Login / Signup

SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores: I - the spiders from native forests of Terceira and Pico Islands (2012-2019).

Ricardo CostaPaulo Alexandre Vieira Borges
Published in: Biodiversity data journal (2021)
The project SLAM (Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores) is described in detail.Seasonal distribution and abundance data of Azorean spiders, based on a long-term study undertaken between 2012 and 2019 in two Azorean Islands (Terceira and Pico), is presented. A total of 14979 specimens were collected, of which 6430 (43%) were adults. Despite the uncertainty of juvenile identification, juveniles are also included in the data presented in this paper, since the low diversity allows a relatively precise identification of this life-stage in Azores.A total of 57 species, belonging to 50 genera and 17 families, were recorded from the area, which constitutes baseline information of spiders from the studied sites for future long-term comparisons. Linyphiidae were the richest and most abundant family, with 19 (33%) species and 5973 (40%) specimens. The ten most abundant species are composed mostly of endemic or native non-endemic species and only one exotic species (Tenuiphantestenuis (Blackwall, 1852)). Those ten most abundant species include 84% of all sampled specimens and are clearly the dominant species in the Azorean native forests. Textrixcaudata L. Koch, 1872 was firstly reported from Terceira and Pico Islands, Araneusangulatus Clerck, 1757 was firstly reported from Terceira Island, Nerieneclathrata (Sundevall, 1830) and Macaroerisdiligens (Blackwall, 1867) were firstly reported from Pico Island.This publication contributes not only to a better knowledge of the arachnofauna present in native forests of Terceira and Pico, but also to understand the patterns of abundance and diversity of spider species, both seasonally and between years.
Keyphrases
  • climate change
  • human health
  • quality improvement
  • big data
  • machine learning
  • social media
  • health information
  • solid state