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Preserving species diversity is critical to ensure ecosystem functioning; however, different components of diversity might respond to human disturbance in different ways. Similarly, trophic levels might have uncoupled responses to the same disturbance, thus ameliorating or aggravating the persistence of ecological communities. In this study, we analysed how the density, richness and evenness of flowers and pollinators respond to four levels of woodland thinning intensity (0, 30, 50 and 70% of woodland basal area removed) over 2 years in three contrasting sites. We found a mismatch in the response of flowers and pollinators to thinning. Flower density and richness had disparate responses, depending on the site and year, while evenness did not change with thinning. In contrast, pollinator density and richness, but not evenness, consistently increased with thinning among years and sites. These results suggest that thinning has a great influence on pollinators through changes in abiotic conditions and, perhaps, flower attractiveness rather than through small‐scale changes in flower density and richness. At the site where tree flowers were absent, bee pollinator community composition was impoverished, suggesting that trees provide important floral resources to pollinators. Our findings indicate that disturbance may diminish local plant abundance and richness, but pollinator abundance and richness are enhanced after intense thinning at small scales. [ABSTRACT FROM AUTHOR]

The proper use of statistics is key for professionals who answer questions from data, including ecologists. However, statistics is generally confusing for these professionals, in part due to difficulties related to its terminology. Many of these difficulties derive from the multiple meanings that a term has, both inside and outside the statistical scope. For Spanish-speaking professionals, the translation of English terms also contributes to this confusion. In this paper we show (and intend to clarify) some of these problems from two key terms of an introductory statistics course: error and bias. These terms are discussed in the different contexts that involve problem resolution using statistics: sampling, measurement, estimation, inference and prediction. Error is inherent to statistics and is used to quantify different types of variability or to indicate the possibility of making mistakes on decision making, depending on the context. On the other hand, bias reflects the tendency towards certain values and/or elements, and leads to erroneous conclusions if not avoided. We propose that the problems associated with lexical ambiguity should to be addressed from university teaching and based on this, we offer some recommendations. Thus, the present article not only offers a guide for professionals to make an adequate use of some statistical terms but also provides a contribution for teaching. [ABSTRACT FROM AUTHOR]

The construction of a network capturing the topological structure linked to the interactions among species and the analysis of its properties constitutes a clarifying way to understand the functioning of an ecosystem at different scales of analysis. Here, we present a novel systematic procedure to profit from the enhanced information derived from considering its multiple levels and apply it to analyse the presence of keystone species. The proposed method presents a way to unveil the information stored in a network by comparing it to some randomised modification of itself. The randomising of the original network is done by swapping a controlled number of links while preserving the degree of the nodes. Then, we compare the modularity value of the original network with the randomised counterparts, which gives us a measure of the amount of relevant information stored in the first one. Once we have verified that the modularity value is meaningful, we use it to perform a community analysis and a characterisation of other topological properties in order to identify keystone species. We applied this method to a pollinator–plant–herbivore trophic network as a case study and we found that (a) the comparison between the modularity of the original and the randomised networks is a suitable tool to detect relevant information; and (b) identifying keystone species yields different results in bipartite networks from the ones obtained in networks of more than two trophic levels. We also analysed the effect of eliminating selected species from the system on the cohesion of the network. The selection of these species was made according to the centralities values, such as degree and betweenness, of the corresponding nodes. Our findings show that our analysis, mainly based on the measure of modularity is a reliable tool to characterise ecological networks. Additionally, we argue that since degree and betweenness are not always correlated, it is more reliable to measure both in an attempt to detect keystone species. The methodology proposed here to identify keystone species can be applied to other ecological networks currently available in the literature. • The measure of modularity is a reliable tool to characterise ecological networks. • Degree and betweenness are not always correlated. • The utility of betweenness as a tool for keystone species selection is revealed. [ABSTRACT FROM AUTHOR]