CONCLUSION DEL DIAGNOSTICO

In order to determine the most appropriate ordination method to use in this study, Detrended Correspondence Analysis (DCA, ter Braak & Smilauer, 2001) was carried out on the species dataset. Initial inspection of the results produced eigenvalues of 0.341, 0.117, 0.064 and 0.045 and gradient lengths of 3.193, 3.06, 2.591, and 1.522 for the first four axes respectively. Considering the scale of the analysis and that the gradient lengths for the first two axes were closer to 3, an unimodal ordination method was considered appropriate (e.g. Correspondence Analysis (CA) and Canonical Correspondence Analysis (CCA), ter Braak & Smilauer, 2002). CA is an ordination technique for investigating the separation of species niches or the ecological amplitudes of species. Ter Braak (1986) further developed CCA explicitly devoted to niche separation along environmental gradients (Dolédec et al., 2000). CCA was especially designed to

66 extract the best combination of environmental variables (synthetic gradient) that maximizes the variance of the weighted average species positions (“niche centroids”) (Dolédec et al., 2000). In addition, CCA implies that the importance of environmental measurements is proportional to the number of individuals per site.

Forward selection of explanatory variables

One of the major problems faced by ecologist when investigating why species and communities are structured is the large number of correlated environmental variables available (Blanchet et al., 2008). Hence there is a need to select a subsample of these factors that explain the most amount of variance hence allowing for the development of a parsimonious model that has greater predictive power (Gauch 2003; Blanchet et al., 2008).

Forward selection has been the most used method to select the most appropriate variables within a dataset. It presents the great advantage of being applicable even if the initial data set contains more explanatory variables than sites, which is often the case in ecology (Blanchet et al., 2008). However, it is well-recognised that classical forward selection overestimates the amount of variance explained and inflates the Type I errors. Therefore, in this study, we applied a corrected forward selection procedure as presented by Blanchet et al. (2008).

Before applying this method, we used the variance inflation factors (VIFs) and correlation values within each environmental sub-set (Table 2.2) to explore variable correlation levels and those predictors with high values (VIF>10) were excluded from further analysis. Forward selection was then run within each set to optimize the model fit and to select those variables that explained the most variance in the dataset. Following the methodology outlined in Blanchet et al. (2008) forward selection was only carried out on the predictor set if a global test using all explanatory variables was significant. Thereafter, to prevent over- estimation of variance explained by a given predictor set, two stopping criteria

67 were used: (i) an alpha significance level of 0.05 with p-values (P), obtained from permutation tests (n=9999), corrected after Sidak (1967) for the number of tests, where Ps = 1-(1-P)k where k was equivalent to the number of variables in

the predictor set, (ii) the adjusted coefficient of multiple determination (R2a) of

the global model with all explanatory variables (see Blanchet et al. 2008 for further details). This procedure was repeated for the overall and each bird group dataset.

Variance partitioning

As delineated in Borcard et al. (1992), variance partitioning allows for the measurement of the relative contribution of sets of explanatory variables by using eigenvalues of constrained and partial ordinations. This concept is conceptually linked to the idea that ecological phenomena are explained by non-mutually exclusive processes that overlap in space and time (Borcard et al. 1992) which allows for the quantification of the total percentage of variation explained into unique and common contributions of the sets of predictors (Borcard et al., 1992). Hence, the relative role of climate, land cover, pollution, biological and spatial factors in driving avian communities was evaluated using a variance partitioning technique where the total percentage of variation explained by a Canonical Correspondence Analysis (CCA; Legendre and Legendre 1998) is partitioned into unique and common contributions of the sets of predictors (Borcard, 1992).

This was done for both the overall species dataset and for each of the established avian groups to compare any significant differences in predictor impacts.

Variance partitioning for CCA was done using the outlined sets of predictors (i.e. land cover, climate, pollution, biological and spatial data) divided into different subgroups to facilitate the analysis (i.e. climate, land use and pollution were initially grouped into one single category of environmental drivers). Of the eight fractions that can be calculated in a set of three variables(Fig 3.1), three

68 can be directly obtained from partial CCAs and correspond to the independent effects of those factors (a, b and c in Fig 3.1). The remaining fractions are calculated on the basis of more than one canonical analysis as the joined effects of couple of factors (d, e and f in Fig 3.1) or the combined contribution of all factors (g in Fig 3.1). Also the amount of unexplained variance can be calculated as 1 minus the sum of fractions (h in Fig 3.1).

Analogous to multiple regression, the amount of explained variance in CCA or redundancy analysis is influenced by the number of explanatory variables as well as the sample size (both number of sample sites and species in this examples) (Peres-Neto et al., 2006). Hence in this study we applied the variance partition procedure outlined in Peres-Neto et al., (2006) where coefficients of determination where adjusted for the numbers of predictors in each set of environmental variables. This adjustment, as previously noted, is not only preferable but necessary to provide more accurate estimations and valid comparisons between set of factors when explaining community structure (Peres-Neto et al., 2006). Significance of fractions was tested by permutation tests (n=9999) (Borcard, 1992). b c _e a f d g [h]

69