Interspecific Competition in Birds

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Biotic homogenization was initially attributed to invasion by non-native species in response to the globalization of commerce and transport [7] , [8]. More recently, renewed interest in biotic homogenization has revealed non-random impacts of anthropogenic activities, including urbanization, atmospheric pollution and agricultural land-use, on local native species assemblages [9]. Geographic range expansion of widespread generalist species is occurring at the expense of sensitive specialist species [2].

Biotic homogenization is often driven by novel disturbance regimes and permanent changes to landscape structure [4] , [10]. For example, frequent forest-cutting and fire in the central New England Region of the United States during the early 17 th century, presented a novel and massive disturbance regime which disrupted forest dynamics at a regional scale with long-term changes to floristic composition and similarity [11].

More recently, Ekroos et al.

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This study showed a decrease in beta-diversity in response to increasing arable field cover at the landscape scale, associated with an increase in the proportion of generalists and highly mobile butterfly species. Increased competition from successful generalist species may also enhance the process of biotic homogenization in human-modified landscapes, although this hypothesis remains untested. In Australia, cooperative interspecific aggression by the noisy miner Manorina melanocephala , a native honeyeater, is known to have a strong impact on the structure of avian species assemblages across the agricultural and woodland landscapes of eastern Australia [13].

These cooperative breeders form large colonies with all individuals contributing to territory defense.

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A congener, the yellow-throated miner M. The noisy miner is well known for reducing species richness of woodland bird communities and excluding smaller species [15] — [18]. Therefore, in Australian agricultural regions, both landscape change and altered interspecific interactions may act synergistically as drivers of biotic homogenization.

Interspecific Competition between Wild Dogs & Lions

In this paper, we addressed the question: does landscape structure and competition from a widespread generalist native species, drive taxonomic and functional homogenization across space? We defined spatial biotic homogenization as an increase in the similarity of assemblage composition through space i. Specifically, we defined taxonomic homogenization as an increase in similarity based on species composition, and functional homogenization as an increase in similarity based on functional group composition.

We tested whether woodland patch type, habitat extent, woodland habitat subdivision, land-use intensity and interspecific competition affected biotic homogenization of woodland bird communities in a fragmented agricultural landscape. We also tested the relationship between the mean degree of specialization of avian assemblages and landscape structure and interspecific competition.

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Land clearance for cropping cotton and cereal and pastures began in the s [19] , and continued until , when state legislation was introduced to control broad-scale clearing. Green shading represents woodland vegetation, irrigated land-use shown in blue, dryland land-use shown in pink and pastoral land-use shown in yellow. Also a scatter plot displaying the relationship between the extent and subdivision of woodland habitat within study landscapes, with sites colonized by Manorina honeyeaters shown in red and sites where colonies are absent shown in blue.

Twenty-four squares were subjectively selected to cover a range of landscape patterns. Sites were distributed across four different woodland patch types to minimize bias towards certain patch types. Sites were separated by a minimum of 1 km.

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Within each study site, the observed density of all diurnal birds was recorded for 20 minutes using the active search method. This method allows observers to flush and identify cryptic or quiet species within the search area to make certain of identification, with counts of birds during a specified time period providing an index of abundance [20]. A single observer O. Birds above the canopy were not recorded with the exception of aerial insectivores, predators and scavengers. Surveys were conducted up to 4 hours after sunrise and 2 hours before sunset.

Nine repeat surveys were conducted on non-consecutive days for each site between March and May Survey effort was equally distributed across three seasons, with 3 repeats per season: autumn , spring and autumn We pooled density data from the three seasons into a single data frame to increase the detection of nomadic species within survey sites, which move over large distances and appear more sporadically at sites across the landscape, compared to resident species.

Few seasonal migrants occurred in the assemblages, which showed little compositional variation between seasons. To quantify taxonomic similarity, we analyzed a Bray-Curtis matrix based on summed counts of all species Manorina honeyeaters excluded across all 96 sites. To quantify functional homogenization, we analyzed a Bray-Curtis matrix based on summed counts of all members of functional groups except Manorina honeyeaters across all 96 sites.

Functional groups were defined a priori and based on primary diet and primary foraging strata, resulting in 15 unique groups Table 1. By using this method species with the same diet but different foraging strata were in different groups and vice versa.

We calculated a Specialization Index SI for all species detected in the study area by counting the number of main habitat associations of each species from a list of 24 potential habitat categories ranging from rainforest to grassland see Appendix S1. We used a reputable field guide [21] and an encyclopedia of reference material [22] — [28] to list habitat types regularly utilized by each species according to our category system.


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This method places each bird along a spectrum of habitat specialization, where species with a low SI score are specialists and species with a relatively high score are habitat generalists. For each site, we calculated the mean SI for all species detected, weighted by the total density of each species in the assemblage. The resulting Assemblage Specialization Index ASI for each site was analyzed as a response variable to test its dependence on the explanatory variables. We examined five potential explanatory variables for similarity: patch type, woodland extent, habitat subdivision, land-use intensity and Manorina density.

Continuous variables were converted to factors, each with two, three or four group levels, as a requirement of the PERMDISP test used in the statistical analysis Table 2. We excluded sites colony absent with lesser densities because sites with only one or two individuals, such as individuals undergoing breeding dispersal or extra-territorial foraging forays, are unlikely to be within the actual territory area.

With interspecific aggression directed towards birds within the colony area [30] , Manorina honeyeaters outside their territory area may not have an influence on avian assemblages. We quantified landscape structure within 1 km radius landscapes surrounding each site.

The extent of woodland vegetation and different land-use cover types was mapped by visual interpretation in ArcMap 9. Habitat subdivision was quantified as the number of woodland patches, excluding the study patch. This ranged between 1—22 mean: 7, median: 6 , and was also converted to a two level factor. We created five factors for habitat subdivision each with a different cut-off for low subdivision including three, four, five, six and seven woodland patches. Landscapes with greater woodland habitat subdivision had smaller woodland patch sizes, smaller mean distance between woodland patches and a greater amount of woodland-agricultural edge.

Land cover within the production matrix was mapped as either pasture, dryland cropping or irrigated cropping. A three level factor was used to categorize land-use intensity for each landscape by the dominant land-use type within the production matrix. We considered pastoral land-use to be low intensity because the required inputs are low and edge contrast with woodland patches is relatively low. Dryland cropping was considered intermediate intensity because inputs and outputs are moderate and habitat structure contrasts greatly with woodland. Irrigated land-use has relatively large inputs and outputs and was defined as high intensity.

Multidimensional scaling MDS configuration plots were used to visualize patterns of similarity and the direction of differences in mean distance to group centroid, by superimposing group labels. We used the default setting of 25 restarts, a minimum stress of 0.

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PERMDISP has previously been used to analyze various biotic communities such as vegetation communities [35] , soil fungal communities [36] , soil seed-bank communities [37] , and marine benthic communities [38] , [39] ; although the method appears to have not been used for the analysis of biotic homogenization of avian communities. This test uses the ANOVA F statistic to compare the distances from observations to their group centroid and therefore cannot test the effects of continuous variables, only factors.

P-values are obtained from permutations of residuals, using permutations of samples among groups after centering all groups onto a common location. This removes any location differences and makes the obtained residuals exchangeable under the null hypothesis of homogeneity of dispersions, as opposed to location effects [40]. Under this method the probability of Type I error remains equal to the a priori chosen significance level despite multiple tests [41].

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For factors found to be significant, we tested potential interactions with other explanatory variables in the R program [42]. Log transformed distances to group centroids generated from the PERMDISP routine for a significant factor were used as the response variable in a generalized linear model. We built separate models testing for an interaction between the main grouping factor identified in PERMDISP and the other explanatory variables patch type, the extent of woodland ha , the number of woodland patches and land-use intensity.

For each factor we also tested for an association with variation in site ASI with a Kruskal-Wallis test. We chose this non-parametric test due to unequal sample sizes and uneven variance between factor groups with non-normal distributions. A significant result for this test indicates a relationship between explanatory factors and habitat specialization across the avian assemblage. These data were collected with permission from the University of Queensland Animal Ethics Committee reference no. Group dispersions did not differ significantly with patch type or landscape structure.

The explanatory factor Manorina colony was also significantly associated with variation in the assemblage similarity index ASI , as indicated by the Kruskal-Wallis test results. There were no significant associations between variation in ASI and patch type or landscape structure. There were no statistically significant interactive effects between Manorina colony and any of the landscape structure variables on group dispersions see Appendix S2.

Solid red circles are sites colonized by either Manorina species average density of M. The graph demonstrates a smaller dispersion of sites taxonomic homogenization where Manorina colonies are present compared to sites where colonies are absent. P-values obtained from permutations of residuals.


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  8. Significant results indicate spatial taxonomic homogenization in relation to particular factors. Statistically significant results reject the null hypothesis of no difference between median group ASI. Group dispersions did not differ significantly with the main effects of patch type or landscape structure, although one aspect of landscape structure did interact with the factor Manorina colony.

    This interaction revealed that the significant reduction in within-group dispersion resulting from the presence of Manorina colonies was primarily associated with landscapes dominated by dryland cropping Figure 4. No other interactions were statistically significant see Appendix S3. The graph demonstrates a smaller dispersion of sites functional homogenization where Manorina colonies are present compared to sites where colonies are absent.

    Significant results indicate spatial functional homogenization in relation to particular factors. By contrast, fledging success as a proportion of clutch size was higher for Great Tits although this was not significant Fig. For clutch size the distance between conspecifics was a significant covariate and focal species was significant Table 3. By contrast, location and the interaction between location and focal species were not significant Table 3. Heterospecific distance did not significantly covary with clutch size and was only significantly affected by focal species Table 3.

    For the proportion of chicks fledged conspecific distance only approached significance although focal species was significant Table 3. Heterospecific distance was not a significant covariate for proportion fledged and the only significant effect was location Table 3. Both geographical location and focal species significantly affected the distances between adjacent boxes containing conspecific individuals. For nest boxes containing heterospecific individuals distances were generally smaller but Blue Tits at Treswell occupied boxes much closer to a conspecific individual.

    Focal species was important in determining clutch size and the distance between adjacent boxes containing conspecifics was a significant covariate: in particular, at Treswell the greater the distance between two boxes containing Blue Tits then the larger the clutch size.

    This effect was not observed for clutch size in relation to heterospecific distance, nor was there any effect of distance on proportion of offspring fledged. Nest boxes were supplied in excess at both Riseholme and Treswell Wood, which is typical of many study situations e. Even though nest box distribution was clumped at Treswell, the two tit species were randomly dispersed within the woodland.

    Our results suggest that the distance between adjacent nest boxes was around half of the minimum distances actually used by birds 20—33 m compared with 60—70 m, respectively irrespective of species.