Impacto anthropogenic pdf to excel
F, Mexico. Ledeganckstraat 35, , Gent, Belgium. Angelo, Via Cinthia 4, , Napoli, Italy. Box , Amman, , Jordan.
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Box , , Nairobi, Kenya. Box 65, FI, Helsinki, Finland. Rovisco Pais 1, , Lisboa, Portugal. O Box , , Campo Grande, Brazil. Kildare, Ireland. Figure S8. Histogram of the distance from each Site to the nearest country GIS polygon. Figure S9. Histogram of the distance from each Site to the nearest ecoregion GIS polygon.
Table S1. Classification of land-use intensity for primary and secondary vegetation based on combinations of impact level and spatial extent of impact. Table S4. Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project — and avert — future declines.
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We describe and assess a new database of more than 1. The database contains measurements taken in of ecoregions, 13 of 14 biomes, 25 of 35 biodiversity hotspots and 16 of 17 megadiverse countries. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. We make site-level summary data available alongside this article. The full database will be publicly available in Despite the commitment made by the Parties to the Convention on Biological Diversity CBD to reduce the rate of biodiversity loss by , global biodiversity indicators show continued decline at steady or accelerating rates, while the pressures behind the decline are steady or intensifying Butchart et al.
Evaluations of progress toward the CBD's target highlighted the need for datasets with broader taxonomic and geographic coverage than existing ones Walpole et al. Geographic breadth is needed because human impacts show strong spatial variation: most of Western Europe has long been dominated by human land use, for example, whereas much of the Amazon basin is still close to a natural state Ellis et al.
Thus, in the absence of broad coverage, any pattern seen in a dataset is prone to reflect the choice of taxa and region as much as true global patterns and trends. However, long-term data suitable for such modeling have limited geographic and taxonomic coverage, and often record only the presence or absence of species e.
Time-series data are also seldom linked to site-level information on drivers of change, making it hard to use such data to model biodiversity responses or to project responses into the future. Ecologists have therefore more often analyzed spatial comparisons among sites that differ in the human impacts they face. Although the underlying assumption that biotic differences among sites are caused by human impacts has been criticized e.
Collations of well-matched site surveys therefore offer the possibility of analyzing how biodiversity is responding to human impacts without losing taxonomic and geographic breadth. Openness of data is a further important consideration. The reproducibility and transparency that open data can confer offer benefits to all areas of scientific research, and are particularly important to research that is potentially relevant to policy Reichman et al. Transparency has already been highlighted as crucial to the credibility of biodiversity indicators and models e.
We present a new database that collates published, in-press and other quality-assured spatial comparisons of community composition and site-level biodiversity from terrestrial sites around the world.
As of March , the dataset contains more than 1. In this paper we introduce the database, describe in detail how it was collated, validated and curated, and assess its taxonomic, geographic and temporal coverage. We make available a summary dataset that contains, for each sampling location, the predominant land use, land-use intensity, type of habitat fragmentation, geographic coordinates, sampling dates, country, biogeographic realm, ecoregion, biome, biodiversity hotspot, taxonomic group studied and the number of measurements taken.
The full dataset constitutes a large evidence base for the analysis of:. The responses of biodiversity to human impacts for different countries, biomes and major taxonomic groups;. How traits such as body size, range size and ecological specialism mediate responses and.
The summary dataset permits analysis of geographic and taxonomic variation in study size and design. The complete database, which will be made freely available at the end of the current phase of the project in , will be of use to all researchers interested in producing models of how biodiversity responds to human pressures.
Measurements within each data source were taken using the same sampling procedure, possibly with variation in sampling effort, at each site and time;. The paper reported, or authors subsequently provided, geographical coordinates for the sites sampled.
MODIS data are available from early onwards so, after a short initial data collation stage, we additionally required that diversity sampling had been completed after the beginning of Where possible, we also obtained the following see Site characteristics, below, for more details :. An indication of the land use at each site, e. Descriptions of any transects used in sampling start point, end point, direction, etc.
Other information about each site that might be relevant to modeling responses of biodiversity to human activity, such as any pressures known to be acting on the site, descriptions of agriculture taking place and, for spatially blocked designs, which block each site was in.
We collated data by running sub-projects that investigated different regions, taxonomic groups or overlapping anthropogenic pressures: some focused on particular taxa e. We introduced the project and requested data at conferences and in journals Newbold et al. After the first six months of broad searching, we increasingly targeted efforts toward under-represented taxa, habitat types, biomes and regions.
In addition to articles written in English, we also considered those written in Mandarin, Spanish and Portuguese — languages in which one or more of our data compilers were proficient.
To maximize consistency in how incoming data were treated, we developed customized metadata and data capture tools — a PDF form and a structured Excel file — together with detailed definitions and instructions on their usage. The PDF form was used to capture bibliographic information, corresponding author contact details and meta-data such as the country or countries in which data were collected, the number of taxa sampled, the number of sampling locations and the approximate geographical center s of the study area s.
The Excel file was used to capture details of each sampling site and the diversity measurements themselves.
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Most papers that we considered did not publish all the information that we required; in particular, site coordinates and species names were frequently not published. We structured data into Data Sources, Studies, and Sites. The highest level of organization is the Data Source. A Data Source typically represents data from a single published paper, although in some cases the data were taken from more than one paper, from a non-governmental organization report or from a PhD or MSc thesis.
A Data Source contains one or more Studies.
A Study contains two or more Sites, a list of taxa that were sampled and a site-by-species matrix of observations e. All diversity measurements within a Study must have been collected using the same sampling method. For example, a paper might present, for the same set of Sites, data from pitfall traps and from Malaise traps.
We would structure these data into a single Data Source containing two Studies — one for each trapping technique. It is therefore reasonable to directly compare observations within a Study but not, because of methodological differences, among Studies. Sometimes, the data presented in a paper were aggregates of data from multiple sampling methods. In these cases, provided that the same set of sampling methods was applied at each Site, we placed the data in a single Study.
We classified the diversity observations as abundance, occurrence or species richness. Some of the site-by-species matrices that we received contained empty cells, which we interpreted as follows: 1 where the filled-in values in the matrix were all non-zero, we interpreted blanks as zeros or 2 where some of the values in the matrix were zero, we took empty cells as an indication that the taxa concerned were not looked for at those Sites, and interpreted empty cells as missing values.
Where possible, we recorded the sampling effort expended at each Site and allowed the units of sampling effort to vary among Studies. For example, if transects had been used, the Study-level sampling effort units might be meters or kilometers and the Site-level sampling efforts might be the length of the transects.
Where possible, we also recorded an estimate of the maximum linear extent encompassed by the sampling at each Site — the distance covered by a transect, the distance between two pitfall traps or the greatest linear extent of a more complex sampling design see Figure S1 in Supplementary Information for details. We recorded each Site's coordinates as latitude and longitude WGS84 datum , converting where necessary from local grid-based coordinate systems.
Where precise coordinates for Sites were not available, we georeferenced them from maps or schemes available from the published sources or provided by authors.
We then obtained geographic coordinates using geographic information systems GIS for each Site center or point location. For each Site we recorded the dates during which sampling took place. Not all authors presented precise sampling dates — some gave them to the nearest month or year. We therefore recorded the earliest possible start date, the latest possible end date and the resolution of the dates that were given to us.
Where dates were given to the nearest month or year, we recorded the start and end dates as the earliest and latest possible day, respectively. This scheme meant that we could store sampling dates using regular database structures which require that the year, month, and day are all present , while retaining information about the precision of sampling dates that were given to us.
We assigned classifications of predominant land use and land-use intensity to each Site. Previous work has suggested that both the biodiversity and community composition differ strongly between sites in secondary vegetation of different maturity Barlow et al.
Given the likely importance of these classifications as explanatory variables in modeling responses of biodiversity to human impacts, we conducted a blind repeatability study in which one person the last author, who had not originally scored any Sites rescored both predominant land use and use intensity for Sites chosen at random.
Cohen's kappa provides a measure of inter-rate agreement, ranging from 0 agreement no better than random to 1 perfect agreement. Where known, we recorded the number of years since conversion to the present predominant land use.
If the Site's previous land use was primary habitat, we recorded the number of years since it was converted to the current land use. Based on previous work Wilcove et al. These are described and illustrated in Table S3 and Figure S2.
We also recorded the area of the patch of predominant habitat within which the Site was located, where this information was available.
The database schema is shown in Figure S3. Our database linked each Data Source to the relevant record in our Insightly contact management database. This allows us to trace each datum back to the email that granted permission for us to include it in our database. In order to assess the data's geographical and biogeographical coverage, we matched each Site's coordinates to GIS datasets that were loaded into our database:.
Terrestrial Ecoregions of the World The Nature Conservancy , giving the ecoregion, biome and biogeographic realm;. World Borders 0. Biodiversity Hotspots Conservation International Foundation