Accounting for weather and time-of-day parameters when analysing count data from monitoring programs

• Published in: Biodiversity and conservation 2008-12, Vol.17 (14), p.3403-3416
• Main Authors: Bas, Yves, Devictor, Vincent, Moussus, Jean-Pierre, Jiguet, Frédéric
• Format: Article
• Language: English
• Subjects: Animal populations; Biodiversity; Biomedical and Life Sciences; Birds; Climate Change/Climate Change Impacts; Conservation Biology/Ecology; Ecology; Environmental monitoring; Heterogeneity; Life Sciences; Original Paper; Weather
• ISSN: 0960-3115; 1572-9710
• DOI: 10.1007/s10531-008-9420-6

Problems induced by heterogeneity in species and individuals detectability are now well recognized when analysing count data. Yet, most recent techniques developed to handle this problem are still hardly applicable to many monitoring schemes, and do not provide abundance estimates at the point count scale. Here, we show how using simple weather variables can be a useful surrogate to detect variability in species detectability. We further look for a potential bias or loss in statistical power based on count data while ignoring weather and time-of-day variables. We first used the French Breeding Bird Survey to test how each of the counts of the 97 most common breeding species was influenced by weather and time-of-day variables. We assessed how the estimation of each species response to fragmentation could be influenced by correcting counts with such variables. Among 97 species, 75 were affected by at least one of the five weather and time-of-day variables considered. Despite these strong influences, the relationship between species abundance and fragmentation was not biased when not controlling counts for weather and time-of-day variables and further found no improvement in statistical power when accounting for these variables. Our results show that simple variables can be very powerful to assess how species detectability is influenced by weather conditions but they are inconsistent with any specific bias due to heterogeneous detectability. We suggest that raw count data can be used without any correction in case the sources of variation in detectability could be considered independent to the factor of interest.