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Tumours are nowadays the second world-leading cause of death after cardiovascular diseases. During the last decades of cancer research, lifestyle and random/genetic factors have been blamed for cancer mortality, with obesity, seden-tary habits, alcoholism, and smoking contributing as supposed major causes. However, there is an emerging consensus that environmental pollution should be considered one of the main triggers. Unfortunately, all this preliminary scien-tific evidence has not always been followed by governments and institutions, which still fail to pursue research on can-cer's environmental connections. In this unprecedented national-scale detailed study, we analyzed the links between cancer mortality, socio-economic factors, and sources of environmental pollution in Italy, both at wider regional and finer provincial scales, with an artificial intelligence approach. Overall, we found that cancer mortality does not have a random or spatial distribution and exceeds the national average mainly when environmental pollution is also higher, despite healthier lifestyle habits. Our machine learning analysis of 35 environmental sources of pollution showed that air quality ranks first for importance concerning the average cancer mortality rate, followed by sites to be reclaimed, urban areas, and motor vehicle density. Moreover, other environmental sources of pollution proved to be relevant for the mortality of some specific cancer types. Given these alarming results, we call for a rearrangement of the priority of cancer research and care that sees the reduction and prevention of environmental contamination as a priority action to put in place in the tough struggle against cancer.

Geographical barriers, distance, and environmental gradients may determine variation in species composition of bat assemblages in tropical regions. For example, bats assume a wide variety of foraging strategies, dietary traits, different flight abilities, and New World leaf-nosed bats (Phyllostomidae) are among the most functionally diverse family of bats. Nonetheless, this variation remains uncharacterized in Honduras. Our study investigated the relationship of environmental and climatic variables that influence leaf-nosed bat assemblages in terms of alpha and beta taxonomic and functional diversity. From February 2015 to May 2019, we sampled 56 sites and captured 1086 phyllostomid bats. We found that taxonomic alpha diversity decreased with elevation, and that functional alpha diversity decreased with elevation and humidity. Changes in taxonomic β-diversity were associated with elevation and changes in functional β-diversity were associated with humidity, elevation, and precipitation. Our findings suggest that elevation is the key environmental variable influencing New World leaf-nosed bat assemblages in Honduras, and even though species composition varies among different habitats, the functional composition of assemblages remains similar.

Although there are recent claims of a lack of evidence of self-consciousness in many tested species, the ability to recognize oneself in a mirror, which seems an exceedingly rare capacity in the animal kingdom, may not be the only way to check for animal self-awareness (i.e. the capacity to become the object of your own attention). A new testing approach, based on a different sensory modality (such as the sniff-test for self-recognition, STSR), recently proved to be effective with dogs. We applied this sniff test to a group of four captive grey wolves, living in male-female couples in two different enclosures at the Wolf Park in Indiana, USA. In this preliminary study, wolves showed some signs of the ability to recognize themselves through the “olfactory mirror” and exhibited some clues of mark-directed responses, particularly scent-rolling, which may shed more light on this still unclear behavior and represent a sort of olfactory equivalent to passing the original mirror test.

This pandemic situation requests a correct understanding of our impacts on wildlife conservation, which would also provide benefits for our species. In this commentary we revised and discussed some of the repercussions that SARS-CoV-2 pandemic may have to wildlife. We propose four actions that should be taken into account to protect and conserve wildlife in this pandemic era: wildlife "wet" markets must close; human interference with wildlife must be reduced; bats and pangolins must be conserved and not blamed; and Chinese traditional medicine must be more controlled.

The origin of economic niches, conceived as potential markets, has been mostly neglected in economic theory. Ecological niches emerge as new species evolve and fit into a web of interactions, and the more species come into existence, the more (exponentially or power-law distributed) ecological niches emerge. In parallel fashion, economic niches emerge with new goods, and niche formation in economics is also exponentially or power-law distributed. In economics and ecology alike, autocatalytic processes drive the system to greater and greater diversity. Novelty begets novelty in a positive feedback loop. An autocatalytic set of self-enabling transactions feed back upon one another in combinatoric fashion to generate progressive diversity. While these combinatorial dynamics cannot be prestated, the model explains the “hockeystick of economic growth”—a pattern of prolonged stasis followed by a sudden takeoff, such as occurred during the Industrial Revolution or the Cambrian explosion in ecology. Several implications derive from our niche emergence model, including the idea that the evolutionary process of technological change is not something we do; rather, it happens to us.

A national-scale study in Italy showed an incidence of cancer higher in the territories indicated as highly polluted compared to the regional average. One of them, the city of Taranto in Apulia (Italy), which is considered one of the most polluted cities in Europe, has numerous industrial activities that impact population health. We studied the epidemiological effects of a high level of pollution produced by the industrial area of Taranto in increasing the mortality rate for some specific cancer types in the city and towns of the two provinces located downwind. We analysed 10-year mortality rates for 15 major types of tumours reported among the residents of Taranto, of eight surrounding towns, randomly placed within an imaginary cone in the main wind direction from the vertex of the industrial zone of Taranto, and of the two related provinces (Bari and Taranto’s metropolitan areas). Our results confirm our hypothesis that the mortality rate for some specific types of cancer is higher than the norm in the municipality of Taranto. We also found strong evidence that, for 12 out of 15 tumour types, mortality decreases with the distance of the other towns from Taranto’s industrial site. However, we have reasons to believe that other local causes may be implicated in the excess of mortality in some specific municipalities besides the potential dispersal of pollutants from the industrial area of Taranto. The proximity to Taranto cannot, in fact, explain all the anomalies detected in some populations. It is likely that other site-specific sources of heavy pollution are playing a role in worsening the death toll of these towns and this must be taken into serious consideration by environmental policymakers and local authorities.

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness., Proceedings of the National Academy of Sciences of the United States of America, 119 (6), ISSN:0027-8424, ISSN:1091-6490

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

The biodiversity–productivity relationship (BPR) constitutes one of the most fundamental yet challenging topics in ecology. Most described BPRs so far are based on relatively small grain sizes (typically smaller than 1ha), and understanding how the BPRs scale up from individual trees to communities of different sizes, a mission critical for biodiversity conservation and forest management, still remains elusive. Based on a unique 54.12-ha large-scale forest observational site in Northeastern China, where we georeferenced and measured approx. 90,000 trees in two consecutive inventories, we studied BPRs across a cascade of spatial scales using geostatistical analysis and bootstrapping. To explore the underlying mechanisms of the scale dependency of BPR, we further investigated the scale gradient in biodiversity and productivity, respectively, and assessed neighbourhood influences on individual trees. Across all the spatial scales, we found a consistent positive concave-down effect of tree species richness on forest productivity. The elasticity of substitution (θ) represented the degree to which species can substitute for each other in contributing to forest productivity. At the community level, the curvature of BPRs in terms of the elasticity of substitution (θ) declined from around 0.50 and converged at around 0.15, as the grain size increased from 0.01 to 1.00ha, all else being equal. Similarly, at the individual tree level, neighbourhood diversity coefficient, a linear proxy of θ, declined from around 0.08 and converged at around 0.03, as neighbourhood size increased from 0.01 to 1.00ha, all else being equal. Synthesis. Our study found a pronounced scale dependency of biodiversity–productivity relationship and quantified, for the first time, how biodiversity–productivity relationship scale up from 0.01 to 1.00ha. Our findings suggest that biodiversity–ecosystem functioning relationship can be generally scale-dependent and, hence, one must factor in this effect in biodiversity conservation and ecological restoration projects. Our findings also indicate that biodiversity–productivity relationship, predominantly determined by biological processes (i.e. complementarity and diminishing marginal productivity) at a small scale, can be increasingly influenced by statistical (i.e. spatial central limit theorem) and other (such as species–area relationship) processes as the scale increases.

As COVID-19 emerged as a phenomenon of the total environment, and despite the intertwined and complex relationships that make humanity an organic part of the Bio- and Geospheres, the majority of our responses to it have been corrective in character, with few or no consideration for unintended consequences which bring about further vulnerability to unanticipated global events. Tackling COVID-19 entails a systemic and precautionary approach to human-nature relations, which we frame as regaining diversity in the Geo-, Bio-, and Anthropospheres. Its implementation requires nothing short of an overhaul in the way we interact with and build knowledge from natural and social environments. Hence, we discuss the urgency of shifting from current to precautionary approaches to COVID-19 and look, through the lens of diversity, at the anticipated benefits in four systems crucially affecting and affected by the pandemic: health, land, knowledge and innovation. Our reflections offer a glimpse of the sort of changes needed, from pursuing planetary health and creating more harmonious forms of land use to providing a multi-level platform for other ways of knowing/understanding and turning innovation into a source of global public goods. These exemplary initiatives introduce and solidify systemic thinking in policymaking and move priorities from reaction-based strategies to precautionary frameworks.

UNSTRUCTURED A national-scale study in Italy showed an incidence of cancer higher in the territories indicated as highly polluted compared to the regional average. One of them, the city of Taranto in Apulia (Italy), which is considered one of the most polluted cities in Europe, has numerous industrial activities that impact population health. We studied the epidemiological effects of a high level of pollution produced by the industrial area of Taranto in increasing the mortality rate for some specific cancer types in the city and towns of the two provinces located downwind. We analysed 10-year mortality rates for 14 major types of tumours reported among the residents of Taranto, of 6 surrounding towns, randomly placed within an imaginary cone in the main wind direction from the vertex of the industrial zone of Taranto. Our results confirm our hypothesis that the mortality rate for some specific types of cancer (namely, Hodgkin and non- Hodgkin lymphomas, leukaemia, liver and bladder tumours) are higher than the norm in the municipality of Taranto and we have evidence that other local causes may be implicated in the excess of mortality besides the potential dispersal of pollutants from the industrial area of Taranto. The proximity to the industrial area of Taranto cannot, therefore, explain alone the anomalies detected in some populations. It is likely that other site-specific sources of heavy pollution are playing a role in worsening the death toll of these towns and this must be taken into serious consideration by environmental policy-makers and local authorities.

Besides our current health concerns due to COVID-19, cancer is a longer-lasting and even more dramatic pandemic that affects almost a third of the human population worldwide. Most of the emphasis on its causes has been posed on genetic predisposition, chance, and wrong lifestyles (mainly, obesity and smoking). Moreover, our medical weapons against cancers have not improved too much during the last century, although research is in progress. Once diagnosed with a malignant tumour, we still rely on surgery, radiotherapy, and chemotherapy. The main problem is that we have focused on fighting a difficult battle instead of preventing it by controlling its triggers. Quite the opposite, our knowledge of the links between environmental pollution and cancer has surged from the 1980s. Carcinogens in water, air, and soil have continued to accumulate disproportionally and grow in number and dose, bringing us to today’s carnage. Here, a synthesis and critical review of the state of the knowledge of the links between cancer and environmental pollution in the three environmental compartments is provided, research gaps are briefly discussed, and some future directions are indicated. New evidence suggests that it is relevant to take into account not only the dose but also the time when we are exposed to carcinogens. The review ends by stressing that more dedication should be put into studying the environmental causes of cancers to prevent and avoid curing them, that the precautionary approach towards environmental pollutants must be much more reactionary, and that there is an urgent need to leave behind the outdated petrochemical-based industry and goods production.

In early studies, northern vegetation response to global warming recognised both increases in biomass/cover and shrinking of species’ distributional ranges. Subsequent field measurements focussed on vegetation cover and biomass increases (“greening”), and more recently decreases (“browning”). However, satellite observations show that more than 50% of arctic vegetation has not changed significantly despite rapid warming. While absence of change in remote sensing data does not necessarily mean no ecological change on the ground, the significant proportion of the Arctic that appears to be stable in the face of considerable climate change points to a greater need to understand Arctic ecosystem stability. In this paper, we performed an extensive review of the available literature to seek balances or imbalances between research focussing on “greening”, “browning” and “stability/no change”. We find that greening studies dominate the literature though two relatively small areas of the Arctic are disproportionately represented for this main change process. Critically, there are too few studies anywhere investigating stability. We highlight the need to understand the mechanisms driving Arctic ecosystem stability, and the potential longer-term consequences of remaining stable in a rapidly changing climate. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13280-021-01607-w.

Although they have received little attention, interspecific feeding associations are a particular predation behavior in which two or more different species temporary feed simultaneously on the same food sources to obtain greater success in predation. These collaborations can be either mutualistic, commensalistic, or parasitic. One of the best-known examples is the seabird–cetacean associations. Prey herding is the foraging tactic most used in these associations and consists of swimming around and under prey balls to trap them in the water column. Here we reported some new evidence of a fish–bird interspecific feeding association between the European seabass, Dicentrarchus labrax (Linnaeus, 1758) and the European shag, Gulosus aristotelis (Linnaeus, 1761). We analyzed this association through the photograph and video documentation provided by “citizen scientists” in the Mediterranean Sea. We collected reliable proofs of this behavior, at least, in three instances from observations made by recreational fishermen in the Kvarner bay (Croatia, North Adriatic Sea). From the photographs and videos made available, we attempted to reconstruct a predation strategy adopted by these associated species to better understand the advantage this provides during hunting. Although it is not yet clear whether this association is an opportunistic behavior of the European seabass or a mutualistic strategy used by both species to improve their catching success, this feeding association seems to increase the chances to defeat prey defense strategies. Notably, some behavioral imitation of hunting strategies may be put in place by seabasses, which often feed in association with dolphins and other birds using similar prey herding strategies.

Although in strictly protected areas no forest management and logging activities should be evident, a preliminary study detected that, even in the 200 areas with the highest protection of Russia, more than 2 Mha of trees have been lost between 2001 and 2018. Nonetheless, a relevant percentage of the actual drivers of tree loss in Russian strictly protected areas was surrounded by uncertainties due to several factors. Here, in an attempt to “clarify the smokescreen of Russian protected areas”, by validating previous remotely sensed data with new high-resolution satellite imagery and aerial images of land-use change, we shed more light on what has happened during the last 20 years. We used the same layer of tree loss from 2001 to 2020 but, instead of intersecting it with the MODIS data that could have been a source of underestimation of burned surfaces, we overlapped it to the layer of tree cover loss by dominant driver. We analysed the main drivers of tree loss in almost 200 strictly protected areas of Russia. We found that although fire is responsible for 75% of the loss in all strictly protected areas, forestry activities still account for 16%, and 9% is due to undefined causes. Therefore, uncontrolled wildfires (including those started before or after logging) and forestry activities are the main causes of 91% of the total tree loss. The combination of wildfires (often started intentionally) and forestry activities (illegally or barely legally put in place) caused a loss of an astonishing 3 million hectares. The fact that ≈10% of Russian tree cover was lost in two decades since 2001 only in strictly protected areas requires high attention by policymakers and important conservation actions to avoid losing other fundamental habitats and species during the next years when climate change and population growth can represent an additional trigger of an already dramatic situation. We call for an urgent response by national and local authorities that should start actively fighting wildfires, arsonists, and loggers even in inhabited remote areas and particularly in those included in strictly protected areas.

While it is well documented that the motor system is more than a mere implementer of motor actions, the possible applications of its cognitive side are still under-exploited, often remaining as poorly organized evidence. Here, we will collect evidence showing the value of action observation treatment (AOT) in the recovery of impaired motor abilities for a vast number of clinical conditions, spanning from traumatological patients to brain injuries and neurodegenerative diseases. Alongside, we will discuss the use of AOT in the maintenance of appropriate motor behavior in subjects at risk for events with dramatic physical consequences, like fall prevention in elderly people or injury prevention in sports. Finally, we will report that AOT can help to tune existing motor competencies in fields requiring precise motor control. We will connect all these diverse dots into the neurophysiological scenario offered by decades of research on the human mirror mechanism, discussing the potentialities for individualization. Empowered by modern technologies, AOT can impact individuals' safety and quality of life across the whole lifespan.

It was New Year's Eve when Chinese authorities alerted WHO that several cases of an unusual pneumonia appeared in Wuhan (Huang et al. 2020). The COVID-19 was still unknown at that time but, in the first months of 2020, its outbreak has infected millions of people, killing (directly or indirectly) hundreds of thousands of them (WHO 2020). The panic related to the pandemic distribution of the virus shut down whole regions (in China, Iran, the United States, etc.) and even entire countries (Italy, Spain, Austria, etc.). Although it is not the worst microscopic killer humanity has ever known (for instance, think about the casualties due to the Black Death and the HIV; Wainberg et al. 2008; Haensch et al. 2010), this coronavirus is already changing our state of mind and impacting on our lifestyle, at a global scale.

This study examined the impact of climate change on plant species diversity of a savanna ecosystem, through an assessment of climatic trends over a period of forty years (1974&ndash, 2014) using Masvingo Province, Zimbabwe, as a case study. The normalised difference vegetation index (NDVI) was used as a proxy for plant species diversity to cover for the absence of long-term historical plant diversity data. Observed precipitation and temperature data collected over the review period were compared with the trends in NDVI to understand the impact of climate change on plant species diversity over time. The nonaligned block sampling design was used as the sampling framework, from which 198 sampling plots were identified. Data sources included satellite images, field measurements, and direct observations. Temperature and precipitation had significant (p &lt, 0.05) trends over the period under study. However, the trend for seasonal total precipitation was not significant but declining. Significant correlations (p &lt, 0.001) were identified between various climate variables and the Shannon index of diversity. NDVI was also significantly correlated to the Shannon index of diversity. The declining trend of plant species in savanna ecosystems is directly linked to the decreasing precipitation and increasing temperatures.

Terrestrial isopods have adapted to predatory pressures by evolving a variety of behaviors, which arise from a combination of specific traits, such as volvation and tonic immobility. Evolutionarily, these behavioral adaptations have been shown to increase the fitness of the individuals of the species who show them because the probability of being predated is reduced due to the loss of interest by the predator towards the immobile prey and the increase of interest towards the other mobile ones. Even if some of these behaviors have been shown to have a genetic basis, there is limited knowledge about the effects of environmental influences and predator-induced learning abilities on the antipredatory strategies of invertebrates, and isopods in particular. Our study aimed to understand the degree to which “nature” and “nurture” (i.e. environmental and genetic factors) and their interactions influence these antipredatory behaviors. There might be a difference in the behavior of wild and captive isopods in their volvation frequency and duration of tonic immobility due to environmental factors (i.e. predation) that induce learning-related behavioral changes. Therefore, we tested this hypothesis. We applied the three types of stimuli, which aim to simulate the interaction of the predator with the isopod. All three species showed a significant difference, between individuals collected in the field (wild) and raised in the laboratory (captive), in the reaction to the stimulus that simulates the fall from a bird’s beak or from the jaws of a lizard after a catch. Although volvation frequency was highly species- and stimulus-specific, the duration of tonic immobility and the delay in the response to each stimulus, when significant, was always higher in wild groups than captive ones. These substantial differences may reflect the evolutionary and ecological characteristics of each species and the importance of environmental pressures to shape the behavior of these invertebrates to optimize their life strategies.

Tropical forests inhabited by endangered orangutans, rhinos, tigers, and elephants in South-east Asia are threatened by deforestation, including oil palm expansion. Certification has been proposed to label sustainable palm oil production. However, from a remotely sensed time-series and imagery analysis (1984–2020), we discovered that most of the currently certified grower supply bases and concessions in Sumatra and Borneo are located in the 1990s large mammals habitat and in areas that were biodiverse tropical forests less than 30 years ago. In light of this dramatic evidence, we suggest that certification schemes claim for the “sustainable” production of palm oil just because they neglect a very recent past of deforestation and habitat degradation.

Limited research has been done to estimate the root biomass (belowground biomass) of savanna grasslands. The advent of remote sensing and related products have facilitated the estimation of biomass in terrestrial ecosystems, providing a synoptic overview on ecosystems biomass. Multispectral remote sensing was used in this study to estimate total biomass (belowground and aboveground) of selected tropical savanna grassland species. Total biomass was estimated by assessing the relationship between aboveground and belowground biomass, the Normalised Difference Vegetation Index (NDVI) and belowground biomass, and NDVI and total biomass. Results showed a positive significant relationship (p = 0.005) between belowground and aboveground biomass. NDVI was significantly correlated (p = 0.0386) to aboveground biomass and the Root Mean Square Error (RMSE) was 18.97 whilst the model BIAS was 0.019, values within acceptable ranges. A significant relationship (p = 0) was found between belowground biomass and NDVI and the RMSE was 5.53 and the model BIAS was 0.0041. More so, a significant relationship (p = 0.054) was observed between NDVI and total biomass. The positive relationships between NDVI and total grass biomass and the lack of bias in the model provides an opportunity to routinely monitor carbon stock and assess seasonal carbon storage fluctuations in grasslands. There is great potential in the ability of remote sensing to become an indispensable tool for assessing, monitoring and inventorying carbon stocks in grassland ecosystems under tropical savanna conditions.

In recent weeks, news has spread that the current coronavirus is the result of something created in a laboratory that then escaped control. This concern also stems from news reporting that back in 2015 experiments were already being carried out in China to create a recombinant SARS-derived coronavirus. Unfortunately, such news finds fertile ground on social media and, especially in pandemic periods, fosters conspiracies and paranoia. This time, however, the news was spread by some authoritative sources. Here I explain how I found out convincing evidence that SARS-CoV-2 did not emerge from a lab but from wildlife exploitation.

Russian forests preserve biodiversity, host endemic species, store carbon, and are inhabited by indigenous people. However, in the last years, large areas of these forests have been damaged by different factors, mainly by logging and fires. This led to important tree loss even in protected areas. Despite it, no previous comprehensive research has been conducted to understand the trends and the causes of this tree loss. This study aims to investigate how Russian protected areas effectively contribute to nature conservation and can reduce forest loss within their borders. For the first time, we analysed the longest time-series available (2001–2018) of tree loss and fires in 201 Russian protected areas (those with the highest protection level). Studying the temporal trends of forest loss, we hypothesized that uncontrolled wildfire should result as the main cause of the forest loss expected within the highest protection areas of Russia and that logging and other anthropogenic activities - because prohibited in these areas - should be a minor threat. Instead, from Earth Observation datasets, we detected that even in the 201 areas with the highest protection, more than 2Mha of forests have been lost between 2001 and 2018. Fire contributed about 25% to this loss but was not the major driver. From a year-by-year analysis of the time series, we were able to disentangle the potential contribution of “other-than-fire” causes of loss. We found that, in 15 out of 18 years of our time series, causes not due to fire were the main driver of tree loss in these strictly protected areas, which reached a peak recently, between 2013 and 2018. Overall, of the 201 strictly protected areas in Russia, 135 experienced tree loss between 2001 and 2018. In 34 of them, the loss was higher than 10,000 ha, where a major percentage was attributable to fire only in 3 sites. In the other 31 protected areas, fire contributed to the total forest loss less than 50%. Our analysis also showed that five strictly protected areas experienced a massive tree loss (> 100kha) from 2001 to 2018. Among them, Lake Baikal, a World Heritage site, suffered an astonishing forest loss of 485 k ha, whose only 21% may be attributed to fire. Considering that we discovered that most of the causes of forest loss may be related to human activities, the situation could be controlled through better management and policy.

The loss of trees in Italy’s forests in recent years (go.nature.com/3yzvdp9) is only partly explained by disturbances such as Storm Vaia in 2018, and salvage logging thereafter. The dominant driver is the production of wood fuel (D. Pettenella et al. Forest@ 18, 1–4; 2021), mainly from coppice. This probably removes about 50% of estimated annual growth (see go.nature.com/3xr1mzc).

A relatively small number of broad-scale patterns describe the distribution of biodiversity across the earth. All of them explore biodiversity focusing on a mono or bi-dimensional space. Conversely, the volume of the forests is rarely considered. In the present work, we tested a global correlation between vascular plant species richness (S) and average forest canopy height (H), the latter regarded as a proxy of volume, using the NASA product of Global Forest Canopy Height map and the global map of plant species diversity. We found a significant correlation between H and S both at global and macro-climate scales, with strongest confidence in the tropics. Hence, two different regression models were compared and discussed to provide a possible pattern of the H–S relation. We suggested that the volume of forest ecosystems should be considered in ecological studies as well as in planning and managing natural sites, although in this first attempt, we cannot definitively prove our hypothesis. Again, high-resolution spatial data could be highly important to confirm the H–S relation, even at different scales.

A central question about biodiversity is how so many species can coexist within the same ecosystem. The idea that ecological niches are critical for the maintenance of species diversity has received increasing support recently. However, a niche is often considered as something static, preconditioned, and unchanging. With the “Biodiversity-related Niches Differentiation Theory” (BNDT), we recently proposed that species themselves are the architects of biodiversity, by proportionally increasing the number of potentially available niches in a given ecosystem. Along similar lines, but independently, the idea of viewing an ecosystem of interdependent species as an emergent autocatalytic set (a self-sustaining network of mutually “catalytic” entities) was suggested, where one (group of) species enables the existence of (i.e., creates niches for) other species. Here, we show that biodiversity can indeed be considered a system of autocatalytic sets, and that this view offers a possible answer to the fundamental question of why so many species can coexist in the same ecosystem. In particular, we combine the two theories (BNDT and autocatalytic sets), and provide some simple but formal examples of how this would work.

Most evolutionary biologists consider selfishness an intrinsic feature of our genes and as the best choice in social situations. During the last years, prolific research has been conducted on the mechanisms that can allow cooperation to emerge “in a world of defectors” to become an evolutionarily stable strategy. A big debate started with the proposal by W.D. Hamilton of “kin selection” in terms of cost sustained by the cooperators and benefits received by related conspecifics. After this, four other main rules for the evolution of cooperation have been suggested. However, one of the main problems of these five rules is the assumption that the payoffs obtained by either cooperating or defeating are quite well known by the parties before they interact and do not change during the time or after repeated encounters. This is not always the case in real life. Following each rule blindly, there is a risk for individuals to get stuck in an unfavorable situation. Axelrod (1984) highlighted that the main problem is how to obtain benefits from cooperation without passing through several trials and errors, which are slow and painful. With a better understanding of this process, individuals can use their foresight to speed up the evolution of cooperation. Here I show that a multi-armed bandit (MAB) model, a classic problem in decision sciences, is naturally employed by individuals to opt for the best choice most of the time, accelerating the evolution of the altruistic behavior and solving the abovementioned problems. A common MAB model that applies extremely well to the evolution of cooperation is the epsilon-greedy (e-greedy) algorithm. This algorithm, after an initial period of exploration (which can be considered as biological history), greedily exploits the best option e% of the time and explores other options the remaining percentage of times (1-e%). Through the epsilon-greedy decision-making algorithm, cooperation evolves as a multilevel process nested in the hierarchical levels that exist among the five rules for the evolution of cooperation. This reinforcement learning, a subtype of artificial intelligence, with trials and errors, provides a powerful tool to better understand and even probabilistically quantify the chances cooperation has to evolve in a specific situation.

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5-10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21-32% additional cases, whose 19-28% more positives and 4-14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution., Graphical abstract Image 1, Highlights • Air quality is linked to respiratory diseases and may facilitate microbial infections. • The prolonged exposure to air pollution can affect SARS-CoV-2 outbreak. • We found that air quality is the best predictor for SARS-CoV-2 mortality in Italy. • A prominent role is played by PM2.5 and emissions from industries and farms. • A worsening of air quality may inflate the death toll of similar future pandemics., Short summary: Artificial intelligence reveals that, in Italy, air pollution mainly due to fine particulate matter produced by industries, farms, and road traffic may severely affect the susceptibility to respiratory infections caused by SARS-CoV-like pathogens.

The total freshwater biodiversity is not fully known. In particular, freshwater invertebrates and microbes are poorly studied groups, and in tropical latitudes, that support most of the species of the world, the information is lacking. Although almost a hundred thousand of species live in fresh water, the species losses continue at the high rate and the probability of preserving much of the remaining biodiversity in fresh water seems to be very low. Freshwater ecosystems are increasingly influenced by multiple stressors that lead to loss of sensitive species and an overall reduction in diversity. Environmental change threatens freshwater biodiversity. This paper reports an extensive review of work that evaluates the current main threats for freshwater biodiversity, on a local and global scale.

Objective The aim of this study was to evaluate the usefulness of magnetic resonance imaging (MRI) in detecting the progression of Duchenne muscular dystrophy (DMD) by quantification of fat infiltration (FI) and muscle volume index (MVI, a residual-to-total muscle volume ratio). Methods Twenty-six patients (baseline age: 5–12 years) with genetically proven DMD were longitudinally analyzed with lower limb 3T MRI, force measurements, and functional tests (Gowers, 10-m time, North Star Ambulatory Assessment, 6-min walking test). Five age-matched controls were also examined, with a total of 85 MRI studies. Semiquantitative (scores) and quantitative MRI (qMRI) analyses (signal intensity ratio – SIR, lower limb MVI, and individual muscle MVI) were carried out. Permutation and regression analyses according to both age and functional test-outcomes were calculated. Age-related quantitative reference curves of SIRs and MVIs were generated. Results FI was present on glutei and adductor magnus in all patients since the age of 5, with a proximal-to-distal progression and selective sparing of sartorius and gracilis. Patients' qMRI measures were significantly different from controls' and among age classes. qMRI were more sensitive than force measurements and functional tests in assessing disease progression, allowing quantification also after loss of ambulation. Age-related curves with percentile values were calculated for SIRs and MVIs, to provide a reference background for future experimental therapy trials. SIRs and MVIs significantly correlated with all clinical measures, and could reliably predict functional outcomes and loss of ambulation. Interpretations qMRI-based indexes are sensitive measures that can track the progression of DMD and represent a valuable tool for follow-up and clinical studies.

Biological diversity analysis is among the most informative approaches to describe communities and regional species compositions. Soil ecosystems include large numbers of invertebrates, among which soil bugs (Crustacea, Isopoda, Oniscidea) play significant ecological roles. The aim of this study was to provide advices to optimize the sampling effort, to efficiently monitor the diversity of this taxon, to analyze its seasonal patterns of species composition, and ultimately to understand better the coexistence of so many species over a relatively small area. Terrestrial isopods were collected at the Natural Reserve “Saline di Trapani e Paceco” (Italy), using pitfall traps monthly monitored over 2 years. We analyzed parameters of α‐ and β‐diversity and calculated a number of indexes and measures to disentangle diversity patterns. We also used various approaches to analyze changes in biodiversity over time, such as distributions of species abundances and accumulation and rarefaction curves. As concerns species richness and total abundance of individuals, spring resulted the best season to monitor Isopoda, to reduce sampling efforts, and to save resources without losing information, while in both years abundances were maximum between summer and autumn. This suggests that evaluations of β‐diversity are maximized if samples are first collected during the spring and then between summer and autumn. Sampling during these coupled seasons allows to collect a number of species close to the γ‐diversity (24 species) of the area. Finally, our results show that seasonal shifts in community composition (i.e., dynamic fluctuations in species abundances during the four seasons) may minimize competitive interactions, contribute to stabilize total abundances, and allow the coexistence of phylogenetically close species within the ecosystem.

The development of a unique index to represent the real diversity of a community is still a challenge. The difficulties to find a single, reliable, diversity metric is mostly due to the fact that the indices allowing an estimation of the number of species actually present in a sample do not provide information on how species abundances are distributed within a community. Although some diversity measures attempted to weigh richness in relation to the total abundance, the homogeneity (evenness) of individuals within species is not integrated into them. The most common diversity indices have been "summarized" by Hill in 1973 in a single equation and, subsequently, different indicators have been proposed to estimate the "absolute" and the “effective” number of species present in a study area in an attempt to find a unified treatment of all standard diversity indices and to compare the true diversity among community. However, the choice of one of the effective number of species indices depends on what aspects of diversity of the study sites the research aims to investigate and could bias the study and mislead the interpretation of the comparative results. Moreover, effective Hill numbers account for only the observed, and not the expected (i.e. the absolute) diversity of the studied community. To compare and interpret the effective number of species with one single metric and analyze the diversity data with techniques that do not depend on a traditional non-parametric index, we developed an absolute measure of diversity based on the effective number of species and derived from the Hill numbers of order 0, 1, and 2. We tested the new index against previously-proposed absolute diversity estimators and the first three Hill numbers taken alone. We simulated the new index's behavior with different gradients of richness, abundance and evenness and we, finally, empirically tested it on tree communities of three biomes (boreal, temperate and tropical forests) of the United States and on a coral reef community of Cuba. This new index proved to be the first reliable and comparable measure, which combines both the absolute richness and the evenness of a community related to the most used traditional indices in a unique, simple and comprehensive numerical value that would represent the absolute effective diversity (AED) of any biological community.

The globalization of the palm oil trade poses a menace to the ecosystem integrity of Southeast Asia. In this short communication, we briefly discuss why palm oil certifications may have failed as an effective means to halt forest degradation and biodiversity loss. From a comparison of multiple new datasets, we analysed recent tree loss in Indonesia, Malaysia, and Papua New Guinea, and discovered that, from 2001 to 2016, about 40% of the area located in certified concessions suffered from habitat degradation, deforestation, fires, or other tree damages. Certified concessions have been subject to more tree removals than non-certified ones. We also detect significant tree loss before and after the start of certification schemes. Beyond non-governmental organisations' concern that Roundtable on Sustainable Palm Oil (RSPO) and Palm Oil Innovation Group (POIG) certifications allow ongoing clearance of any forest not identified as of high conservation values (HCV) or high carbon stock (HCS), we suggest an alarming and previously overlooked situation, such as that current "sustainable palm oil" is often associated with recent habitat degradation and forest loss. In other words, certified palm oil production may not be so sustainable.

Motivation The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2). Time period and grain BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format .csv and .SQL., Global Ecology and Biogeography, 27 (7), ISSN:1466-822X, ISSN:1466-8238