Biogeography of the world’s worst invasive species has spatially-biased knowledge gaps but is predictable

The world’s “100 worst invasive species” were listed in 2000. The list is taxonomically diverse and often cited (typically for single-species studies), and its species are frequently reported in global biodiversity databases. We acted on the principle that these notorious species should be well-reported to help answer two questions about global biogeography of invasive species (i.e., not just their invaded ranges): (1) “how are data distributed globally?” and (2) “what predicts diversity?” We collected location data for each of the 100 species from multiple databases; 95 had sufficient data for analyses. For question (1), we mapped global species richness and cumulative occurrences since 2000 in (0.5 degree)2 grids. For question (2) we compared alternative regression models representing non-exclusive hypotheses for geography (i.e., spatial autocorrelation), sampling effort, climate, and anthropocentric effects.
Reported locations of the invasive species were spatially-biased, leaving la..., Data Acquisition and Processing
Data were acquired from multiple data bases for the 100 invasive species in February 2022 using the spocc package in R (Chamberlain 2021). Data sources (in alphabetical order) included: the Atlas of Living Australia ('ALA'; https://www.ala.org.au); eBird (http://www.ebird.org/home; Sullivan et al. 2009); the Integrated Digitized Biocollections ('iDigBio'; https://www.idigbio.org; Matsunaga et al. 2013); the Global Biodiversity Information Facility (GBIF (https://www.gbif.org); Ocean 'Biogeographic' Information System ('OBIS'; https://portal.obis.org; Grassle and Stocks 1999); VertNet (https://vertnet.org; Constable et al. 2010); and the US Geological Survey’s Biodiversity Information Serving Our Nation ('BISON'; replaced December 2021 by GBIF). Several databases set limits to 100,000 initial point records (before cleaning, described below) when accessed using spocc. As a result, data for 19 species with >100,000 point records (e.g., the European starli..., , # Biogeography of the world’s worst invasive species has spatially-biased knowledge gaps but is predictable

[https://doi.org/10.5061/dryad.zw3r228bh](https://doi.org/10.5061/dryad.zw3r228bh)

The provided datatoanalyze.csv file represents data further processed in provided R code to include spatial autocorrelation for each of species richness and cumulative occurences analyses.

## Description of the data and file structure

The data file includes 59586 rows and 19 columns. NAs indicate missing data. Columns include:

* a row ID
* lon: longitude (decimal degrees) for the center of a 0.5 degree grid cell
* lat: latitude (decimal degrees) for the center of a 0.5 degree grid cell
* UN: the UN code for the country
* ISO3: the ISO3 code for the country
* NAME: the country name
* Country: may be identical to NAME, but some differences (e.g., The Republic of ...) occur via different data sources
* corrupt: the corruption score (range = -2.5 to 2.5) for the country, from the Wo...

2000年，全球"100种最严重入侵物种"名录正式发布。该名录涵盖的类群具有丰富的分类学多样性，常被引用（多应用于单物种研究），名录中的物种也频繁出现在全球生物多样性数据库中。本研究秉持以下原则：对这些臭名昭著的入侵物种进行全面的信息上报，以解答关于入侵物种全球生物地理学的两个核心问题（而非仅聚焦其入侵范围）：其一，数据在全球范围内的分布格局如何？其二，哪些因素能够预测物种多样性？
我们从多个数据库中收集了这100种入侵物种的分布点位数据，其中95个物种拥有足够用于分析的有效数据。针对第一个问题，我们以0.5°×0.5°的网格为单元，绘制了2000年以来全球物种丰富度及累计出现频次的空间分布图。针对第二个问题，我们对比了多组备选回归模型，这些模型分别对应入侵物种分布的非排他性假说：包括地理空间效应（即空间自相关）、采样强度、气候条件以及人为活动影响。
入侵物种的已上报分布点位存在空间偏差，导致…… 数据获取与处理
本研究于2022年2月借助R语言spocc包（Chamberlain, 2021），从多个数据库中获取了这100种入侵物种的分布数据。数据来源按字母顺序排列如下：澳大利亚生物图集（Atlas of Living Australia, ALA, https://www.ala.org.au）；eBird观鸟平台（http://www.ebird.org/home; Sullivan et al., 2009）；整合数字化生物标本库（Integrated Digitized Biocollections, iDigBio, https://www.idigbio.org; Matsunaga et al., 2013）；全球生物多样性信息设施（Global Biodiversity Information Facility, GBIF, https://www.gbif.org）；海洋生物地理信息系统（Ocean Biogeographic Information System, OBIS, https://portal.obis.org; Grassle & Stocks, 1999）；VertNet生物标本数据库（https://vertnet.org; Constable et al., 2010）；以及美国地质调查局本国生物多样性信息服务平台（Biodiversity Information Serving Our Nation, BISON，2021年12月被GBIF替代）。
部分数据库在通过spocc包调用时，对初始点位记录（清洗前，详见下文）设置了10万条的上限。因此，对于19个点位记录超过10万条的物种（例如欧洲椋……），
# 全球最严重入侵物种的生物地理学：存在空间偏差的认知缺口但具备可预测性
https://doi.org/10.5061/dryad.zw3r228bh
本次提供的datatoanalyze.csv文件为经配套R代码进一步处理后的数据，可直接用于物种丰富度与累计出现频次分析中的空间自相关计算。
数据与文件结构说明
该数据文件共包含59586行与19列，其中NA代表缺失值。各字段说明如下：
* 行号ID
* lon：0.5°网格单元中心点的经度（十进制度）
* lat：0.5°网格单元中心点的纬度（十进制度）
* UN：国家的联合国代码
* ISO3：国家的ISO 3字母代码
* NAME：国家名称
* Country：与NAME字段可能一致，但因数据来源不同可能存在差异（例如"共和国"的表述差异等）
* corrupt：该国的腐败感知指数（取值范围为-2.5至2.5，数据源自世界……）