DOI: 10.5061/dryad.sn02v6xfr
title: Data from: Impact of crop type on biodiversity globally

description: The negative impact of agricultural land on biodiversity is widely
 recognized. However, there remains a knowledge gap regarding the role of
 different crop types in maintaining biodiversity within the agricultural
 landscape. By extracting biodiversity data from global datasets and
 classifying different crop types, we quantified the contribution of
 different crop types to biodiversity. Our results indicate that
 biodiversity levels vary widely among crop types. We found a general loss
 of biodiversity when natural vegetation is converted to agricultural land,
 and highest losses in fiber crops, cereals and oil crops, and least in
 other crops (such as coffee or cocoa) and in mixed crops. In general,
 perennial crops retain more biodiversity than annual crops. Losses of
 biodiversity can be mitigated through mixed cropping of multiple crop
 types, especially by combining annual and perennial crops. The negative
 impact of converting natural vegetation to agriculture is greater in
 tropical than in non-tropical areas, and hence, the import of commodities
 from these biodiversity-rich regions may be particularly detrimental.
 Given the ongoing increase in biodiversity losses from global
 intensification and expansion of agricultural land, maintaining or
 restoring natural vegetation, rating the crop-type specific biodiversity,
 diversifying crops, and preferring perennial over annual crops,
 particularly in the tropics, need to be better considered and implemented
 in global agri-environmental schemes.
description: # Impact of crop type on biodiversity globally
 [https://doi.org/10.5061/dryad.sn02v6xfr](https://doi.org/10.5061/dryad.sn02v6xfr) ## Description of the data and file structure The data are extracted from PREDICTS dataset ([https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1](https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1)).  Data is extracted from PREDICTS database (https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database-v1-1). Detail information can be found in method section or reference (Hudson et al., 2014) . Each row represents data from a site, with each site recording the information of“SS”， "SSB"，"SSBS"，"Longitude"，"Latitude" ，"Predominant_land_use" ， "Class" ， "Species_richness"， "Total_abundance"，"logAbun" ，"Group2"，"Group1". Column "SS": Concatenation of Source_ID (individual published papers or datasets) and Study_number (data within a source that were sampled with the same sampling methods and spanning a contained geographical area). Column "SSB": Concatenation of Source_ID, Study_number and Block (distinct spatial clusters of sampled locations within a study). Column "SSBS": Concatenation of Source_ID, Study_number, Block and Site_number (the sampling locations within a block). Column "Longitude" and "Latitude" : the coordinates of sites. Column "Predominant_land_use" : Three class variables including Primary vegetation, cropland, planation forest. (It should be noted that this land-use information comes from the classification of PREDICTS database). Column "Class" : Three class variables including Insecta, Aves, Arachnida. Column "Species_richness" : the total number of species sampled at a site. Column "Total_abundance" : total number of individuals across all species sampled at a site. Column "logAbun" :Transformed from Total_abundance using log(x+1). Column "Group2" : Crop lifecycle was classified as annual, perennial, or mixed. Column "Group1" : Crop types were grouped into cereals, fruit, fiber, oil crops, roots & tubers, vegetables & melons, sugar crops, forage, pulses, other, and mixed.