中国科学院西双版纳热带植物园机构知识库

Understanding the distribution of rare species is important for conservation prioritisation. Traditionally, museums and other research institutions have served as depositories for specimens and biodiversity information. However, estimating abundance from these sources is challenging due to spatiotemporally biased collection methods. For instance, large-bodied reptiles that are found near research institutions or in popular, easily accessible sites tend to be overrepresented in collections compared to smaller species found in remote areas. Recently, a substantial number of observations have been amassed through citizen (or community) science initiatives, which are invaluable for monitoring purposes. Given the unstructured nature of this sampling, these datasets are often affected by biases, such as taxonomic, spatial and temporal preferences. Therefore, analysing data from these two sources can lead to different abundance estimates. This study compiled data on Brazilian reptile species from the Global Information Biodiversity Facility (GBIF). It employed a community-ecology approach to analyse data from research institutions and citizen science initiatives, separately and collectively, to assess taxonomic and spatial species coverage and predict species rarity. Using a 1-degree hexagonal grid, we analysed the spatial distribution of reptile communities and calculated rarity indices for 754 reptile species. Our findings reveal that 87 species were exclusively recorded in the citizen science subset, while 212 were recorded only by research institutions. The number of observations per species in the citizen science data followed a Gambin distribution, which aligns with the expected pattern of abundance in natural communities, unlike the data from research institutions. This suggests that citizen science data may be a more accurate source for estimating species abundance and rarity. The discrepancies in rarity classifications between the datasets were likely due to differences in sample size and potentially other sampling parameters. Nevertheless, combining data collected by both research institutions and citizen science initiatives can help to fill knowledge gaps in reptile species occurrence, thus enhancing the foundation for conservation efforts on a national scale. 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Tradicionalmente, museus e outras institui & ccedil;& otilde;es de pesquisa t & ecirc;m sido utilizados como acervos de esp & eacute;cimes e outras informa & ccedil;& otilde;es sobre biodiversidade. No entanto, & eacute; dif & iacute;cil estimar a abund & acirc;ncia a partir dessa fonte de dados, considerando a tend & ecirc;ncia de m & eacute;todos de coleta enviesados espacial e temporalmente. Por exemplo, r & eacute;pteis de grande porte que ocorrem pr & oacute;ximos a institui & ccedil;& otilde;es de pesquisa ou em locais populares e de f & aacute;cil acesso s & atilde;o melhor representados em cole & ccedil;& otilde;es cient & iacute;ficas do que esp & eacute;cies menores localizadas em & aacute;reas remotas. Recentemente, um grande n & uacute;mero de observa & ccedil;& otilde;es foi acumulado por iniciativas de ci & ecirc;ncia cidad & atilde; que podem ser usadas para biomonitoramento. Dada a amostragem n & atilde;o estruturada, esses conjuntos de dados tamb & eacute;m s & atilde;o afetados por vieses, como as prefer & ecirc;ncias taxon & ocirc;micas, espaciais e temporais. Portanto, analisar dados dessas duas fontes pode levar a diferentes estimativas de abund & acirc;ncia de esp & eacute;cies. Compilamos dados para esp & eacute;cies de r & eacute;pteis brasileiros do Global Biodiversity Information Facility (GBIF). Utilizando uma abordagem de ecologia de comunidades, analisamos dados de institui & ccedil;& otilde;es de pesquisa e de ci & ecirc;ncia cidad & atilde; separadamente e em conjunto para descrever a cobertura taxon & ocirc;mica e espacial das esp & eacute;cies e prever a raridade das mesmas. Utilizando uma grade hexagonal de 1 grau, analisamos a organiza & ccedil;& atilde;o espacial das comunidades de r & eacute;pteis e calculamos & iacute;ndices de raridade para 751 esp & eacute;cies de r & eacute;pteis. Descobrimos que 87 esp & eacute;cies estavam presentes apenas no subconjunto de dados da ci & ecirc;ncia cidad & atilde;, enquanto 212 foram registradas apenas por institui & ccedil;& otilde;es de pesquisa. O n & uacute;mero de observa & ccedil;& otilde;es por esp & eacute;cie nos dados de ci & ecirc;ncia cidad & atilde; se ajustou & agrave; distribui & ccedil;& atilde;o de Gambin, o padr & atilde;o esperado para abund & acirc;ncia em comunidades naturais, ao contr & aacute;rio dos dados de institui & ccedil;& otilde;es de pesquisa, sugerindo que o primeiro & eacute; uma fonte mais precisa para estimar a abund & acirc;ncia e a raridade das esp & eacute;cies. A raz & atilde;o por tr & aacute;s das diferentes classifica & ccedil;& otilde;es de raridade provavelmente se deve a diferen & ccedil;as no tamanho da amostra nos dois subconjuntos de dados e possivelmente tamb & eacute;m em outros par & acirc;metros de amostragem. No entanto, combinar dados coletados por institui & ccedil;& otilde;es de pesquisa e iniciativas de ci & ecirc;ncia cidad & atilde; pode ajudar a preencher lacunas no conhecimento sobre a ocorr & ecirc;ncia de esp & eacute;cies de r & eacute;pteis, fornecendo melhores evid & ecirc;ncias para a conserva & ccedil;& atilde;o em escala nacional.