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| The loss of biotic integrity in ecosystems due to human pressure has been receiving much attention from the scientific community. The primary aim of this study is to understand how the increasing human pressure on natural forests in the Azorean archipelago (North Atlantic) is affecting their epigean arthropod communities and which biological parameters it affects most. An expert team did fieldwork covering most of the natural forests (mainly inside nature reserves) of the archipelago using standardized pitfall trapping. To build a multimetric index we tested a number of taxonomic and ecological parameters that can potentially be influenced by disturbance. Sixteen of these were found to be significantly influenced by disturbance in forests. We retained seven metrics due to both, desirable scalability properties and relatively low correlation between them. These included the percentages of endemic and predator species richness and also predator abundance, which are inversely related to disturbance; and the percentages of native and saprophagous species richness and introduced and herbivore abundance, which are positively related to disturbance. All seven metrics were combined in an Index of Biotic Integrity (IBI) value. We then proceeded to understand which potential disturbance factors are influencing the biotic integrity of communities and how such influence is felt. Five disturbance factors were found to influence the IBI, although in different ways: the size and fragmentation of reserves, the distance of sites to the reserve borders, the invasion by alien plants and the density of human paths at the sites. Given that only percentages of taxonomical or ecological characteristics were chosen as metrics, we tested and found the scalability of the IBI to be possible, allowing the comparison of sites with different collecting effort or even the comparison of reserves with different areas and numbers of collecting sites in each. Finally, we propose a novel graphical representation for multimetric indices like the IBI, one which allows retaining much of the information that is usually lost in multimetric indices. | |
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