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We know citizen science can generate large, high-quality datasets, but as much as this volume of observations demonstrates the potential of citizen science, there are new challenges and opportunities, such as reconnecting people with nature. Focusing on the social dimensions of citizen science The use of the term citizen science suggests that by including citizens or communities, or individuals, in addressing scientific questions that it is by its very nature, a social or a psychological endeavour. Yet, citizen science research has only recently begun to consider what motivates participants to take part and stay engaged (e.g. Geoghegan et al., 2016), and the extent to which citizen science might enhance public understanding of science or engender behaviour change (Bonney et al., 2016). What different roles can citizen science play across the broad temporal and spatial scales that these studies have identified, and to what extent can citizen science play in elevating local or indigenous knowledge in the scientific endeavour, potentially transforming our understanding of what science can do and how it can contribute to solving our contemporary environmental problems?. [Extracted from the article]

Abstract: Many indices have been proposed to measure functional diversity and its four distinct dimensions: functional richness, evenness, divergence and redundancy. Identifying indices that reliably measure the functional diversity dimension(s) of interest requires careful testing of how each index responds to species' traits and abundance distributions. In the absence of a convenient simulation tool, tests with artificial data have to date explored only a limited number of scenarios or have altered trait and abundance distributions only indirectly based on principles of evolution and community assembly. We provide simul.comms, an R function that allows users to test the efficacy of functional diversity indices by easily creating artificial species communities with user‐specified abundance and trait distributions for continuous, ordinal and categorical traits. To illustrate the function's utility, we examine the performance of R, a recently published abundance‐sensitive index for functional redundancy. We use two approaches to designing simulation tests for this example analysis. The first uses simul.comms to create six separate sets of artificial communities to qualitatively assess how R responds to predictable changes in functional redundancy. The second uses simul.comms to independently alter seven community composition parameters, whose influence on R is then examined quantitatively via effect sizes in linear regression. Our analyses indicate that R broadly mirrors expected changes in functional redundancy and predictably responds to changes in community composition parameters. R appears, however, to primarily reflect trait distributions, responding minimally to variance in abundance and counter‐intuitively to abundance range. Further refinement of tools to measure functional redundancy may therefore be desirable. The R tool we provide should assist with refining functional diversity measures, a critical step towards improving our ability to understand and mitigate the impacts of biodiversity loss on ecosystem functioning. Because simul.comms simply produces two linked matrices, a species‐by‐traits matrix and a site‐by‐species abundance matrix, it may be equally valuable in exploring questions and analytical approaches in other areas of community ecology. [ABSTRACT FROM AUTHOR]

Global knowledge about the spatial distribution of species is orders of magnitude coarser in resolution than other geographically-structured environmental datasets such as topography or land cover. Yet such knowledge is crucial in deciphering ecological and evolutionary processes and in managing global change. In this review, we propose a conceptual and cyber-infrastructure framework for refining species distributional knowledge that is novel in its ability to mobilize and integrate diverse types of data such that their collective strengths overcome individual weaknesses. The ultimate aim is a public, online, quality-vetted ‘Map of Life’ that for every species integrates and visualizes available distributional knowledge, while also facilitating user feedback and dynamic biodiversity analyses. First milestones toward such an infrastructure have now been implemented. [Copyright &y& Elsevier]

Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey-based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods Four species richness maps were compiled based on range maps, field-derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad-scale gradients in species diversity. Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large-scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad-scale patterns. [ABSTRACT FROM AUTHOR]

In the face of accelerating biodiversity loss and limited data, species distribution models – which statistically capture and predict species’ occurrences based on environmental correlates – are increasingly used to inform conservation strategies. Additionally, distribution models and their fit provide insights on the broad-scale environmental niche of species. To investigate whether the performance of such models varies with species’ ecological characteristics, we examined distribution models for 1329 bird species in southern and eastern Africa. The models were constructed at two spatial resolutions with both logistic and autologistic regression. Satellite-derived environmental indices served as predictors, and model accuracy was assessed with three metrics: sensitivity, specificity and the area under the curve (AUC) of receiver operating characteristics plots. We then determined the relationship between each measure of accuracy and ten ecological species characteristics using generalised linear models. Among the ecological traits tested, species’ range size, migratory status, affinity for wetlands and endemism proved most influential on the performance of distribution models. The number of habitat types frequented (habitat tolerance), trophic rank, body mass, preferred habitat structure and association with sub-resolution habitats also showed some effect. In contrast, conservation status made no significant impact. These findings did not differ from one spatial resolution to the next. Our analyses thus provide conservation scientists and resource managers with a rule of thumb that helps distinguish, on the basis of ecological traits, between species whose occurrence is reliably or less reliably predicted by distribution models. Reasonably accurate distribution models should, however, be attainable for most species, because the influence ecological traits bore on model performance was only limited. These results suggest that none of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra-specific niche differentiation. [ABSTRACT FROM AUTHOR]