Automated location invariant animal detection in camera trap images using publicly available data sources

1. A time‐consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.
2. We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain‐specific applications.
3. We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image‐sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out‐of‐sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.
4. In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%–88.59%) than that achieved by the models trained only on camera trap datasets (38.5%–66.74%). Infusion further improved mAP by 1.78%–32.08%.
5. Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out‐of‐the‐box software. Models can be further optimized by infusion of 5%–10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

     

Identifying modules of cooperating cancer drivers

Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2–6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co‐occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well‐studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS‐mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.     

Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale

1. Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.
2. Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.
3. The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.
4. The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.

     

Rewilding with invertebrates and microbes to restore ecosystems: Present trends and future directions

1. Restoration ecology has historically focused on reconstructing communities of highly visible taxa while less visible taxa, such as invertebrates and microbes, are ignored. This is problematic as invertebrates and microbes make up the vast bulk of biodiversity and drive many key ecosystem processes, yet they are rarely actively reintroduced following restoration, potentially limiting ecosystem function and biodiversity in these areas.
2. In this review, we discuss the current (limited) incorporation of invertebrates and microbes in restoration and rewilding projects. We argue that these groups should be actively rewilded during restoration to improve biodiversity, ecosystem function outcomes, and highlight how they can be used to greater effect in the future. For example, invertebrates and microbes are easily manipulated, meaning whole communities can potentially be rewilded through habitat transplants in a practice that we refer to as “whole‐of‐community” rewilding.
3. We provide a framework for whole‐of‐community rewilding and describe empirical case studies as practical applications of this under‐researched restoration tool that land managers can use to improve restoration outcomes.
4. We hope this new perspective on whole‐of‐community restoration will promote applied research into restoration that incorporates all biota, irrespective of size, while also enabling a better understanding of fundamental ecological theory, such as colonization and competition trade‐offs. This may be a necessary consideration as invertebrates that are important in providing ecosystem services are declining globally; targeting invertebrate communities during restoration may be crucial in stemming this decline.

     

DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes'' activity scores as input and report subnetworks that show significant over‐representation of accrued activity signal (“active modules”), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation‐based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir‐Lab.     

PhenoSpace: A Shiny application to visualize trait data in the phenotypic space of the global spectrum of plant form and function

1. A recent analysis of variation in six major traits conducted on a large worldwide sample of vascular plant species showed that three‐quarters of trait variation was captured by a two‐dimensional global spectrum of plant form and function (“global spectrum” hereafter). We developed the PhenoSpace application, whose aim is to visualize and export the position of any individual/population/species in the phenotypic space of the global spectrum.
2. PhenoSpace is a Shiny application that helps users to manipulate and visualize data pertaining to the global spectrum of plant form and function. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/PhenoSpace/.
3. PhenoSpace has three main functionalities. First, it allows users to visualize the phenotypic space of the global spectrum using different combinations of traits and growth forms. Second, trait data from any new user‐defined dataset can be projected onto the phenotypic space of the global spectrum, provided that at least two of the six traits are available. Finally, figures produced and loadings of the imported data on the PCA axes can be downloaded, allowing users to conduct further analyses.
4. PhenoSpace fulfills the practical goal of positioning plants in the phenotypic space of the global spectrum, making it possible to compare trait variation at any level of organization against the worldwide background. This serves a major aim of comparative plant ecology, which is to put specific sets of individuals, populations or species into a broader context, facilitating comparison and synthesis of results across different continents and environments using relevant indicators of plant design and function.

     

De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures

The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta''s FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5′ UTR; the reverse complement of the 5′ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3′ UTR. For eleven of these elements (the stems in SL1–8, reverse complement of SL1–4, FSE, s2m and 3′ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.     

Reply to Bronstein and Vinogradov

The response by the author. Subject Categories: S&S: Economics & Business, S&S: Ethics

I thank Michael Bronstein and Sophia Vinogradov for their interest and comments. I would like to respond to a few of their points.First, I agree with the authors that empirical studies should be conducted to validate any approaches to prevent the spread of misinformation before their implementation. Nonetheless, I think that the ideas I have proposed may be worth further discussion and inspire empirical studies to test their effectiveness.Second, the authors warn that informing about the imperfections of scientific research may undermine trust in science and scientists, which could result in higher vulnerability to online health misinformation (Roozenbeek et al, 2020; Bronstein & Vinogradov, 2021). I believe that transparency about limitations and problems in research does not necessarily have to diminish trust in science and scientists. On the contrary, as Veit et al put it, “such honesty… is a prerequisite for maintaining a trusting relationship between medical institutions (and practitioners) and the public” (Veit et al, 2021). Importantly, to give an honest picture of scientific research, information about its limitations should be put in adequate context. In particular, the public also should be aware that “good science” is being done by many researchers; we do have solid evidence of effectiveness of many medical interventions; and efforts are being taken to address the problems related to quality of research.Third, Bronstein and Vinogradov suggest that false and dangerous information should be censored. I agree with the authors that “[c]ensorship can prevent individuals from being exposed to false and potentially dangerous ideas” (Bronstein & Vinogradov, 2021). I also recognize that some information is false beyond any doubt and its spread may be harmful. What I am concerned about are, among others, the challenges related to defining what is dangerous and false information and limiting censorship only to this kind of information. For example, on what sources should decisions to censor be based and who should make such decisions? Anyone, whether an individual or an organization, with a responsibility to censor information will likely not only be prone to mistakes, but also to abuses of power to foster their interests. Do the benefits we want to achieve by censorship outweigh the potential risks?Fourth, we need rigorous empirical studies examining the actual impact of medical misinformation. What exactly are the harms we try to protect against and what is their scale? This information is necessary to choose proportionte and effective measures to reduce the harms. Bronstein and Vinogradov give an example of a harm which may be caused by misinformation—an increase in methanol poisoning in Iran. Yet, as noticed by the authors, misinformation is not the sole factor in this case; there are also cultural and other contexts (Arasteh et al, 2020; Bronstein & Vinogradov, 2021). Importantly, the methods of studies exploring the effects of misinformation should be carefully elaborated, especially when study participants are asked to self‐report. A recent study suggests that some claims about the prevalence of dangerous behaviors, such as drinking bleach, which may have been caused by misinformation are largely exaggerated due to the presence of problematic respondents in surveys (preprint: Litman et al, 2021).Last but not least, I would like to call attention to the importance of how veracity of information is determined in empirical studies on misinformation. For example, in a study of Roozenbeek et al, cited by Bronstein and Vinogradov, the World Health Organization (WHO) was used as reliable source of information, which raises questions. For instance, Roozenbeek et al (2020) used a statement “the coronavirus was bioengineered in a military lab in Wuhan” as an example of false information, relying on the judgment of the WHO found on its “mythbusters” website (Roozenbeek et al, 2020). Yet, is there a solid evidence to claim that this statement is false? At present, at least some scientists declare that we cannot rule out that the virus was genetically manipulated in a laboratory (Relman, 2020; Segreto & Deigin, 2020). Interestingly, the WHO also no longer excludes such a possibility and has launched an investigation on this issue (https://www.who.int/health‐topics/coronavirus/origins‐of‐the‐virus, https://www.who.int/emergencies/diseases/novel‐coronavirus‐2019/media‐resources/science‐in‐5/episode‐21‐‐‐covid‐19‐‐‐origins‐of‐the‐sars‐cov‐2‐virus); the information about the laboratory origin of the virus being false is no longer present on the WHO “mythbusters” website (https://www.who.int/emergencies/diseases/novel‐coronavirus‐2019/advice‐for‐public/myth‐busters). Against this backdrop, some results of the study by Roozenbeek et al (2020) seem misleading. In particular, the perception of the reliability of the statement about bioengineered virus by study participants in Roozenbeek et al (2020) does not reflect the susceptibility to misinformation, as intended by the researchers, but rather how the respondents perceive reliability of uncertain information.I hope that discussion and research on these and related issues will continue.     

Confidence intervals by constrained optimization—An algorithm and software package for practical identifiability analysis in systems biology

Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models’ predictability: how accurately can the models’ parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters’ confidence intervals. Development of methods, which can accurately produce parameters’ confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling.We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article.The CICO algorithm is implemented in a software package freely available in Julia (https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py).     

Biodiversity 2030: a road paved with good intentions: The new EU Commission's biodiversity Strategy risks to remain an empty husk without proper implementation

The EU''s Biodiversity Strategy for 2030 makes great promises about halting the decline of biodiversity but it offers little in terms of implementation. Subject Categories: S&S: Economics & Business, Ecology, S&S: Ethics

Earth is teeming with a stunning variety of life forms. Despite hundreds of years of exploration and taxonomic research, and with 1.2 million species classified, we still have no clear picture of the real extent of global biodiversity, with estimates ranging from 3 to 100 million species. A highly quoted—although not universally accepted—study predicted some 8.7 million species, of which about 2.2 million are marine (Mora et al, 2011). Although nearly any niche on the surface of Earth has been colonized by life, species richness is all but evenly distributed. A large share of the known species is concentrated in relatively small areas, especially in the tropics (Fig 1). Ultimately, it is the network of the interactions among life forms and the physical environment that make up the global ecosystem we call biosphere and that supports life itself.Open in a separate windowFigure 1Biological hotspots of the worldA total of 36 currently recognized hotspots make up < 3% of the planet''s land area but harbor half of the world''s endemic plant species and 42% of all terrestrial vertebrates. Overall, hotspots have lost more than 80% of their original extension. Credit: Richard J. Weller, Claire Hoch, and Chieh Huang, 2017, Atlas for the End of the World, http://atlas‐for‐the‐end‐of‐the‐world.com/. Reproduced with permission.Driven by a range of complex and interwoven causes–such as changes in land and sea use, habitat destruction, overexploitation of organisms, climate change, pollution, and invasive species–biodiversity is declining at an alarming pace. A report by the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) issued a clear warning: “An average of around 25 per cent of species in assessed animal and plant groups are threatened, suggesting that around 1 million species already face extinction, many within decades, unless action is taken to reduce the intensity of drivers of biodiversity loss. Without such action, there will be a further acceleration in the global rate of species extinction, which is already at least tens to hundreds of times higher than it has averaged over the past 10 million years” (IPBES, 2019) (Fig 2). Although focused on a smaller set of organisms, a more recent assessment by WWF has reached similar conclusions. Their Living Planet Index, that tracks the abundance of thousands of populations of mammals, birds, fish, reptiles, and amphibians around the world, shows a stark decline in monitored populations (WWF, 2020). As expected, the trend of biodiversity decline is not homogeneous with tropical areas paying a disproportionately high price, mostly because of unrestrained deforestation and exploitation of natural resources.Open in a separate windowFigure 2The global, rapid decline of biodiversity(A) Percentage of species threatened with extinction in taxonomic groups that have been assessed comprehensively, or through a “sampled” approach, or for which selected subsets have been assessed by the IUCN Red List of Threatened Species. Groups are ordered according to the best estimate, assuming that data‐deficient species are as threatened as non‐data deficient species. (B) Extinctions since 1500 for vertebrate groups. (C) Red List Index of species survival for taxonomic groups that have been assessed for the IUCN Red List at least twice. A value of 1 is equivalent to all species being categorized as Least Concern; a value of zero is equivalent to all species being classified as Extinct. Data for all panels from www.iucnredlist.org. Reproduced from (IPBES, 2019), with permission.

Driven by a range of complex and interwoven causes […] biodiversity is declining at an alarming pace.

Against this dire background, the EU has drafted a Biodiversity Strategy 2030, an ambitious framework aimed to tackling the key reasons behind biodiversity loss. The plan hinges around a few main elements, such as the establishment of protected areas for at least 30% of Europe''s lands and seas (Fig 3); a significant increase of biodiversity‐rich landscape features on agricultural land by establishing buffer zones like hedges and fallow fields; halting and reversing the decline of pollinators; and planting 3 billion trees by 2030 (https://ec.europa.eu/info/strategy/priorities‐2019‐2024/european‐green‐deal/actions‐being‐taken‐eu/eu‐biodiversity‐strategy‐2030_en). The budget for implementing these measures was set at €20 billion per year.Open in a separate windowFigure 3Natura 2000, the EU''s network of protected areasIn 2019, 18% of land in the EU was protected as Natura 2000, with the lowest share of protected land in Denmark (8%) and the highest in Slovenia (38%). In 2019, the largest national network of terrestrial Natura 2000 sites was located in Spain, covering 138,111 km², followed by France (70,875 km²) and Poland (61,168 km²). Reproduced from Eurostat: https://ec.europa.eu/eurostat/statistics‐explained/index.php?title=Main_Page “Nature is vital for our physical and mental wellbeing, it filters our air and water, it regulates the climate and it pollinates our crops. But we are acting as if it didn''t matter, and losing it at an unprecedented rate”, said Virginijus Sinkevičius, Commissioner for the Environment, Oceans and Fisheries, at the press launch of the new EU action (https://ec.europa.eu/commission/presscorner/detail/en/ip_20_884). “This new Biodiversity Strategy builds on what has worked in the past, and adds new tools that will set us on a path to true sustainability, with benefits for all. The EU''s aim is to protect and restore nature, to contribute to economic recovery from the current crisis, and to lead the way for an ambitious global framework to protect biodiversity around the planet”.Environmental groups and other stakeholders have welcomed the EU''s pledge in principle. “This is a unique opportunity to shape a new society in harmony with nature”, applauded Wetlands International. “We must not forget that the biodiversity and climate crisis is a much bigger and persistent challenge for humanity than COVID‐19”, (https://europe.wetlands.org/news/welcoming‐the‐eu‐biodiversity‐strategy‐for‐2030/). EuroNatur, a foundation focused on conservation, stated that the goals set out by the new strategy provide a strong basis for improving the state of nature in the EU (www.euronatur.org).Alongside the voices of praise, however, many have expressed concerns that the strategy could turn into a little more than a wish list. “The big issue of the strategy is that while setting a goal for financial funds, the EU does not specify where the money is supposed to come from. It only says it should include ‘EU funds and national and private funding’”, commented the European Wilderness Society, an environmental advocacy non‐profit organization headquartered in Tamsweg, Austria. “Goals are important, but do not create change without an organized and sustainable implementation. It''s a good and ambitious document, but what is also obvious is the lack of strategy of how to implement it, and a lack of discussion of why previous documents of this type failed” (https://wilderness‐society.org/ambitious‐eu‐biodiversity‐strategy‐2030/).

Alongside the voices of praise, however, many have expressed concerns that the strategy could turn into a little more than a wish list.

The Institute for European Environmental Policy (IEEP) is on the same page. The sustainability think‐tank based in Brussels and London noted that the outgoing EU 2020 biodiversity strategy showed major implementation problems, especially because of lack of engagement at national level and of ad hoc legislation supporting the meeting of key targets. Therefore, “[it] can be argued that a legally binding approach to the biodiversity governance framework is urgently needed unless Member States and other key stakeholders can show greater intrinsic ownership to deliver on agreed objectives”, (https://ieep.eu/news/first‐impressions‐of‐the‐eu‐biodiversity‐strategy‐to‐2030). In addition, IEEP remarked that money is an issue, since the €20 billion figure appears more as an estimate than a certified obligation.“The intentions of the Commission are good and the strategy contains a number of measures and targets that can really make a difference. However, implementation depends critically on the member states and experiences with the Common Agricultural Policy the past decade or so have taught us that many of them are more interested in short‐term economic objectives than in safeguarding the natural wealth of their country for future generations”, commented David Kleijn, an ecologist and nature conservation expert at the Wageningen University, the Netherlands. “I think it is important that we now have an ambitious Biodiversity Strategy but at the same time I have little hope that we will be able to achieve its objectives”.

I think it is important that we now have an ambitious Biodiversity Strategy but at the same time I have little hope that we will be able to achieve its objectives.

There is further criticism against specific measures, such as the proposal of planting 3 billion trees. “To have lots of trees planted in an area does not necessarily translate into an increase of biodiversity. Biodiverse ecosystems are the result of million years of complex multi‐species interactions and evolutionary processes, which are not as easy to restore”, explained plant ecologist Susana Gómez‐González, from the University of Cádiz, Spain. Planting a large number of trees is a too simplistic approach for saving European forests from the combined effects of excessive anthropic pressure and climate change, and could even have detrimental effects (see Box 1). More emphasis should be placed instead in reducing tree harvesting in sensitive areas and in promoting natural forest renewal processes (Gómez‐González et al, 2020). “For a biodiversity strategy, increasing the number of trees, or even increasing the forest area, should not be an objective; priority should be given to the conservation and restoration of natural ecosystems, forests and non‐forests”, Gómez‐González said.In other cases, it could be difficult, if not impossible, to reach some of the goals because of lack of information. For example, one of the roadmap''s targets is to restore at least 25,000 km of Europe''s rivers back to free‐flowing state. However, the number of barriers dispersed along European rivers will probably prevent even getting close to the mark. An international research team has collected detailed information on existing instream barriers for 147 rivers in 36 European countries, coming up with the impressive figure of over 1.2 million obstacles that inevitably impact on river ecosystems, affecting the transport and dispersion of aquatic organisms, nutrients, and sediments (Belletti et al, 2020). Existing inventories mainly focused on dams and other large barriers, while, in fact, a large number of artificial structures are much smaller, such like weirs, locks, ramps, and fords. As a result, river fragmentation has been largely underestimated, and the models used to plan flow restoration might be seriously flawed. “To avoid ‘death by a thousand cuts’, a paradigm shift is necessary: to recognize that although large dams may draw most of the attention, it is the small barriers that collectively do most of the damage. Small is not beautiful”, concluded the authors (Belletti et al, 2020).

Box 1: Why many trees don''t (always) make a forestForests are cathedrals of biodiversity. They host by far the largest number of species on land, which provide food and essential resources for hundreds of millions of people worldwide. However, forests are disappearing and degrading at an alarming pace. The loss of these crucial ecosystems has given new impulses to a variety of projects aimed at stopping this devastation and possibly reversing the trend.Once it is gone, can you rebuild a forest? Many believe the answer is yes, and the obvious solution is to plant trees. Several countries have thus launched massive tree‐planting programs, notably India and Ethiopia, where 350 million trees have been planted in single day (https://www.unenvironment.org/news‐and‐stories/story/ethiopia‐plants‐over‐350‐million‐trees‐day‐setting‐new‐world‐record). The World Economic Forum has set up its own One Trillion Tree initiative (https://www.1t.org/) “to conserve, restore, and grow one trillion trees by 2030”. Launched in January last year at Davos, 1t.org was conceived as a platform for governments, companies and NGOs/civil society groups to support the UN Decade on Ecosystem Restoration (2021–2030). The initiative has been christened by renowned naturalist Jane Goodall, who commented: “1t.org offers innovative technologies which will serve to connect tens of thousands of small and large groups around the world that are engaged in tree planting and forest restoration”, (https://www.weforum.org/agenda/2020/01/one‐trillion‐trees‐world‐economic‐forum‐launches‐plan‐to‐help‐nature‐and‐the‐climate/).However, things are way more complicated than they appear: large‐scale tree planting schemes are rarely a viable solution and can even be harmful. “[A] large body of literature shows that even the best planned restoration projects rarely fully recover the biodiversity of intact forests, owing to a lack of sources of forest‐dependent flora and fauna in deforested landscapes, as well as degraded abiotic conditions resulting from anthropogenic activities”, commented Karen Holl from the University of Caliornia, Santa Cruz, and Pedro Brancalion from the University of São Paulo (Holl & Brancalion, 2020). A common problem of tree plantations, for example, is the low survival rate of seedlings, mostly because the wrong tree species are selected and due to poor maintenance after planting. Moreover, grasslands and savannas, which are often targeted for establishing new forests, are themselves treasure troves of biodiversity. Ending indiscriminate deforestation, improving the protection of existing forests, and promoting their restoration would therefore be a more efficient strategy to preserve biodiversity in the shorter term. If tree planting is indeed necessary, it should be well planned by selecting the right areas for reforestation, using suitable tree species that can maximize biodiversity, and involving local populations to maintain the plantations, Holl and Brancalion argue (Holl & Brancalion, 2020).

…even the best planned restoration projects rarely fully recover the biodiversity of intact forests, owing to a lack of sources of forest‐dependent flora and fauna in deforested landscapes…

The health of soil, where a high proportion of biodiversity is hosted, is another problem the new strategy should address in a more focused manner. “In my opinion, the EU Biodiversity Strategy is already a leap forward in terms of policy interest in soils in general and in soil biodiversity in particular. Compared with other nations/regions of the world, Europe is by far in the forefront regarding this issue”, commented Carlos António Guerra at the German Centre for Integrative Biodiversity Research (iDiv) in Leipzig, Germany, and Co‐leader of the Global Soil Biodiversity Observation Network (https://geobon.org/bons/thematic‐bon/soil‐bon/). “Nevertheless, the connection between soil biodiversity and ecological functions needs further commitments. Soils allow for horizontal integration of several policy agendas, from climate to agriculture and, very importantly, nature conservation. This is not explicit in the EU Biodiversity Strategy in regard to soils”. It remains to be seen if EU restoration plan will emphasize soil biodiversity, or consider it as a mere side effect of other initiatives, Guerra added. “A soil nature conservation plan should be proposed”, he said. “Only such a plan, that implies that current and future protected areas have to consider, describe and protect their soil biodiversity would make a significant push to help protect such a valuable resource”.More generally, research shows that the current paradigm of protection must be shifted to prevent further losses to biodiversity. In fact, an analysis of LIFE projects—a cornerstone of EU nature protection—found that conservation efforts are extremely polarized and strongly taxonomically biased (Mammola et al, 2020). From 1992 to 2018, investment in vertebrates was sixfold higher than that for invertebrates, with birds and mammals alone accounting for 72% of the targeted species and 75% of the total budget. In relative terms, investment per species for vertebrates has been 468 times higher than for invertebrates (Fig 4). There is no sound scientific reasoning behind this uneven conservation attention, but just popularity. “[T]he species covered by a greater number of LIFE projects were also those which attracted the most interest online, suggesting that conservation in the EU is largely driven by species charisma, rather than objective features”, the researchers wrote (Mammola et al, 2020).Open in a separate windowFigure 4Taxonomic bias in EU fauna protection effortsBreakdown of the number of projects (A) and budget allocation (B) across main animal groups covered by the LIFE projects (n = 835). (C) The most covered 30 species of vertebrates (out of 410) and invertebrates (out of 78) in the LIFE projects analyzed (n = 835). The vertical bar represents monetary investment and the blue scatter line the number of LIFE projects devoted to each species. Reproduced from (Mammola et al, 2020), with permission.     

The Data Governance Act and the EU's move towards facilitating data sharing

The implementation of the EU General Data Protection Regulation (GDPR) has had significant impacts on biomedical research, often complicating data sharing among researchers. The recently announced proposal for a new EU Data Governance Act is a promising step towards facilitating data sharing, if it can interplay well with the GDPR.Subject Categories: S&S: Ethics

The EU General Data Protection Regulation (GDPR) has affected biomedical research, often complicating data sharing. The recently announced proposal for a new EU Data Governance Act, is a promising step towards facilitating data sharing.

In an attempt to improve and increase data sharing in the EU and to optimize the re‐use of personal and non‐personal data, the European Commission has recently announced the proposal for a new EU Data Governance Act (https://ec.europa.eu/digital‐single‐market/en/news/proposal‐regulation‐european‐data‐governance‐data‐governance‐act). If approved, it will enable the creation and regulation of “secure spaces” where various types of data, including health data, can be shared and re‐used for both commercial and altruistic purposes, including scientific research. The Data Governance Act, within the framework of a European Strategy for Data, (https://ec.europa.eu/info/sites/info/files/communication‐european‐strategy‐dat‐19feb2020_en.pdf), would address some of the shortcomings and drawbacks of the current regulatory framework which holds back sharing and re‐using data for biomedical research purposes.While the proposed Act would apply to all types of personal and non‐personal data, the increasing demand for sharing health data has most likely been a major rationale for this new legislation of data governance. Notably, sharing health and genetic data for scientific research entails an extra layer of complexity, owing to concerns over data protection and privacy when sharing sensitive personal data. Vice versa, there are also concerns in the scientific community over the negative impact of regulatory restrictions on sharing health data in data‐driven biomedical research. The pressing question here is how far the EU’s proposed legislative and policy framework can offset either concerns?     

Linking behavioral states to landscape features for improved conservation management

1. A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat‐specific behaviors.
2. Here we use behavioral change point analysis to identify behavioral states of golden eagles (Aquila chrysaetos) in the Sonoran and Mojave Deserts of the southwestern United States, and we identify, for each behavioral state, conservation‐relevant habitat associations.
3. We modeled behavior using 186,859 GPS points from 48 eagles and identified 2,851 distinct segments comprising four behavioral states. Altitude above ground level (AGL) best differentiated behavioral states, with two clusters of short‐distance movement behaviors characterized by low AGL (state 1 AGL = 14 m (median); state 2 AGL = 11 m) and two associated with longer‐distance movement behaviors and characterized by higher AGL (state 3 AGL = 108 m; state 4 AGL = 450 m).
4. Behaviors such as perching and low‐altitude hunting were associated with short‐distance movements in updraft‐poor environments, at higher elevations, and over steeper and more north‐facing terrain. In contrast, medium‐distance movements such as hunting and transiting were over gentle and south‐facing slopes. Long‐distance transiting occurred over the desert habitats that generate the best updraft.
5. This information can guide management of this species, and our approach provides a template for behavior‐specific habitat associations for other species of management concern.

     

Thermal ecology and baseline energetic requirements of a large‐bodied ectotherm suggest resilience to climate change

1. Most studies on how rising temperatures will impact terrestrial ectotherms have focused on single populations or multiple sympatric species. Addressing the thermal and energetic implications of climatic variation on multiple allopatric populations of a species will help us better understand how a species may be impacted by altered climates.
2. We used eight years of thermal and behavioral data collected from four populations of Pacific rattlesnakes (Crotalus oreganus) living in climatically distinct habitat types (inland and coastal) to determine the field‐active and laboratory‐preferred body temperatures, thermoregulatory metrics, and maintenance energetic requirements of snakes from each population.
3. Physical models showed that thermal quality was best at coastal sites, but inland snakes thermoregulated more accurately despite being in more thermally constrained environments. Projected increases of 1 and 2°C in ambient temperature result in an increase in overall thermal quality at both coastal and inland sites.
4. Population differences in modeled standard metabolic rate estimates were driven by body size and not field‐active body temperature, with inland snakes requiring 1.6× more food annually than coastal snakes.
5. All snakes thermoregulated with high accuracy, suggesting that small increases in ambient temperature are unlikely to impact the maintenance energetic requirements of individual snakes and that some species of large‐bodied reptiles may be robust to modest thermal perturbations under conservative climate change predictions.

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Underwater photogrammetry for close‐range 3D imaging of dry‐sensitive objects: The case study of cephalopod beaks

• Technical advances in 3D imaging have contributed to quantifying and understanding biological variability and complexity. However, small, dry‐sensitive objects are not easy to reconstruct using common and easily available techniques such as photogrammetry, surface scanning, or micro‐CT scanning. Here, we use cephalopod beaks as an example as their size, thickness, transparency, and dry‐sensitive nature make them particularly challenging. We developed a new, underwater, photogrammetry protocol in order to add these types of biological structures to the panel of photogrammetric possibilities.
• We used a camera with a macrophotography mode in a waterproof housing fixed in a tank with clear water. The beak was painted and fixed on a colored rotating support. Three angles of view, two acquisitions, and around 300 pictures per specimen were taken in order to reconstruct a full 3D model. These models were compared with others obtained with micro‐CT scanning to verify their accuracy.
• The models can be obtained quickly and cheaply compared with micro‐CT scanning and have sufficient precision for quantitative interspecific morphological analyses. Our work shows that underwater photogrammetry is a fast, noninvasive, efficient, and accurate way to reconstruct 3D models of dry‐sensitive objects while conserving their shape. While the reconstruction of the shape is accurate, some internal parts cannot be reconstructed with photogrammetry as they are not visible. In contrast, these structures are visible using reconstructions based on micro‐CT scanning. The mean difference between both methods is very small (10⁻⁵ to 10⁻⁴ mm) and is significantly lower than differences between meshes of different individuals.
• This photogrammetry protocol is portable, easy‐to‐use, fast, and reproducible. Micro‐CT scanning, in contrast, is time‐consuming, expensive, and nonportable. This protocol can be applied to reconstruct the 3D shape of many other dry‐sensitive objects such as shells of shellfish, cartilage, plants, and other chitinous materials.

     

Microtubule poleward flux in human cells is driven by the coordinated action of four kinesins

Mitotic spindle microtubules (MTs) undergo continuous poleward flux, whose driving force and function in humans remain unclear. Here, we combined loss‐of‐function screenings with analysis of MT‐dynamics in human cells to investigate the molecular mechanisms underlying MT‐flux. We report that kinesin‐7/CENP‐E at kinetochores (KTs) is the predominant driver of MT‐flux in early prometaphase, while kinesin‐4/KIF4A on chromosome arms facilitates MT‐flux during late prometaphase and metaphase. Both these activities work in coordination with kinesin‐5/EG5 and kinesin‐12/KIF15, and our data suggest that the MT‐flux driving force is transmitted from non‐KT‐MTs to KT‐MTs by the MT couplers HSET and NuMA. Additionally, we found that the MT‐flux rate correlates with spindle length, and this correlation depends on the establishment of stable end‐on KT‐MT attachments. Strikingly, we find that MT‐flux is required to regulate spindle length by counteracting kinesin 13/MCAK‐dependent MT‐depolymerization. Thus, our study unveils the long‐sought mechanism of MT‐flux in human cells as relying on the coordinated action of four kinesins to compensate for MT‐depolymerization and regulate spindle length.     

DNA G-quadruplexes for native mass spectrometry in potassium: a database of validated structures in electrospray-compatible conditions

G-quadruplex DNA structures have become attractive drug targets, and native mass spectrometry can provide detailed characterization of drug binding stoichiometry and affinity, potentially at high throughput. However, the G-quadruplex DNA polymorphism poses problems for interpreting ligand screening assays. In order to establish standardized MS-based screening assays, we studied 28 sequences with documented NMR structures in (usually ∼100 mM) potassium, and report here their circular dichroism (CD), melting temperature (T_m), NMR spectra and electrospray mass spectra in 1 mM KCl/100 mM trimethylammonium acetate. Based on these results, we make a short-list of sequences that adopt the same structure in the MS assay as reported by NMR, and provide recommendations on using them for MS-based assays. We also built an R-based open-source application to build and consult a database, wherein further sequences can be incorporated in the future. The application handles automatically most of the data processing, and allows generating custom figures and reports. The database is included in the g4dbr package (https://github.com/EricLarG4/g4dbr) and can be explored online (https://ericlarg4.github.io/G4_database.html).     

The normalized segment classification model: A new tool to compare spectral reflectance curves

1. Color patterns are complex traits under selective pressures from conspecifics, mutualists, and antagonists. To evaluate the salience of a pattern or the similarity between colors, several visual models are available. Color discrimination models estimate the perceptual difference between any two colors. Their application to a diversity of taxonomic groups has become common in the literature to answer behavioral, ecological, and evolutionary questions. To use these models, we need information about the visual system of our beholder species. However, many color patterns are simultaneously subject to selective pressures from different species, often from different taxonomic groups, with different visual systems. Furthermore, we lack information about the visual system of many species, leading ecologists to use surrogate values or theoretical estimates for model parameters.
2. Here, we present a modification of the segment classification method proposed by Endler (Biological Journal of the Linnean Society, 1990 41, 315–352): the normalized segment classification model (NSC). We explain its logic and use, exploring how NSC differs from other visual models. We also compare its predictions with available experimental data.
3. Even though the NSC model includes no information about the visual system of the receiver species, it performed better than traditional color discrimination models when predicting the output of some behavioral tasks. Although vision scientists define color as independent of stimulus brightness, a likely explanation for the goodness of fit of the NSC model is that its distance measure depends on brightness differences, and achromatic information can influence the decision‐making process of animals when chromatic information is missing.
4. Species‐specific models may be insufficient for the study of color patterns in a community context. The NSC model offers a species‐independent solution for color analyses, allowing us to calculate color differences when we ignore the intended viewer of a signal or when different species impose selective pressures on the signal.