Suchergebnisse

Apps are small task-orientated programs with the potential to integrate the computational and sensing capacities of smartphones with the power of cloud computing, social networking, and crowdsourcing. They have the potential to transform how humans interact with nature, cause a step change in the quantity and resolution of biodiversity data, democratize access to environmental knowledge, and reinvigorate ways of enjoying nature. To assess the extent to which this potential is being exploited in relation to nature, we conducted an automated search of the Google Play Store using 96 nature-related terms. This returned data on ~36 304 apps, of which ~6301 were nature-themed. We found that few of these fully exploit the full range of capabilities inherent in the technology and/or have successfully captured the public imagination. Such breakthroughs will only be achieved by increasing the frequency and quality of collaboration between environmental scientists, information engineers, computer scientists, and interested publics.

"Before starting to outline the structure of biogeography today, it is worthwhile to try to explain how scientists work, and what are their limitations - how far should the student trust what they say and believe? And the best way to learn this is to look at how scientists have behaved in the past, for the research workers of today are no different from them. So history has much to teach us. It is natural to assume that any research worker is free to make any sort of suggestion as to what new idea they might put forward in trying to solve their current problems. The reality is rather different. Just as in the past, the range of what are seen as possible solutions is limited by what contemporary society or science views as permissible or respectable. Attitudes to the idea of evolution (chapter 6) or of continental drift (see below) are good examples of such inhibitions in the 19th and 20th centuries, and the concept of evolution is still controversial today in some societies and communities. The history of scientific debate is rarely, if ever, one of dispassionate, unemotional evaluation of new ideas, particularly if they conflict with one's own. Scientists, like all men and women, are the product of their upbringing and experience, affected by their political and religious beliefs (or disbeliefs), by their position in society, by their own previous judgments and publicly expressed opinions, and by their ambitions-just as "there's no business like show business," there's no interest like self-interest! Very good examples of this, discussed later in this chapter, is the use of the concept of evolution by the rising middle-class scientists of England as a weapon against the 19th-century establishment (see later in this chapter) while, at the individual level, the history of Leon Croizat and his ideas (see later in this chapter) provides an interesting study"--

Here we present the code to generate MoBIs (Maps of Biogeographical Ignorance) for single species. The method is based on the calculation of different dimensions of biodiversity data (spatial, temporal and taxonomic). Specifically, in this method, we calculated completeness of each cell by generating accumulation curves with records of all individuals of a group. We also set taxonomy quality values for each identifier considering their experience with the group. For the temporal dimension, we adjust a decay curve to quantify temporal decay of information with time so that older records of the species are least informative and recent ones are the most informative. These components are summarized to generate an index varying from 0 to 1 (lower and higher ignorance, respectively) for each cell for a species. Finally, for cells without data we performed an interpolation, based on the BI calculated for neighbor cells. ; Funding – This work has been funded by the Brazilian CNPq PVE grant 314523/2014-6. GT was supported by CAPES REUNI doctorate fellowship and PDSE grant no. 11842121. RJL is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 854248.

Species distribution models (SDMs) are subject to many sources of uncertainty, lim-iting their application in research and practice. One of their main limitations is the quality of the distributional data used to calibrate them, which directly influences the accuracy of model predictions. We propose a standardized methodology to cre-ate maps, describing the limitations of occurrence data for covering the distribution of a species. We develop a set of tools based on the general framework of Maps of Biogeographical Ignorance to describe the main sources of data-driven uncertainty: taxonomic stability, environmental similarity, geographical proximity and temporal decay of the underlying biodiversity data. The so-derived indicators of data-driven uncertainty account for inventory completeness, taxonomic quality, time since the surveys and geographical (and environmental) distance to localities with information. These indicators form the basis of ignorance maps, which can be used to visualize the reliability of SDM projections in geographical space, to estimate the uncertainty of these predictions and to identify target survey areas. To demonstrate the application of our approach, we use data on fourteen Iberian species of Scarabaeidae dung beetles. Data-driven uncertainty is widespread even for this well-surveyed group; more than 60% of the region has distributional uncertainty values higher than 0.6, and 30% higher than 0.7. Ignorance maps can be jointly evaluated with SDM predictions to generate spatially explicit maps of uncertainty, identifying where predictions are reli-able/unreliable. Neglecting such uncertainty can severely affect SDM effectiveness, as it can introduce biases and inaccuracies into the measured species–environment rela-tionships. These errors could result in incorrect theoretical or practical applications, including ill-advised conservation actions. We therefore advocate for the routine use of ignorance maps or similar techniques as supporting information in SDM applications. ; This work has been funded by the Brazilian CNPq PVE grant 314523/2014-6 and the Brazilian National Inst. for Science and Technology in Ecology, Evolution and Biodiversity Conservation (INCT-EECBio), supported by MCTIC/CNPq (465610/2014-5) and the Fundação de Amparo à Pesquisa do Estado de Goiás (201810267000023). GT was supported by CAPES REUNI doctorate fellowship and PDSE grant no. 11842121. RJL is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 854248. ; Peer reviewed