Plant Diversity Patterns and Conservation Implications under Climate-Change Scenarios in the Mediterranean: The Case of Crete (Aegean, Greece)
TL;DR: In this article, a case study of the extinction risk of the endemic plants of Crete (S Aegean) and a case-study upon which a climate-smart conservation planning strategy might be set is provided.
Abstract: Climate change poses a great challenge for biodiversity conservation. Several studies exist regarding climate change’s impacts on European plants, yet none has investigated how climate change will affect the extinction risk of the entire endemic flora of an island biodiversity hotspot, with intense human disturbance. Our aim is to assess climate change’s impacts on the biodiversity patterns of the endemic plants of Crete (S Aegean) and provide a case-study upon which a climate-smart conservation planning strategy might be set. We employed a variety of macroecological analyses and estimated the current and future biodiversity, conservation and extinction hotspots in Crete. We evaluated the effectiveness of climatic refugia and the Natura 2000 network of protected areas (PAs) for protecting the most vulnerable species and identified the taxa of conservation priority based on the Evolutionary Distinct and Globally Endangered (EDGE) index. The results revealed that high altitude areas of Cretan mountains constitute biodiversity hotspots and areas of high conservation and evolutionary value. Due to the “escalator to extinction” phenomenon, these areas are projected to become diversity “death-zones” and should thus be prioritised. Conservation efforts should be targeted at areas with overlaps among PAs and climatic refugia, characterised by high diversity and EDGE scores. This conservation-prioritisation planning will allow the preservation of evolutionary heritage, trait diversity and future ecosystem services for human well-being and acts as a pilot for similar regions worldwide.
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01 Jan 2016
TL;DR: People have search hundreds of times for their favorite books like this evolution in changing environments some theoretical explorations, but end up in malicious downloads instead of enjoying a good book with a cup of tea in the afternoon.
Abstract: Thank you very much for reading evolution in changing environments some theoretical explorations. As you may know, people have search hundreds times for their favorite books like this evolution in changing environments some theoretical explorations, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop.
621 citations
TL;DR: In this paper, the authors evaluated the effectiveness of the Greek SAC in safeguarding the biodiversity hotspots and the endemism centers (EC) at a national scale and in a phylogenetic framework.
Abstract: Biodiversity hotspots (BH) cover a small fraction of the Earth’s surface, yet host numerous endemics. Human-induced biodiversity loss has been increasing worldwide, despite attempts to halt the extinction crisis. There is thus an urgent need to efficiently allocate the available conservation funds in an optimised conservation prioritization scheme. Identifying BH and endemism centres (EC) is therefore a valuable tool in conservation prioritization and planning. Even though Greece is one of the most plant species-rich European countries, few studies have dealt with the identification of BH or EC and none has ever incorporated phylogenetic information or extended to the national scale. Consequently, we are unaware of the extent that Special Areas of Conservation (SAC) of the Natura 2000 network efficiently protect Greek plant diversity. Here, we located for the first time at a national scale and in a phylogenetic framework, the areas serving as BH and EC, and assessed the effectiveness of the Greek SAC in safeguarding them. BH and EC are mainly located near mountainous areas, and in areas supposedly floristically impoverished, such as the central Aegean islands. A critical re-assessment of the Greek SAC might be needed to minimize the extinction risk of the Greek endemics, by focusing the conservation efforts also on the BH and EC that fall outside the established Greek SAC.
37 citations
TL;DR: In this paper, an extinction risk assessment according to the IUCN Criteria A and B for all the endemic plant taxa occurring in Greece, one of the most biodiverse countries in Europe, in a phylogenetically-informed framework was performed.
Abstract: Human-induced biodiversity decline has been on the rise for the past 250 years, due to various causes. What is equally troubling, is that we are unaware which plants are threatened and where they occur. Thus, we are far from reaching Aichi Biodiversity Target 2, i.e., assessing the extinction risk of most species. To that end, based on an extensive occurrence dataset, we performed an extinction risk assessment according to the IUCN Criteria A and B for all the endemic plant taxa occurring in Greece, one of the most biodiverse countries in Europe, in a phylogenetically-informed framework and identified the areas needing conservation prioritization. Several of the Greek endemics are threatened with extinction and fourteen endemics need to be prioritized, as they are evolutionary distinct and globally endangered. Mt. Gramos is identified as the most important conservation hotspot in Greece. However, a significant portion of the identified conservation hotspots is not included in any designated Greek protected area, meaning that the Greek protected areas network might need to be at least partially redesigned. In the Anthropocene era, where climate and land-use change are projected to alter biodiversity patterns and may force many species to extinction, our assessment provides the baseline for future conservation research, ecosystem services maintenance, and might prove crucial for the timely, systematic and effective aversion of plant extinctions in Greece.
32 citations
TL;DR: A phylogenetically informed, conservation prioritization study dealing with the effects of climate change on Crete’s plant diversity and biogeographical patterns, using several macroecological analyses to identify the current and future endemism centers and assessed the impact ofClimate change on the biogeography patterns in Crete.
Abstract: Human-induced biodiversity loss has been accelerating since the industrial revolution. The climate change impacts will severely alter the biodiversity and biogeographical patterns at all scales, leading to biotic homogenization. Due to underfunding, a climate smart, conservation-prioritization scheme is needed to optimize species protection. Spatial phylogenetics enable the identification of endemism centers and provide valuable insights regarding the eco-evolutionary and conservation value, as well as the biogeographical origin of a given area. Many studies exist regarding the conservation prioritization of mainland areas, yet none has assessed how climate change might alter the biodiversity and biogeographical patterns of an island biodiversity hotspot. Thus, we conducted a phylogenetically informed, conservation prioritization study dealing with the effects of climate change on Crete’s plant diversity and biogeographical patterns. Using several macroecological analyses, we identified the current and future endemism centers and assessed the impact of climate change on the biogeographical patterns in Crete. The highlands of Cretan mountains have served as both diversity cradles and museums, due to their stable climate and high topographical heterogeneity, providing important ecosystem services. Historical processes seem to have driven diversification and endemic species distribution in Crete. Due to the changing climate and the subsequent biotic homogenization, Crete’s unique bioregionalization, which strongly reminiscent the spatial configuration of the Pliocene/Pleistocene Cretan paleo-islands, will drastically change. The emergence of the ‘Anthropocene’ era calls for the prioritization of biodiversity-rich areas, serving as mixed-endemism centers, with high overlaps among protected areas and climatic refugia.
25 citations
TL;DR: In this paper , the RUSLE soil erosion prediction model was implemented, using freely available geospatial data and cloud-computing processes, and the results showed that soil erosion is a serious threat to biodiversity in N2K-protected areas.
Abstract: Water erosion is a major threat to biodiversity, according to the European Commission’s Soil Thematic Strategy, as it negatively affects soil structure, soil fertility and water availability for plants. The island of Crete (Southern Greece) has been characterized as a biodiversity hotspot including several Natura 2000 (N2K)-protected areas. The aim of this study was to model the soil loss rate in Crete regarding species richness, habitat types and their conservation status, as well as the MAES (Mapping and Assessment of Ecosystem and their Services) ecosystem types. To this end, the RUSLE soil erosion prediction model was implemented, using freely available geospatial data and cloud-computing processes. The estimated average soil loss in the study area was 6.15 t ha−1 y−1, while there was no significant difference between the terrestrial N2K (6.06 t ha−1 y−1) and non-N2K (6.19 t ha−1 y−1) areas. Notably, the natural habitats of principal importance for the conservation of biodiversity (referred to as “priority” areas), according to Annex I to Directive 92/43/EEC, are threatened by soil erosion with an estimated mean annual soil loss equal to 8.58 t ha−1 y−1. It is also notable that grasslands, heathland and shrubs and sparsely vegetated areas experienced the highest erosion rates among the identified MAES ecosystem types. The results showed that soil erosion is a serious threat to biodiversity in N2K-protected areas. Therefore, there is a need for systematic spatiotemporal monitoring and the implementation of erosion mitigation measures.
24 citations
References
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TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Abstract: We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright 2005 Royal Meteorological Society.
17,977 citations
"Plant Diversity Patterns and Conser..." refers methods in this paper
...Current and future climatic data were obtained from the WorldClim database [29] at a 30 sec resolution....
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TL;DR: It was found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection and the value of GLM in combination with penalised methods and thresholds when omitted variables are considered in the final interpretation.
Abstract: Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
6,199 citations
"Plant Diversity Patterns and Conser..." refers methods in this paper
...7 and VIF < 5—[35]) and used in all the subsequent analyses....
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TL;DR: Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities and performs tests for phylogenetic signal in trait distributions, community structure and species interactions.
Abstract: Summary: Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities. The package calculates phylogenetic diversity metrics, performs trait comparative analyses, manipulates phenotypic and phylogenetic data, and performs tests for phylogenetic signal in trait distributions, community structure and species interactions. Availability: Picante is a package for the R statistical language and environment written in R and C, released under a GPL v2 open-source license, and freely available on the web (http://picante .r-forge.r-project.org) and from CRAN (http://cran.r-project.org).
4,218 citations
"Plant Diversity Patterns and Conser..." refers background in this paper
...where rankobs is the rank of the observed scores compared with those of their null distributions, and runs is the number of randomisations [55]....
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TL;DR: Calculation of PD for different population subsets shows that protection of populations at either of two extremes of the geographic range of the group can significantly increase the phylogenetic diversity that is protected.
4,085 citations
"Plant Diversity Patterns and Conser..." refers methods in this paper
...We also estimated for each grid cell the phylogenetic alpha diversity (PD—Sensu, [57]) of the species inhabiting each of the grid cells with the “picante” 1....
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TL;DR: In this paper, a method is proposed to partition the variation of species abundance data into independent components: pure spatial, pure environmental, spatial component of environmental influence, and undetermined.
Abstract: A method is proposed to partition the variation of species abundance data into independent components: pure spatial, pure environmental, spatial component of environmental influence, and undetermined. The new method uses pre-existing techniques and computer programs of canonical ordination. The intrinsic spatial component of com- munity structure is partialled out of the species-environment relationship in order to see if the environmental control model still holds. The method is illustrated using oribatid mites in a peat blanket, forest vegetation data, and aquatic heterotrophic bacteria. In this latter example, the new method is shown to be complementary to another approach based on partial Mantel tests.
4,005 citations
"Plant Diversity Patterns and Conser..." refers methods in this paper
...We quantified the magnitude of turnover for each environmental variable and the relative importance of that variable in driving species turnover, according to [79,80]....
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