Publications by authors named "C Perez-Granados"

Biological invasions are a major threat to biodiversity, ecosystem functioning and nature's contributions to people worldwide. However, the effectiveness of invasive alien species (IAS) management measures and the progress toward achieving biodiversity targets remain uncertain due to limited and nonuniform data availability. Management success is usually assessed at a local level and documented in technical reports, often written in languages other than English, which makes such data notoriously difficult to collect at large geographic scales.

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A species' diet is highly dependent on the availability of food resources in space and time, as well as on intrinsic factors such as sex or age. Accurate assessments of variations in the diet composition of bird populations across spatial scales, seasons and demographic groups are essential not only for understanding the basic ecology of species, but also for the conservation of endangered ones. However, our current knowledge about how birds' diet change according to spatio-temporal variations or intrinsic factors is very limited.

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Article Synopsis
  • In recent years, automated and noninvasive wildlife monitoring methods like passive acoustic monitoring (PAM) have gained popularity, utilizing acoustic sensors for ecological data collection.
  • PAM generates a lot of data, which often necessitates machine learning tools for effective analysis; this study integrates PAM with BirdNET, an accessible sound recognition algorithm, to assess its accuracy in identifying three tropical songbird species.
  • The results indicated high precision (72-84%) in recognizing the species and provided insights into their vocal activity patterns, noting that both the Buff-breasted Wren and Thrush-like Wren had peak vocal activity around sunrise and during the months of April to June.
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Insects are the most diverse animal taxon on Earth and play a key role in ecosystem functioning. However, they are often neglected by ecological surveys owing to the difficulties involved in monitoring this small and hyper-diverse taxon. With technological advances in biomonitoring and analytical methods, these shortcomings may finally be addressed.

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The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g.

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