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Amazon Inspire Digital Educational Resources. Amazon Rapids Fun stories for kids on the go. Amazon Restaurants Food delivery from local restaurants. ComiXology Thousands of Digital Comics. East Dane Designer Men's Fashion. Shopbop Designer Fashion Brands. All records were obtained during a minute sampling sessions, in which an experienced observer recorded all birds detected aurally and visually, regardless of distance. A minimum distance of meters was kept between point counts.
All information was compiled in a bird species occurrence database, which included a GIS component. We eliminated all 2x2 km quadrats that had more than half of their area occupied by the ocean, and identified all quadrats that had at least five point counts sampled. The remaining 96 2x2 km quadrats, located mostly in non-forested low-altitude areas, were identified for surveying.
Subsequently, we randomly ranked each of the 1x1 km quadrats in each of the larger quadrats for sampling, to determine sampling priority de Lima et al. Bird sampling To complement the previously compiled bird database, we sampled 91 out of the previously identified 96 quadrats, between January and March Following previous work de Lima ; de Lima et al. The points were at least meters apart, to ensure independence and that the environmental variability inside each quadrat was sampled in the proportion that they occurred within the quadrat. In each point count, we registered all bird species detected visually and aurally.
To maximize the number of sampled points during our short sampling period, counts were made throughout the day. All bird data were compiled in a database totalling 1x1 km quadrats. All species that are aquatic or difficult to identify were excluded Table S9 , leaving a total of 36 species for the analyses. Point counts with zero presences for these species or with inconsistencies between the field land cover classification and the land use map were also excluded.
For each 1x1 km quadrat, an average point count was calculated based on the average of the coordinates of all five point counts. Total species richness, endemic species richness and nonendemic species richness was calculated for every average point count. The slope was initially calculated in decimal degrees and then transformed to percentage.
Topography was represented using a Topography Position Index TPI which allows comparing of each cell s elevation to the mean elevation of a specified neighbourhood Jenness The continuous TPI thus obtained was transformed in a fivecategory discrete variable: Still, given the nature of further analyses, the TPI variable was considered Rainfall was obtained by digitizing the island s mean annual precipitation map in millimetres Silva ; Fig.
The land use map was created based on satellite images Google Earth , supplemented by field information de Lima ; de Lima et al.
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S11 and expert knowledge Fig. First, it was considered a four-category discrete variable: Later, this same variable was transformed in a continuous variable reflecting a gradient of habitat degradation: All variables were considered continuous and standardised to a common in raster grid, using the nearest neighbour sampling method and the TPI raster as a reference. Exploratory analysis Multicollinearity was tested using Spearman s rank correlation coefficient, and visualized in a correlogram built using the corrgram package Wright ; Part I, Section VIII.
Remoteness index and ruggedness were excluded from further analyses, having correlation coefficients with land use and slope, respectively, equal to or higher than 0. To identify potential outliers and analyse variance homogeneity, boxplots were drawn for each environmental variable, using the vegan package Oksanen No outliers were removed from the analysis. The data were divided into a training and a testing set, using the catools package Tuszynski The models were validated using the testing data.
Goodness of fit was analysed with the McFadden s index in the pscl package and with the Residual Deviance Jackman et al. Validation was also explored by plotting the Pearson and Deviance residuals To identify which variables best explain the species richness models, we ran the model averaging function from the MuMIn package to obtain relative variable importance RVI.
To evaluate the response of total, endemic and non-endemic species richness to each continuous variables, we calculated the Spearman s rank correlation coefficient Table S Finally, a map with predictions from each of the three fitted models was generated, using the raster package and the environmental variables in raster format Hijmans et al. Generalized dissimilarity modelling Generalized dissimilarity modelling GDM was used to map beta diversity using the gdm package Manion et al. GDM compares community composition and environmental variables at pairs of sites to predict compositional difference as a function of environmental difference, extrapolating the prediction beyond surveyed sites.
The resulting models give a spatially continuous prediction of turnover, and thus of the spatial structure of diversity. To quantify the compositional dissimilarity between different sites, a dissimilarity matrix was calculated using the Bray Curtis dissimilarity statistics. The model fit was examined by the total deviance explained by the model and by plotting the observed dissimilarities against the predicted values Fig. To assess the model significance of each variable a significance test was made using permutations. The significance testing in the gdm package is still in the early phase of development, and it is therefore rather computationally intensive.
The variable importance was measured as the percent change in deviance explained by the full model and the deviance explained by a model fit with that variable permuted. A robust assessment of model s capacity to generate predictions was made by validating the independent testing set. A k-fold cross-validation was used to test the predictive accuracy of the model, using permutations. The output of the cross-validation was the correlation between the observed and predicted compositional dissimilarities, for the testing set of sites Fig.
A principal components analysis PCA was made on the dissimilarities between classes to reduce dimensionality and assign the first three components to an RGB colour palette red, green and blue. This way, similar colours represent a similar avifauna composition. The output was a raster image composed of three single rasters representing the three ordination axes.
The relative importance of each predictor variable was determined by summing the coefficients of the I-splines from the fitted generalized dissimilarity model Table S The response curves were used to evaluate the response of predicted compositional dissimilarity to each predictor variable Fig. Generalized dissimilarity model categorization An unsupervised classification method was applied to the continuous GDM, using modified k- means classification in the Whitebox Geospatial Analysis Tools v Image Classification menu Lindsay ; Fuss et al.
The algorithm was limited to Euclidian distances smaller than 75, a value that ensured the creation of robust composition categories. The initial cluster centres were generated randomly.
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To explore the differences in species richness inside and outside the STONP, we used the random points QGIS tool in vector menu Quantum GIS Development Team a to sample random points from the total, endemic and non-endemic species richness maps previously created. Total and endemic species richness were calculated for each GDM class. Total and endemic average species richness were calculated for each quadrat. Both median and quartiles were plotted in a single scatterplot to explore the relation between endemic species proportion and detection rate Part V, Section VIII. Some of the poorest areas were also found inside the park, coinciding with higher altitudes and steeper slopes Fig.
Endemic species richness pattern was clear: Non-endemic species followed the opposite pattern: In the south-east of the island, an area can be identified in all three predictive maps, characterized by a smaller number of species than surrounding areas, and it corresponds to a large oil palm plantation. In the total species richness model, none of the environmental variables was statistically significant and relative variable importance RVI was always smaller than To model endemic species richness, land use was the most important variable.
On the other hand, several environmental variables were significant and important to the distribution of non-endemic species richness. The most important variable was rainfall, followed by altitude and land use. Endemic species responded negatively to more intensive land uses, but positively to forested habitats, like native and secondary forests. Whereas non-endemic species had an opposite response and therefore a strong connection to non-forested habitats and humanized landscapes Table S The GDM allowed explaining The most important environmental predictor was land use, followed by rainfall and altitude Table S A larger rate of species turnover was found for high values of land use, meaning that the biggest changes in bird community composition occurred in humanized habitats, like shade plantations and non-forested areas.
In forested habitats, the species composition was similar Fig. Smaller values were associated to bigger species composition turnover rates for slope, altitude, rainfall and TPI. Class 1 corresponds to the large oil palm monoculture, class 2 to the open areas surrounded by agro-forest habitats in slightly wetter regions, class 3 to the most humanized habitats in the driest parts of the island, class 4 to mixed of forested habitats like shade plantations and secondary forest in the north-east and class 5 to secondary and native forests in the centre and south The first class to be separated was class 1, suggesting the existence of a very distinctive bird species assemblage in the oil palm plantation, previously identified in all species richness maps Fig.
Subsequently, there was also an obvious separation between bird assemblages that inhabit more forested habitats classes 5 and 4 and those living in non-forested areas classes 2 and 3. Of the random points generated to assess the number of species, both total, endemic and nonendemic, were located in the park. The predicted number of species was similar inside and outside Figure 2. The boxplots represent the median thick line , the first and third quartiles box , the extremes whiskers and the outliers dots.
Even so, a bigger range of values was found inside the park Fig. There were no major differences in average species richness between all five GDM classes Table 2. However, there were several differences in average endemic species richness: There were also differences in terms of total number of species and total number of endemic species.
Classes 1 and 2 had identical values, namely the lowest total number of species 20 and an intermediate total number of endemics Class 3 had an intermediate total number of species 23 , but the lowest total number of endemics 8. Classes 4 and 5 had the highest total number of species 28 , but class 5 had a higher total number of endemics 19 against Species richness and endemic species richness calculated for each GDM class 1 to 5.
Proportion of endemic species and frequency of endemic species for each GDM class 1 to 5. The bars represent the first and third quartiles of the median values estimated for each quadrat.
We found that the STONP did not protect necessarily the richest assemblages, but did protect those that were richest in endemic species. The highest values of species richness were found in the centre-south of the island, inside native forest, and were almost entirely included in the STONP. Right next to them, two large speciespoor areas can be identified, also mostly included inside the park: This result coincides with previous findings, indicating that endemic species are associated with forest-dominated habitats and avoid humanized landscapes de Lima et al.
The highest values of endemic species richness also tended to occur further away from the coast line.
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Secondary forests are found mostly around native forests, both inside the STONP and in the buffer zone. Although they shelter less endemic species than native forests, they seem to be acting as a transition zone to more humanized areas Atkinson et al. The greatest number of non-endemic species was found in the more humanized habitats near the coast in the north-east of the island Fig. A pattern that is rather contrasting to that of the endemic species richness. The northern exclave of the STONP is the only protected area including areas rich in non-endemic bird species.
Since non-endemic birds tend to avoid forested areas and to use distinct food resources, they do not seem to be competing with the endemics. Instead, the gradient between endemic and non-endemic dominated bird assemblages seems to be facilitated by the gradient of native forest degradation Didham et al. Comparing the distribution of total, endemic and non-endemic species richness Fig. This area corresponds to a large oil palm plantation, characterized by having few bird species, and notably fewer endemics and, proportionally, more non-endemics than the surrounding landscape.
Most endemics rely on complex forest environments and do not find the required resources to subsist in these monocultures Turner et al. Being mostly granivores, the non-endemics also struggle to persist in these plantations, due to the severely impoverished vegetation. Moreover, the extremely wet conditions are not favourable to the production of grains on which they often rely. Studies suggest that The maps of total, endemic and non-endemic species richness Fig.
Species assemblages vary mostly in response to habitat humanization Modelling species composition dissimilarity revealed that bird assemblages were strongly determined by the same humanization gradient that had been identified when analysing species richness: This pattern can be seen in the north and south regions of the island, both of which hold rather distinctive species assemblages linked to a wide rainfall gradient. The large oil palm plantation in the south-east once more reveals a very distinctive species assemblages. The analyses showed a bigger species composition turnover within non-forested habitats Fig.
Composition response curve to land use suggested that bird assemblages were more distinct within humanized than in natural habitats, as already indicated by previous studies de Lima et al. This pattern has been associated with stronger differences between intensive agricultural areas, holding a simplified vegetation, compared to natural ecosystems Waltert et al.
The categorical GDM Fig. Class 1 represents the most distinctive bird community to be isolated, and corresponds to the large oil palm plantation already identified in the species richness maps. Although located in the south, where rainfall is much higher, this class is closer to classes 2 and 3, all of which corresponding to non-forested habitats, where the non-endemic species prevail. Classes 2 and 3 represent lowland non-forested areas, where non-endemic species are frequent.
Class 3 includes the most humanized habitats, in the driest parts of the island, while the similar class 2 appears in open areas surrounded by agro-forest habitats, in slightly wetter regions. Class 5 covers secondary and native forests in the centre and south, and holds without a doubt the community with the highest proportion of endemic species. There is an obvious species turnover from forests, where the endemics are clearly dominant, to more open habitats, where non-endemics become more numerous Lima et al.
Islands are known to have a limited pool of species available to colonize disturbed areas Atkinsons et al. They have been widely colonized by introduced granivore species, since these are better adapted to nonforested habitats than the native, mostly endemic avifauna de Lima et al. On the other hand, the introduced granivores seem to be much less frequent in forested habitats, including the cocoa and coffee shade plantations, even though the vegetation of these agroforestry Our results seem to provide further support for the hypothesis that the landscape being dominated by forested habitats is involved in maintaining and ensuring the overall dominance of the endemic avifauna de Lima et al.
Other factors, such as hunting and the introduction of non-avian forest species might be affecting the avifauna. Hunting has been shown to affect the distribution of birds, and notably large frugivores Carvalho et al. The introduction of non-avian vertebrates, such as feral pigs Sus domesticus and cats Felis catus, rats Rattus sp.
The boundaries of the STONP were established, mostly based in a habitat field survey, and our work represents the first assessment of its adequacy to protect the island s biodiversity. To do so, we evaluated if bird species richness and assemblage composition was well represented within the boundaries of the protected area, paying special attention to the endemic and non-endemic components of avifauna. The STONP covered some of the highest values of total species richness, but also some of the lowest, resulting in no significant differences when compared with areas outside the park Fig.
However, endemic species richness was clearly higher inside the STONP, and non-endemic richness higher outside. These results show that using species richness on its own can be misleading as an indicator of conservation value and that it should be used in combination with other metrics Le Saout et al. These results are also encouraging, since the park limits seem to be well established for the protection of the endemic species, which are the most threatened IUCN and the most interesting species, in terms of global conservation goals de Lima et al.
The STONP is almost entirely composed by areas covering the class 5 we identified by GDM, which represents the richest bird assemblage, having the highest number of species and being mostly composed of endemic Atkinson et al. This class includes almost all native forest and is the bird assemblage best represented inside the STONP All other classes have a poor representation inside the protected area, regardless of how many endemics they hold.
This is of little concern in terms of global species protection, since all endemic and threatened species are included in class 5. The boundaries of the STONP were primarily defined based on native forest distribution, natural barriers and small levels of human pressure, but coincide with the distribution of the bird assemblages that are richest in endemics Albuquerque et al. This match is due to the key determinants of bird diversity patterns being the same environmental factors that were used to define STONP boundaries Rocha ; de Lima et al.
Therefore helping to mitigate many negative impacts of human activities, like hunting and logging Atkinson et al. This way, key STONP will gain a higher level of protection, contributing to the conservation of threatened small-ranged endemic species, like the Dwarf Ibis Bostrychia bocagei Dallimer et al. At last, STONP provides a good example that areas of higher conservation interest can be identified using the distribution of natural habitats and human population.
Protected areas should prioritize natural ecosystems supporting high species richness and high proportions of endemic and threatened species. However, in most cases this information is not available when the boundaries are being defined. Our results suggest focusing first on identifying key natural ecosystems, and then zoning based on the distribution of the different biodiversity components, when these become better known, eventually extending the initial boundaries.
This strategy allows for assessing if protected areas are still achieving their key conservation goals, and adjust them while knowledge on their biodiversity increases.
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Understanding how human actions affect biodiversity is therefore a first step to minimize and prevent further impacts on species and ecosystems. Human population is expected to grow exponentially within upcoming years, making it crucial to learn how to coexist and share world ecosystems and natural resources Cincotta et al. Our study exemplifies how human occupation can influence species distribution. We have shown that the strong gradient of land use intensification is the main responsible for the changes found in bird species assemblages: Given these results, forested patches are vital for the persistence of endemic birds inside a landscape increasingly dominated by intensive land uses.
Thus, we recommend the protection of the remaining native forest and the expansion or improvement of secondary forest, to provide a landscape matrix more suitable for the endemic species. Non-native birds will have the opposite response, since they tend to avoid forested habitats Atkinson et al. Therefore, increasing the forest cover will have the additional benefit of preventing the spread of introduced birds throughout the island.
The establishment of protected areas is one the most important and common conservation measures Myers et al. The STONP was created based on native forest distribution, natural barriers and small levels of human pressure Albuquerque et al. With our study, we concluded that most of the areas with endemic-rich assemblages are well represented inside the park, even though these do not necessarily correspond to the richest bird assemblages.
We emphasize the need to transform these environmental laws into active conservation actions on the field, setting up a monitoring program to stop or at least minimize the still ongoing threats inside and nearby the park, e. The expansion and management of secondary forests for conservation could improve the quality of ecosystems in the STONP buffer zone, which has an important role in the conservation of endemic bird species, helping to minimize possible human impacts inside the park and surrounding areas, while providing additional habitat to many of the endemics.
The current study is an important basis for future studies, and to establish specific monitoring activities and conservation strategies. However, further research is needed to gain a more detailed knowledge about the distribution of each bird species, namely regarding seasonality and single species response to forest degradation. We also highlight the need to gain a better understanding of the impact of other threats, such as hunting and introduced species.
Andren H Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: Journal of Applied Ecology North-Western Journal of Zoology Bird Conservation International 1: Balmford A, Bond W Trends in the state of nature and their implications for human well-being.
R package version Available from accessed May Trends in Ecology and Evolution Blair RB Land use and avian species diversity along an urban gradient. Brown JH On the relationship between abundance and distribution of species. The American Naturalist Ecology and Society Landscape and Urban Planning African Journal of Ecology Diversity and Distributions Garcia da Orta Biodiversity and Conservation 3: Annual Review of Ecology, Evolution, and Systematics Exell AW Catalogue of the vascular plants of S. Trustees of the British museum.
Ferrier S, Manion G, Elith J, Richardson K Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Priority sites for conservation. From Cocoa Monoculture to Petro-State. International Journal of Digital Earth 9: Available from h, t,-0r accessed May Available from accessed September Geographic Data Analysis and Modeling. Available from accessed November Available from accessed April Available from accessed October British Ornithologists Union, Oxford. Applied Econometrics with R. A case study in geomorphometric analysis.
Luck GW A review of the relationships between human population density and biodiversity. Journal of Biogeography McKinney ML Urbanization as a major cause of biotic homogenization. Melo M Bird speciation in the Gulf of Guinea. African Journal of Herpetology Naidoo R Species richness and community composition of songbirds in tropical forest-agricultural landscape. Simple Fisheries Stock Assessment Methods.
A representation approach to conserving the Earth's most biologically valuable ecoregions. Land Sharing and Land Sparing Compared. Proceedings of the National Academy of Sciences Quantum Gis Development Team. Open Source Geospatial Foundation Project. Raster Terrain Analysis Plugin. Topographic position index tpi. Available from ml accessed September Journal of Educational and Behavioral Statistics R Development Core Team R: A language and environment for statistical computing. Available from accessed May Rocha R Birds in humanized landscapes: Tropical Conservation Science 8: Garcia de Orta 6: Visualizing the Performance of Scoring Classifiers.
Stork N Re-assessing current extinction rates. Biodiversity and Conservation DOI: Thiollay JM Responses of an avian community to rain forest degradation. Biodiversity and Conservation 8: Identifying the Need for Biodiversity Assessment. Habitat effects on Afrotropical forest bird diversity. Environmental Variables Table S1.
List of environmental variables used to model each species potential distribution, species richness and species compositional dissimilarity. All variables were built in Quantum Gis program. Environmental raster s characteristics. Dimensions are x cells rows x columns. Distance to coast line in degrees. Separation of flat plain areas and middle slope areas. Both flat and middle slope areas have topography index values comprised between Transforming continuous Topographic Position Index in a categorical variable.
Continuous TPI was transformed to take only positive values before categorization. Flat plain areas were then combined with the categorical topography index to separate flat areas from middle slope areas. Valleys and deep valleys were joint together to form a more representative class. Topography was reclassified so flat areas were considered the reference class with a value of 1. Road Map Friction Surface: Slope in percentage was used as a base raster for the calculation of remoteness index.
This friction surface was reversed to give larger values to remote areas and combined with a road map. A human population density raster based on a kernel density filter applied to localities was used to weight the cost accumulated surface.
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A logarithmic transformation was used to get a better representation of reality. Land use map created by S. Correlogram between environmental variables. Spearman s rank correlation coefficient panel between environmental variables. Ruggedness was excluded, having a correlation coefficient value with slope bigger than. Species were divided in endemic and non-endemic, and according to feeding guilds. Validation of the best multivariable model. The goodness of fit was analysed with McFadden s index. The receiving operating characteristic curve ROC was calculated, as well as the area under the curve AUC to examine the model s performance.
Relative variable importance RVI. The relative variable importance was obtained for every environmental variable from the multivariable species model RVI values range from 0 to 1. Ruggedness was excluded from these models given a variance inflation factor VIF bigger than A relative importance value of 1 means the variable is included in all best models.
Species Rainfall Remoteness Distance to Index Coast Altitude Slope Agapornis pullaria Amaurocichla bocagei Anabathmis newtonii Bostrychia bocagei Bubulcus ibis Chrysococcyx cupreus Columba larvata Columba malherbii Columba thomensis Coturnix delegorguei Dreptes thomensis Estrilda astrild Euplectes albonotatus Euplectes aureus Euplectes hordeaceus Lanius newtoni Lonchura cucullata Milvus migrans Neospiza concolor Onychognathus fulgidus Oriolus crassirostris Otus hartlaubi Ploceus grandis Ploceus sanctithomae Prinia molleri Serinus rufobrunneus Streptopelia senegalensis Terpsiphone atrochalybeia Treron sanctithomae 3.
The coefficients were obtained from single-variable models. Positive coefficients indicate a positive relation between the variable in question and the species response. On the contrary, negative coefficients translate a negative relation between variables and species response, indicating a decrease in species occurrence with an increase in variable values. The degree of increase or decrease is given by the coefficients value.
Species Rainfall Remoteness Distance Index to Coast Altitude Slope Agapornis pullaria x Amaurocichla bocagei x x Anabathmis newtonii x x Bostrychia bocagei x x Bubulcus ibis x x Chrysococcyx cupreus x x Columba larvata 4, x x Columba malherbii x Columba thomensis x Coturnix delegorguei Dreptes thomensis x x Estrilda astrild x Euplectes albonotatus Euplectes aureus Euplectes hordeaceus Lanius newtoni x Lonchura cucullata Milvus migrans x Neospiza concolor -0, Onychognathus fulgidus x x Oriolus crassirostris x Otus hartlaubi x x Ploceus grandis x Ploceus sanctithomae x x Prinia molleri x x Serinus rufobrunneus x x Streptopelia senegalensis Terpsiphone atrochalybeia x x Treron sanctithomae x x Turdus olivaceofuscus -2, x x Uraeginthus angolensis -0, Vidua macroura -0, Zosterops feae -9, x x Zosterops lugubris x x.
Kruskal-Wallis rank test to analyse the difference in relative importance of each environmental variable between endemic and non-endemic species, as well as among feeding guilds. A post hoc Dunn-test with the Benjamini- Hochberg correction was performed to evaluate the differences between feeding guilds. Relative variable importance RVI of each continuous environmental variable. Boxplots drawn with RVI values of all bird species See List of Abbreviations and Acronyms, pages IX to X , representing the median thick line , the first and third quartiles box , the extremes whiskers and the outliers dots.
Endemic species are represented in bold. Relative variable importance RVI of each continuous environmental variable in endemic and nonendemic species. Separate boxplots were drawn with RVI values of endemic and non-endemic species, representing the median thick line , the first and third quartiles box , the extremes whiskers and the outliers dots. Relative variable importance RVI of each continuous environmental variable in every feeding guild species group. Boxplots were drawn with RVI values of each feeding guild O omnivores, G granivores, F frugivores, C carnivores , representing the median thick line , the first and third quartiles box , the extremes whiskers and the outliers dots.
Proportion of species occurrence per land use type Table S8. Proportion of species occurrence per land use type and topography class. The standardized proportion of species occurrence in every land use type and topography class for endemic and non-endemic species E - endemic species, N nonendemic species , as well as for each feeding guild O omnivores, G granivores, F frugivores, C carnivores. All values in percentage. Total proportion of species occurrence was calculated. Exploratory analysis for species richness and composition modelling Table S9.
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For each species the total number of presences was calculated. Correlogram between environmental variables and response variables. Remoteness index and ruggedness had correlation values bigger than 0. Validation of the best model of species richness, endemic species richness and non-endemic species richness. The null hypothesis of the Residual Deviance Goodness of Fit Test is that our model is correctly specified. Pearson and Deviance Residuals. Validation was explored by plotting the Pearson and Deviance residuals against the predicted values for each fitted model scatterplots on the first and third columns.
Species richness and environmental variables. The relative importance RVI was obtained for every variable from each species richness model. The response of total, endemic and non-endemic species richness to each environmental variable was analysed with the Spearman s rank correlation coefficient rho. Generalized Dissimilarity Modelling Figure S Overall model fit in explaining the observed dissimilarities.
The observed composition dissimilarity values were plotted against the predicted composition dissimilarity values. Significance test of GDM model. A significance test was made using permutations to explore model significance. Model fit was examined by the total deviance explained in each model. The full model contains all environmental variables. Further models have a bigger model deviance and less explanatory variables.
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