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Invasive rats and seabirds after 2,000 years                                            1635


          as potential sources of permanent rat populations, since  remove right skew distribution and increase normal-
          86% of islands C5 ha housed ship rats in our dataset.  ity of their distribution (Sokal and Rohlf 1995;
             We reviewed studies reporting ship rat impact on  Russell and Clout 2004). Cory’s and Balearic shear-
          Mediterranean Procellariiformes throughout the entire  water breeding success were arcsin-square-root
          basin by collecting data from published works,  transformed (Sokal and Rohlf 1995). Before con-
          unpublished reports, and personal communications.  structing GLMs, we used Spearman rank correlations
          Finally, we collected data on the breeding success of  to identify colinearity between explanatory variables.
          Cory’s and Balearic shearwaters in three situations  Even though island area and island elevation were
                                                                    2
          (Table 1): (1) rat-inhabited islands, (2) islands where  correlated (r = 0.73), we kept the two terms in the
          rats have been controlled within shearwater colonies,  models since these two factors are known to be
          and (3) rat-free islands (either absent or eradicated).  potential predictors of seabird presence and abun-
          Unfortunately, for the two other Procellariiform species  dance (Schramm 1986; Brandt et al. 1995; Muller-
          (storm petrel and Yelkouan shearwater), reliable data  Dombois 1999; Lomolino 2000; Sullivan and Wilson
          on the breeding success were too sparse to be used.  2001; Catry et al. 2003). Furthermore, explanatory
                                                          value would be lost by arbitrarily dropping one of the
          Statistical analysis                            variables (Russell and Clout 2004). Models were
                                                          constructed in order to identify and interpret explan-
          In order to highlight factors likely to explain ship rat  atory variables, not to maximize predictive power
          presence on Western Mediterranean islands, we con-  (Russell and Clout 2004).
          structed a generalized linear model (GLM, binomial
          distribution, LOGIT link function) for a set of 257
          islands and islets (see Table 1; Appendix 1). For  Results
          seabirds, GLMs (binomial distribution, LOGIT link
          function) were first performed to explore how seabird  Factors affecting the distribution of ship rats
          presence on islands may be influenced by the a priori  on Mediterranean islands
          selected explanatory variables for each seabird species
          (P. yelkouan: n = 135 islands; C. diomedea: n = 180  Ship rats were present on 201 (68.8%) out of the 292
          islands; P. mauretanicus: n = 69 islands; H. pelagicus:  Mediterranean islands for which reliable data were
          n = 101 islands; see Table 1; Appendix 1). We   found. The smallest rat-infested islet was 0.021 ha.
          performed one model for each seabird species since  Rats were found on 36% of islands B0.5 ha and on
          the four species did not share the same geographical  99% of islands C30 ha (Fig. 2). Ship rat presence
          distribution and reliable data on breeding status were  was positively related to island area (Table 2; Fig. 2)
          not available for each species on all islands. Then,  but negatively related to distance to the nearest
          GLMs (normal distribution, IDENTITY link function)  potential source of rats (Table 2). Ship rat presence
          were used to evaluate which variables may explain  was not related (P \ 0.05) to elevation, distance to
          seabird abundance on islands where they breed
          (P. yelkouan: n = 30 islands; C. diomedea: n = 79
          islands; P. mauretanicus: n = 13 islands; H. pelagicus:
          n = 23 islands; Table 1). Finally, the effects of rat
          impact (rat presence, local control, and rat absence),
          year of study and geographical region on the breeding
          success of Cory’s shearwaters were investigated
          through GLM analysis (normal distribution, IDEN-
          TITY link function). Due to the lack of data on
          Balearic shearwater breeding success (n = 13), we
          only investigated the effect of rat impact through a
          non-parametric Kruskal–Wallis ANOVA (Table 1).
                                                          Fig. 2 Proportions of rat-infested islands in relation to island
             All continuous variables (i.e., size, distance,  area. The number of islands used for each area interval is
          elevation and abundance) were log 10 -transformed to  indicated (total n = 292)


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