The ED strategy: how species-level surrogates indicate general biodiversity patterns through an 'environmental diversity' perspective

Biodiversity assessment requires that we use surrogate information in practice to indicate more general biodiversity patterns. ‘ED’ refers to a surrogates framework that can link species data and environmental information based on a robust relationship of compositional dissimilarities to ordinations that indicate underlying environmental variation. In an example analysis of species and environmental data from Panama, the environmental and spatial variables that correlate with an hybrid multi‐dimensional scaling ordination were able to explain 83% of the variation in the corresponding Bray Curtis dissimilarities. The assumptions of ED also provide the rationale for its use of p‐median optimization criteria to measure biodiversity patterns among sites in a region. M.B. Araújo, P.J. Densham & P.H. Williams (2004, Journal of Biogeography 31 , 1) have re‐named ED as ‘AD’ in their evaluation of the surrogacy value of ED based on European species data. Because lessons from previous work on ED options consequently may have been neglected, we use a corroboration framework to investigate the evidence and ‘background knowledge’ presented in their evaluations of ED. Investigations focus on the possibility that their weak corroboration of ED surrogacy (non‐significance of target species recovery relative to a null model) may be a consequence of Araújo et al.'s use of particular evidence and randomizations. We illustrate how their use of discrete ED, and not the recommended continuous ED, may have produced unnecessarily poor species recovery values. Further, possible poor optimization of their MDS ordinations, due to small numbers of simulations and/or low resolution of stress values appears to have provided a possible poor basis for ED application and, consequently, may have unnecessarily favoured non‐corroboration results. Consideration of Araújo et al.'s randomizations suggests that acknowledged sampling biases in the European data have not only artefactually promoted the non‐significance of ED recovery values, but also artefactually elevated the significance of competing species surrogates recovery values. We conclude that little credence should be given to the comparisons of ED and species‐based complementarity sets presented in M.B. Araújo, P.J. Densham & P.H. Williams (2004, Journal of Biogeography 31 , 1), unless the factors outlined here can be analysed for their effects on results. We discuss the lessons concerning surrogates evaluation emerging from our investigations, calling for better provision in such studies of the background information that can allow (i) critical examination of evidence (both at the initial corroboration and re‐evaluation stages), and (ii) greater synthesis of lessons about the pitfalls of different forms of evidence in different contexts.     

植物学的发展趋势及国家自然科学基金委员会植物学科资助状况

根据植物学的发展趋势和植物学科的特点,对该学科采取的资助策略是“重视前沿领域,扶持弱势学科,关注新的学科生长点”;学科资助项目数和经费数逐年增加,2004年投入经费和资助项目数是2001年的2倍;简要介绍了学科遴选项目的原则和2004年度项目申请和资助的情况,提供了2004年度植物学科资助项目一览表。     

``Two' Many Optimalities

In evolutionary biology, a trait is said to be optimal if it maximizes the fitness of the organism, that is, if the trait allows the organism to survive and reproduce better than any other competing trait would. In engineering, a design is said to be optimal if it complies with its functional requirements as well as possible. Cognitive science is both a biological and engineering discipline and hence it uses both notions of optimality. Unfortunately, the lack of a clear methodological stance on this tissue has made it common for researchers to conflate these two kinds of optimality. In this paper, I argue that a strict distinction must kept in order to avoid inaccurate assumptions.     

Macroecology in the age of Big Data – Where to go from here?

Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this “Big Data” trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (GfÖ). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space- and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data-driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.     

Out-of-school experience categories influencing interest in biology of secondary school students by gender: exploration on an Abu Dhabi sample

This study employed the international Relevance of Science Education questionnaire to survey the interest in biology and the out-of-school experiences of Abu Dhabi secondary school students (median age 17, mean age 17.53 and mode age of 16) in the third semester of 2014. It included 3100 participants. An exploratory factor analysis was used to categorise the items for both interest in biology and out-of-school experience. Ten interest in biology and 12 out-of-school experience factors were extracted. The summated means for each factor indicated that ‘health and fitness’ and ‘disease control’ enjoyed highest interests among students. For out-of-school experiences, the two factors of ‘digital applications’ and ‘medical treatment’ received the highest scores. Multivariate analysis of variance revealed that all factors for both interest in biology and out-of-school experience exhibited significant differences between boys and girls. More girls than boys were interested in disease control, reproduction (human biology), alternative science, health and fitness, zoology, and applied cosmetic biology. No significant differences were observed for the remaining five other categories. Furthermore, analysis of variance revealed significant differences between boys and girls with regard to individual items comprising each of the factors. The highest correlations were between the two factors of out-of-school experiences of ‘the natural world’ and ‘learning through observation’ and the interest in biology factor related to ‘plant and animal farming and agriculture’. Results suggested that more emphasis must be placed on students’ out-of-school experience and their engagement in informal learning in contextual outdoor environments to enhance their interest in learning more about biology and the living environment in general.     

The Art of Sorting: Using Venn Diagrams to Learn Science Process Skills

In this article, the author describes activities that have been proven to teach young learners to sort and classify objects that contain more than one attribute. The activities require that students employ the use of sorting hoops and attribute blocks to create Venn diagrams, the assembly of which requires practice. The author also describes crosscurricular uses of these learning tools.