Why most conservation monitoring is, but need not be, a waste of time. J Env Manag
ABSTRACT Ecological conservation monitoring programmes abound at various organisational and spatial levels from species to ecosystem. Many of them suffer, however, from the lack of details of goal and hypothesis formulation, survey design, data quality and statistical power at the start. As a result, most programmes are likely to fail to reach the necessary standard of being capable of rejecting a false null hypothesis with reasonable power. Results from inadequate monitoring are misleading for their information quality and are dangerous because they create the illusion that something useful has been done. We propose that conservation agencies and those funding monitoring work should require the demonstration of adequate power at the outset of any new monitoring scheme.
- SourceAvailable from: Mélanie BOUYSSOU
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- "Ensuite , les statistiques se basent sur un échantillon qui est supposé décrire la population tout en sachant que la récolte des données est dépendante des conditions météorologiques et de l ' observateur ( Colin & Laszlo , 2006 ) . "
DESCRIPTION: Mémoire d'examen pour le Master IEGB, dispensé à Montpellier. Résumé : Deux populations de Couleuvres vipérines ont été étudiées en Poitou Charente sur deux types d’habitats différents : l’une dans un biotope marécageux sur la réserve de Moëze Oléron et l’autre le long de la Boutonne sur la pisciculture de Fontenille Saint-Martind'Entraigues. Leur comparaison sur le plan biométrique (taille, condition corporelle, longueur des mâchoires,) et populationnel (sex-ratio, âge ratio, reproduction) mettent en évidence des différences multiples entre ces deux populations.
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- "While this is widely acknowledged, very few vegetation monitoring studies described in the literature appear to have evaluated the suitability of visual estimates for detecting changes in cover over time, by quantifying the extent of variability due to these sources and how it affects the power of statistical tests to detect change or min. E.S. (Legg and Nagy 2006; Milberg et al. 2008; Vittoz et al. 2010; Barrett and Gray 2011). Here, we set out to determine the extent of variability in visual cover estimates due to observer bias, growth form and plot relocation precision, using pilot study data collected to evaluate a sampling design to detect long-term changes in Newnes Plateau shrub swamp vegetation due to drying or disturbance . "
ABSTRACT: Visual cover estimates from fixed plots are often used for monitoring changes in wetland vegetation. However, while it is recognised that estimates can vary due to observer bias, vegetation height and differences in plot placement between surveys, the effects of this variability on power to detect change are rarely assessed. We used vegetation survey data from shrub swamps in the Blue Mountains, Australia, to quantify variability in visual cover estimates from these sources and how it affects power to detect change between surveys, at the swamp scale, using paired-sample t-tests. Key variables included total cover of live green vegetation, bare ground and open water and the proportional cover of native vs exotic and/or terrestrial vs amphibious species, pooled across multiple 1 m2 plots, per transect. Minimum detectable effect sizes for these variables ranged from a <1% to 23% change in mean cover per swamp, at the lowest possible level of replication (n = 3 transects), decreasing as replication increased. Our results highlight how useful pilot study data and power analyses are for assessing the adequacy of a monitoring methodology and sampling design, particularly the effects of sampling variability and replication on the magnitude of changes that can be detected.Wetlands 09/2015; In press. DOI:10.1007/s13157-015-0694-7 · 1.57 Impact Factor
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- "Type-II errors occur when no change is detected, but a change has occurred. Measures of statistical error rates are important for interpreting experimental results and informing experimental designs required to meet research objectives and resolve hypotheses about patterns in species richness (Field et al. 2004, 2007; Legg & Nagy 2006). Indeed, these metrics can provide direct measures of the ability of estimators to correctly detect changes in species assemblages, whereas simple measures of bias and precision cannot. "
ABSTRACT: 1. The performance of species richness estimators can be highly variable. Evaluating the accuracy and precision of different estimators for different assemblages is common in the ecological literature, but estimator performance is rarely measured in terms of research goals such as detecting patterns in diversity. 2. We evaluated the efficacy of nonparametric richness estimators to detect changes (i.e. type-I and type-II error rates) in species richness using two experimental designs: a block design and a trend analysis. We also evaluated estimator efficacy across a variety of species-abundance distributions, species number and sample sizes. The evaluation was performed using simulated data that mimicked the qualities of real data to ensure real-world relevance. 3. We found that the bias and precision of all estimators evaluated had high sensitivity to sample size and shifts in the species-abundance distribution. Importantly, all estimators demonstrated elevated type-I error rates when the species-abundance distribution varied. These inflated type-I error rates resulted in spurious conclusions about patterns in species richness. 4. Results suggest that caution should be taken when using nonparametric estimators to detect pattern in species richness. Furthermore, estimator evaluations should always include measures of type-I and type-II error rates. These quantities can reveal the inference consequences of the dependency of estimator bias and precision on community and sampling characteristics.Methods in Ecology and Evolution 09/2015; DOI:10.1111/2041-210X.12462 · 6.55 Impact Factor