, the rest are listed in Table 2.3
Correlation: r = 0.605
0 2 4 6 8 10 12 14 16 18 20 22 24 26
N° of sites were present -2 0 2 4 6 8 10 12 M e a n a b u n d a n c e 95% confidence Figure 2.2. Correlation between the number of sites where a bird species is present and its mean abundance.
S p e ci e s C o m m o n n a m e N ° o f si te s w h e re p re se n t % o f si te s w h e re p re se n t M e a n a b u n d a n ce A cc ip it e r n is u s Li n n a e u s, 1 7 5 8 E u ra si a n S p a rr o w h a w k 1 4 .1 7 0 .0 4 A e g it h a lo s ca u d a tu s Li n n a e u s, 1 7 5 8 Lo n g -t a il e d T it 1 0 4 1 .6 7 2 .6 7 C a rd u e li s ca n n a b in a L in n a e u s, 1 7 5 8 E u ra si a n L in n e t 1 4 .1 7 0 .0 4 C a rd u e li s ca rd u e li s Li n n a e u s, 1 7 5 8 E u ro p e a n G o ld fi n ch 2 8 .3 3 0 .0 8 C e rt h ia b ra ch y d a ct y la B re h m , 1 8 3 1 S h o rt -t o e d T re e cr e e p e r 8 3 3 .3 3 0 .8 3 C o cc o th ra u st e s co cc o th ra u st e s L in n a e u s, 1 7 5 8 H a w fi n ch 2 8 .3 3 0 .1 3 C o lu m b a l iv ia G m e li n , 1 7 8 9 R o ck P ig e o n 1 4 .1 7 0 .9 6 C o lu m b a p a lu m b u s Li n n a e u s, 1 7 5 8 C o m m o n W o o d -p ig e o n 1 6 6 6 .6 7 1 .0 0 C o rv u s co rn ix L in n a e u s, 1 7 5 8 H o o d e d C ro w 9 3 7 .5 0 1 .5 0 C u cu lu s ca n o ru s Li n n a e u s, 1 7 5 8 C o m m o n C u cko o 2 8 .3 3 0 .0 8 D e n d ro co p o s m a jo r Li n n a e u s, 1 7 5 8 G re a t S p o tt e d W o o d p e cke r 1 5 6 2 .5 0 7 .6 7 D e n d ro co p o s m in o r Li n n a e u s, 1 7 5 8 Le ss e r S p o tt e d W o o d p e cke r 1 4 .1 7 0 .0 8 E m b e ri za c ir lu s Li n n a e u s, 1 7 5 8 C ir l B u n ti n g 6 2 5 .0 0 0 .3 8 E ri th a cu s ru b e cu la L in n a e u s, 1 7 5 8 E u ro p e a n R o b in 2 4 1 0 0 .0 0 0 .0 4 F ri n g il la c o e le b s L in n a e u s, 1 7 5 8 E u ra si a n C h a ff in ch 1 9 7 9 .1 7 2 .9 6 G a rr u lu s g la n d a ri u s L in n a e u s, 1 7 5 8 E u ra si a n J a y 2 1 8 7 .5 0 2 .3 8 Jy n x t o rq u il la L in n a e u s, 1 7 5 8 E u ra si a n W ry n e ck 1 4 .1 7 0 .0 8 Lu sc in ia m e g a rh y n ch o s B re h m , 1 8 3 1 C o m m o n N ig h ti n g a le 2 8 .3 3 0 .0 8 O ri o lu s o ri o lu s L in n a e u s, 1 7 5 8 E u ra si a n G o ld e n O ri o le 6 2 5 .0 0 0 .7 5 P a ru s ca e ru le u s Li n n a e u s, 1 7 5 8 B lu e T it 2 2 9 1 .6 7 5 .5 4
S p e ci e s C o m m o n n a m e N ° o f si te s w h e re p re se n t % o f si te s w h e re p re se n t M e a n a b u n d a n ce P a ru s m a jo r Li n n a e u s, 1 7 5 8 G re a t T it 2 3 9 5 .8 3 0 .3 3 P a ru s p a lu st ri s Li n n a e u s, 1 7 5 8 M a rs h T it 6 2 5 .0 0 2 .3 8 P a ss e r it a li a e V ie il lo t 1 8 1 7 It a li a n S p a rr o w 2 8 .3 3 1 .5 8 P h a si a n u s c o lc h ic u s Li n n a e u s, 1 7 5 8 C o m m o n P h e a sa n t 1 4 .1 7 0 .2 1 P h y ll o sc o p u s b o n e ll i V ie il lo t, 1 8 1 9 B o n e lli 's W a rb le r 4 1 6 .6 7 0 .2 9 P h y ll o sc o p u s co ll y b it a V ie ill o t, 1 8 1 9 C o m m o n C h if fc h a ff 2 0 8 3 .3 3 2 .3 3 P ic a p ic a L in n a e u s, 1 7 5 8 B la ck -b ill e d M a g p ie 1 4 .1 7 0 .0 4 P ic u s v ir id is L in n a e u s, 1 7 5 8 E u ra si a n G re e n W o o d p e ck e r 1 2 5 0 .0 0 1 .9 6 R e g u lu s ig n ic a p il la T e m m in ck , 1 8 2 0 F ir e cr e st 6 2 5 .0 0 0 .5 0 S e ri n u s se ri n u s Li n n a e u s, 1 7 5 8 E u ro p e a n S e ri n 1 4 .1 7 0 .0 8 S it ta e u ro p a e a L in n a e u s, 1 7 5 8 W o o d N u th a tc h 1 5 6 2 .5 0 0 .1 3 S tr e p to p e li a t u rt u r L in n a e u s, 1 7 5 8 E u ro p e a n T u rt le -d o ve 1 0 4 1 .6 7 1 .0 4 S tu rn u s v u lg a ri s Li n n a e u s, 1 7 5 8 C o m m o n S ta rl in g 4 1 6 .6 7 0 .4 2 S y lv ia a tr ic a p il la L in n a e u s, 1 7 5 8 B la ck ca p 2 3 9 5 .8 3 1 0 .4 6 S y lv ia c a n ti ll a n s P a ll a s, 1 7 6 4 S u b a lp in e W a rb le r 7 2 9 .1 7 0 .5 8 T ro g lo d y te s tr o g lo d y te s Li n n a e u s, 1 7 5 8 W in te r W re n 1 7 7 0 .8 3 3 .6 7 T u rd u s m e ru la L in n a e u s, 1 7 5 8 E u ra si a n B la ck b ir d 2 3 9 5 .8 3 5 .1 7 T u rd u s p h il o m e lo s B re h m , 1 8 3 1 S o n g T h ru sh 6 2 5 .0 0 0 .0 4 T u rd u s v is ci v o ru s L in n a e u s, 1 7 5 8 M is tl e T h ru sh 1 4 .1 7 1 .5 8 U p u p a e p o p s Li n n a e u s, 1 7 5 8 E u ra si a n H o o p o e 1 4 .1 7 0 .0 4 Table 2.2. Continuation.
Table 2.3. List of recorded bird species not included in the analyses. S p e ci e s C o m m o n n a m e N ° o f si te s w h e re p re se n t % o f si te s w h e re p re se n t M e a n a b u n d a n ce A la u d a a rv e n si s Li n n a e u s, 1 7 5 8 E u ra si a n S ky la rk 5 2 0 .8 3 0 .5 4 A p u s a p u s L in n a e u s, 1 7 5 8 C o m m o n S w if t 6 2 5 .0 0 0 .5 4 A rd e a c in e re a L in n a e u s, 1 7 5 8 G re y H e ro n 2 8 .3 3 0 .0 8 B u te o b u te o L in n a e u s, 1 7 5 8 C o m m o n B u zz a rd 9 3 7 .5 0 0 .3 3 C e tt ia c e tt i T e m m in ck, 1 8 2 0 C e tt i's W a rb le r 2 8 .3 3 0 .1 7 C o rv u s m o n e d u la L in n a e u s, 1 7 5 8 E u ra si a n J a ckd a w 4 1 6 .6 7 0 .2 1 C o tu rn ix c o tu rn ix L in n a e u s, 1 7 5 8 C o m m o n Q u a il 3 1 2 .5 0 0 .2 1 D e li ch o n u rb ic u m L in n a e u s, 1 7 5 8 N o rt h e rn H o u se -m a rt in 5 2 0 .8 3 1 .5 4 E m b e ri za h o rt u la n a L in n a e u s, 1 7 5 8 O rt o la n B u n ti n g 3 1 2 .5 0 0 .2 1 G a ll in u la c h lo ro p u s Li n n a e u s, 1 7 5 8 C o m m o n M o o rh e n 1 4 .1 7 0 .3 8 H ir u n d o r u st ic a L in n a e u s, 1 7 5 8 B a rn S w a ll o w 3 1 2 .5 0 0 .3 3 Lu ll u la a rb o re a L in n a e u s, 1 7 5 8 W o o d L a rk 9 3 7 .5 0 0 .5 0 M il ia ri a c a la n d ra L in n a e u s, 1 7 5 8 C o rn B u n ti n g 8 3 3 .3 3 0 .3 3 M o ta ci ll a a lb a L in n a e u s, 1 7 5 8 W h it e W a g ta il 2 8 .3 3 0 .0 8 P h o e n ic u ru s p h o e n ic u ru s Li n n a e u s, 1 7 5 8 C o m m o n R e d st a rt 4 1 6 .6 7 0 .1 7 S tr e p to p e li a d e ca o ct o F ri va ld sz ky , 1 8 3 8 E u ra si a n C o ll a re d -d o ve 7 2 9 .1 7 0 .5 4 S y lv ia m e la n o ce p h a la G m e li n , 1 7 8 9 S a rd in ia n W a rb le r 2 8 .3 3 0 .0 4 T a ch y b a p tu s ru fi co ll is P a ll a s, 1 7 6 4 Li tt le G re b e 1 4 .1 7 0 .2 5
Just one species, the robin Erithacus rubecula, was present in every sample patch. The second most common species were the blackbird Turdus merula, the great tit Parus major and the blackcap Sylvia atricapilla (96% of the sites). The 25% of the species were present in only one site and the 13% in only two sites (see Table 2.1). There was a positive correlation (Pearson correlation: r=0.61, p<0.001) between the number of occupied sites and mean species abundance, hence the most abundant species were also the most widespread.
In order to verify that the observed differences in species diversity were not due to a sampling bias, all the samples were rarefied with EcoSim 7.72 (Gotelli & Entsminger, 2010) down to the poorest site. Comparing the expected species diversity for samples with a fixed abundance (10,000 iterations, Rarefaction Curve as randomization algorithm), since all the values fell within the 95% confidence interval, we can assume that the assemblages are comparable (Table 2.4).
Sp. ab. Sp. rich. H J Av. div.* CI* Var*
Agolla 27 12 2.166 0.657 6.08 [4,8] 1.20 Apollinare 88 20 2.463 0.550 6.20 [4,8] 1.30 Boccafornace 48 17 2.663 0.688 7.14 [5,9] 1.00 Campolarzo 9 5 1.523 0.693 5.00 [5,5] 0.00 Castelraimondo 24 12 2.186 0.688 6.42 [5,8] 0.90 Cignano 44 19 2.740 0.724 7.29 [5,9] 1.00 Colle Biocca 82 19 2.650 0.601 6.79 [4,8] 1.20 Colle Severino 61 18 2.648 0.644 6.95 [5,9] 1.05 Crispiero km 8 40 15 2.462 0.667 6.70 [5,9] 1.10 Crispiero Torri 18 12 2.245 0.777 6.89 [5,9] 0.89 Giove 31 13 2.425 0.706 6.87 [5,9] 0.96 Mergnano 14 12 2.069 0.784 6.70 [5,8] 0.73 Mistrano 55 16 2.294 0.572 5.95 [4,8] 1.20 Monteneri 39 14 2.373 0.648 6.46 [4,8] 1.10 Muccia km 1 16 12 1.890 0.682 5.83 [4,7] 0.78 Padullo 56 19 2.663 0.662 6.92 [5,9] 1.20 Palatina 56 14 2.296 0.570 6.07 [4,8] 1.20 Piecollina 53 13 2.350 0.592 6.36 [4,8] 1.01 S. Andrea 17 12 2.262 0.799 7.04 [5,9] 0.83 S. Eustachio 94 16 2.326 0.512 5.92 [4,8] 1.30 Sfercia 24 12 1.752 0.551 4.99 [3,7] 0.71 Tenosa 48 18 2.697 0.697 7.17 [5,9] 1.00 Vaccareccia 83 19 2.572 0.582 6.62 [5,9] 1.13 Fiastra 416 29 2.888 0.479 6.97 [5,9] 1.13
Table 2.4. Species diversity of the sites. Sp. ab, total number of individuals; Sp. rich. number of species; H, Shannon-Weaver index; J, Lloyd-Ghelardi index. (*) Expected diversity of rarefied samples, with 9 as fixed abundance level, Av. div., average diversity; CI, confidence interval; Var, variance.
2.2.2.Communities and environmental variables
A cluster analysis (Ward's dendrogram) was performed to group the sample sites on the basis of their similarity in the bird species assemblages contained (Euclidean distances on species). The result, Figure 2.4, highlights the presence of two separated clusters of sites. This observed pattern does not mirror the spatial arrangement of the sites (see Figure A.3 of Appendix I).
A correspondence analysis was then performed to the bird species according to their association with the PCA factors. As it can be observed in Figure 2.6 the whole bird community is influenced by the same three factors:
1. FCF1: landscape connectivity (wood-hedge shared perimeter) 2. FCF2: lack of isolation (wood mean nearest neighbour)
3. FCM1: % of anthropic areas, grassland and woods (in which the amount was in inverse relation to the amount of crops)
The remaining three factors describing patch structure and composition (FPA1, FPA2 and FPA3) appear irrelevant.
The only intrinsic patch feature that has an effect on species diversity is patch size (Figure 2.7). Actually, the wood size accounts for the 44% of the observed species diversity variability (R2=0.44, P<0.001).
-1 0 1 2 3 4 5 LNSIZE 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 L N S P R2 = 0.44 y = 2.0684 + 0.282*x
Figure 2.3. Regression of bird species abundance (ln) on patch area (ln), data points and fitted line.
Figure 2.5. Correspondence analysis of PCA factors and sample sites:1. Agolla, 2. Apollinare, 3. Boccafornace, 4. Campolarzo, 5. Castelraimondo, 6. Cignano, 7. Colle Biocca, 8. Colle Severino, 9.
Crispiero km 8, 10. Crispiero Torri, 11. Giove, 12. Fiastra, 13. Mergnano, 14. Mistrano, 15. Monteneri, 16. Muccia, 17. Padullo, 18. Palatina, 19. Piecollina, 20. S. Andrea, 21. S. Eustachio, 22. Sfercia, 23. Tenosa, 24. Vaccareccia.
Figure 2.6. Correspondence analysis of PCA factors and bird species. 1. Sylvia atricapilla, 2. Carduelis carduelis, 3. Parus major, 4. Parus palustris, 5. Parus caeruleus, 6. Columba palumbus,
7. Aegithalos caudatus, 8. Corvus cornix, 9. Cuculus canorus, 10. Phasianus colchicus, 11. Carduelis cannabina, 12. Regulus ignicapilla, 13. Fringilla coelebs, 14. Coccothraustes coccothraustes, 15. Pica
pica, 16. Garrulus glandaris, 17. Phylloscopus bonelli, 18. Phylloscopus collybita, 19. Turdus merula, 20. Sitta europaea, 21. Dendrocopos major, 22. Dendrocopos minor, 23. Picus viridis, 24. Passer italiae, 25. Erithacus rubecula, 26. Columba livia, 27. Certhia brachydactyla, 28. Oriolus oriolus, 29. Troglodytes troglodytes, 30. Accipiter nisus, 31. Sylvia cantillans, 32. Sturnus vulgaris, 33. Jynx torquilla, 34. Turdus philomelos, 35. Turdus viscivorus, 36. Streptopelia turtur, 37. Upupa epops, 38. Luscinia megarhynchos, 39. Serinus serinus, 40. Emberiza cirlus.
fias apol cign colb padu vacc cols teno bocc mist seus crik mont pala piec giov agol crit sand cast merg muck sfer camp N° PRES Erithacus rubecula 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 24 Parus major 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 23 Turdus merula 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 23 Parus caeruleus 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 22 Phylloscopus collybita 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 20 Sylvia atricapilla 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 23 Fringilla coelebs 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 19 Garrulus glandaris 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 21 Sitta europaea 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 15 Troglodytes troglodytes 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 0 0 0 17 Columba palumbus 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 0 0 16 Dendrocopos major 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 0 0 1 0 0 0 15 Picus viridis 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 12 Aegithalos caudatus 1 1 0 1 1 1 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 10 Streptopelia turtur 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 10 Corvus cornix 1 1 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 9 Certhia brachydactyla 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 8 Sylvia cantillans 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 7 Emberiza cirlus 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 6 Sturnus vulgaris 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 Turdus philomelos 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 Regulus ignicapillus 1 1 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 6 Oriolus oriolus 1 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 6 Parus palustris 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 6 Phasianus colchicus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Serinus serinus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Carduelis carduelis 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 Luscinia megarhynchos 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 Coccothraustes coccothraustes 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 Upupa epops 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Cuculus canorus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 2 Carduelis cannabina 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Phylloscopus bonelli 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 4 Passer italiae 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 Dendrocopos minor 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Columba livia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 Turdus viscivorus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Jynx torquilla 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Pica pica 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Accipiter nisus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 N° SPECIES 25 19 17 18 18 18 17 17 16 15 15 14 13 13 12 12 11 10 10 9 8 7 6 4
2.2.3.Community nestedness
Since larger sites contained a higher number of species, the community structure was studied to verify if this outcome was the result of a nested pattern (i.e. that the species present in poor sites are a subset of those present in richer sites). So, the presence-absence matrix of species per site was sorted by ordering the patches according to their richness (
Table 2.5): some species appeared nested but there were a few unexpected presences and absences to infer a perfectly nested pattern.
The matrix was then tested with the software Nestedness Temperature Calculator (Atmar & Patterson, 1995) and the observed species-site matrix had a temperature of 17° whereas the randomized matrixes obtained with 10000 iterations had an average temperature of 66.62° (Figure 2.7). Since the original matrix had a temperature significantly lower than the simulated matrixes (P < 0.001), a higher degree of order and therefore of nestedness was found in the original data compared to what was expected by simple chance.
Figure 2.7. Nestedness Temperature Calculator results showing the original matrix maximally packed and its simulated temperature.
2.2.4.Guilds structure
To study the existing relationship between functional guilds and environmental variables, the percentage of species and individuals belonging to the groups outlined in the methods appendix (Table A.22) were calculated.
The average results for the number of individuals in each typology are summarised in Figure 2.8. Four categories were identified for what concern the breeding season diet, and the majority of the species (74%) relied on small arthropods, the 20% of the recorded species was omnivore (feeding on two or more of the delineated categories).
Five classes were adopted for the feeding techniques, and the majority of the recorded species were canopy gleaners and ground probers, with 10% of bark foragers and just a few individuals of understory gleaners and ground gleaners.
Four groups were adopted for the location of the nest, and the average number of individuals for each of them (open on tree, open on ground, open on shrub, inside a cavity) was similar.
Four categories were used to describe the clutch size, and the intermediate sizes contained the higher number of individuals, 36% and 38% for clutch sizes of 4 eggs and 5-6 eggs respectively.
According to the average laying date, the species were subdivided in five classes, and 76% of the recorded individuals laid at the beginning of April.
The last considered attribute was the body mass, and the majority of the individuals fell within the first group, weighing less than 20 grams.
Multiple regression models were obtained for the six guilds, considering both the number of species and of individuals belonging to each class in every sample site. The PCA factors (Table 2.1) were used as explanatory variables.
The models that included the percentage of species as dependent variables were not significant.
The significant models obtained using the percentage of individuals belonging to each class as dependent variables are listed in Table 2.6. All of them involved at least one factor describing the patch, highlighting the importance of patch characteristics in shaping guild compositions.
For what concerns species diet, only the model including the amount of granivorous individuals in each sample site was significant, and their distribution was mainly influenced by wood age (FPA1) and amount of woods, grassland and anthropic areas in the matrix (FCM1).
0% 10% 20% 30% 40% 50% 60% 70% 80%
Insectivore Omnivore Granivore Carnivore
Diet 0% 10% 20% 30% 40% 50% Canopy gleaner Ground prober
Bark forager Understory gleaner Ground gleaner Feeding technique 0% 5% 10% 15% 20% 25% 30%
Open in tree Open on ground
Cavity Open in shrub
Nest location
Figure 2.8. Average amount of individuals (%) belonging to the considered guilds in the 24 sample sites.
0% 10% 20% 30% 40% ≤ 3 4 5-6 ≥ 7 Clutch size 0% 10% 20% 30% 40% 50% 60% 70% 80%
March Early April Late April Early May Late May
Average laying date
0% 10% 20% 30% 40% 50% 60% 70% Body mass (g) Figure 2.8. Continuation.
Guild: Model form: df R2 F p Diet
Granivorous FPA1 + FCM1 21 0.33 5.07 0.02 *
Feeding technique
Bark forager FPA1 + FPA2 21 0.27 3.86 0.04 * Ground gleaner FPA1 + FCM1 21 0.26 3.78 0.04 *
Laying date
Early April FPA1 22 0.29 9.07 0.01 **
Late April FPA1 + FPA2 + FCM1 20 0.53 7.65 0.00 **
Early May FPA2 22 0.17 4.35 0.05 *
Clutch size
≤ 3 FPA1 + FPA2 + FPA3 + FCM1 19 0.47 4.20 0.01 * 4 FPA1 + FPA2 + FPA3 + FCM1 + FCF2 18 0.55 4.39 0.01 **
5-6 FPA3 + FCF2 21 0.43 8.01 0.00 **
Body mass
40.1-90.0 FPA1 + FPA2 21 0.43 7.82 0.00 **
140.1-200.0 FCF2 22 0.20 5.46 0.03 *
>200 FPA1 + FPA2 + FCF1 20 0.34 3.44 0.00 **
Table 2.6. Multiple regression models of the percentage of individuals in each guild class.
Two of the categories describing feeding techniques were significantly correlated to different factors. The amount of bark foragers (Certhia brachydactyla, Dendrocopos major, Dendrocopos minor, Parus palustris, Sitta europaea) was influenced by wood age (FPA1) and patch upper layers structure (FPA2). The percentage of ground gleaners was influenced by wood age (FPA1) and amount of woods, grassland and anthropic areas in the matrix (FCM1).
Three of the five categories describing the average laying date were significantly correlated with PCA factors. Birds laying in early April were influenced by wood age (FPA1), birds laying in early May by patch upper layers structure (FPA2), and birds laying in late April by these two factors plus the amount of woods, grassland and anthropic areas in the matrix (FCM1).
Three of the four groups of clutch size were associated to significant models, which involved for the first two (≤ 3 and 4) the three factors describing the patch (FPA1, FPA2 and FPA3) and two factors describing matrix composition (FCM1) and configuration (FCF2). The distribution of birds with bigger clutch sizes was influenced by the patch lower layers structure (FPA3) and the patch lack of isolation (FCF2).
The body mass classes could be significantly associated to wood age and upper structure (40.1-90 grams), to these two factors plus the landscape connectivity (>200 grams) and to the patch lack of isolation (140.1-200 grams).
2.3.
Hazel dormouse
The species was detected in the 36.4% of the wood patches, eight sites out of twenty-two (Table 2.7).
Patch Muscardinus avellanarius Sciurus vulgaris
Agolla 0 0 Apollinare 0 1 Boccafornace 0 1 Campolarzo - 0 Castelraimondo 0 0 Cignano 0 0 Colle Biocca 0 0 Colle Severino 0 1 Crispiero km 8 1 0 Crispiero Torri 0 0 Fiastra 0 1 Giove 1 1 Mergnano 0 0 Mistrano 1 0 Monteneri 0 1 Muccia km 1 0 0 Padullo 1 0 Palatina 0 0 Piecollina 1 1 S.Andrea 1 1 S.Eustachio 0 1 Sfercia - 1 Tenosa 1 1 Vaccareccia 1 0
Table 2.7. Hazel dormouse Muscardinus avellanarius and European red squirrel Sciurus vulgaris distribution in the study area.
Its distribution was studied using logistic regression models, considering both original metrics (Table 2.8) and PCA factors (Table 2.9) as explanatory variables.
A set of alternative models was built from all the linear combinations of variables and factors, to which the presence-absence data were fitted. Since the ranking of the models through their second order Akaike information criterion value (AICc)showed that there were several models in the 95% confidence set of the best alternative models
that had a cumulative Wi of 0.95 (Burnham & Anderson, 2002), indicating the presence of model uncertainty.
Model form: -2LL AICc ΔAICc Wi
Wood% 23.901 31.234 0.000 0.203
Wood% + Wood MNN 22.006 32.359 1.125 0.116
Hedge % 25.699 33.032 1.798 0.083
Hedge % + Wood MNN 23.062 33.415 2.181 0.068
Wood% + Anthr. % 23.325 33.678 2.444 0.060
Canopy cover + Wood % 23.447 33.800 2.566 0.056
Shrub cover + Wood % 23.592 33.945 2.711 0.052
Hedge % + Wood % 23.886 34.239 3.005 0.045
Canopy cover 27.191 34.524 3.290 0.039
Anthr. % 27.588 34.921 3.687 0.032
Hedge % + Canopy cover 24.608 34.961 3.727 0.031
Hedge % + Shrub cover 24.728 35.081 3.847 0.030
Wood % + Anthr. % + Wood MNN 21.547 35.297 4.063 0.027
Wood% + Canopy cover + Wood MNN 21.749 35.499 4.265 0.024
Hedge % + Anthr. % 25.174 35.527 4.293 0.024
Shrub cover 28.683 36.016 4.782 0.019
Wood MNN 28.825 36.158 4.924 0.017
Canopy cover + Anthr % + Wood % 22.893 36.643 5.409 0.014
Canopy cover + Anthr. % 26.345 36.698 5.464 0.013
Canopy cover + Wood MNN 27.170 37.523 6.289 0.009
Shrub cover + Canopy cover 27.173 37.526 6.292 0.009
Shrub cover + Anthr. % 27.420 37.773 6.539 0.008
Anthr. % + Wood MNN 27.562 37.915 6.681 0.007
Canopy cover + Anthr. % + Wood MNN + Wood % 21.264 38.864 7.630 0.004
Shrub cover + Wood MNN 28.683 39.036 7.802 0.004
Canopy cover + Anthr. % + Wood MNN 26.318 40.068 8.834 0.002
Shrub cover + Wood MNN 28.683 39.036 7.802 0.004
Table 2.8. Hazel dormouse Muscardinus avellanarius regression models, using original metrics as explanatory variables, ranked according to second order Akaike’s information criteria (AICc ). Wi, Akaike weights.
To determine the relative importance of the different variables and factors included in the models, the sum of Akaike weight (Wi) for each parameter was calculated as the sum of the Wi of all the models where the parameter occurred within the 95% confidence set. The parameters were then ranked, considering that the importance of a variable is proportional to its sum of Wi (Burnham & Anderson, 2002). The ranking results are summarised in Table 2.10.
Variables describing the amount of hedges and wood in the matrix were the most important determinants of dormouse distribution concerning the models including the original metrics. For the models
derived using PCA factors, the canopy cover (FPA2) and the amount of woods, grassland and anthropic areas in the matrix (FCM1) were principal aspects that influenced the species distribution.
Model form: -2LL AICc ΔAICc Wi
FPA2 26.688 31.319 0.000 0.140 FCM1 26.742 31.373 0.053 0.136 FPA2 + FCM1 25.218 32.551 1.231 0.076 FPA1 28.125 32.756 1.436 0.068 FPA1 + FPA2 25.816 33.149 1.829 0.056 FCM1 + FPA1 25.895 33.228 1.908 0.054 FPA3 28.815 33.447 2.127 0.048 FCF2 28.831 33.462 2.142 0.048 FCF1 28.839 33.471 2.151 0.048 FCF1 + FPA2 26.301 33.634 2.314 0.044 FCM1 + FPA3 26.320 33.653 2.333 0.044 FCF2 + FCM1 26.420 33.753 2.433 0.041 FCF2 + FPA2 26.603 33.936 2.616 0.038 FPA2 + FPA3 26.653 33.986 2.666 0.037 FCF1 + FPA1 26.699 34.032 2.712 0.036 FPA1 + FPA3 28.097 35.430 4.110 0.018 FCF2 + FPA1 28.104 35.437 4.117 0.018 FCF2 + FPA3 28.805 36.138 4.818 0.013 FCF1 + FPA3 28.812 36.145 4.825 0.013 FCF1 + FCF2 28.828 36.161 4.841 0.012 FCF1 + FCM1 28.828 36.161 4.841 0.012
Table 2.9. Hazel dormouse Muscardinus avellanarius regression models, using PCA factors as explanatory variables, ranked according to second order Akaike’s information criteria (AICc ). Wi, Akaike weights.
The models derived with the third set of covariates, those extrapolated from the regional vegetation associations map (Catorci et al., 2007) also showed a substantial model uncertainty. The ranking of the involved factors, included in the 95% confidence set are listed in Table 2.14. The most important parameters were the presence of hop hornbeam (Ostrya carpinifolia) woods, shrubs and coniferous reforestations, however there was not a substantial difference in the cumulative Akaike weights of the different factors.
