Luc Hoffmann Institute

Biodiversity research is replete with scientific studies depicting future trajectories of decline that have failed to mobilize transformative change. Imagination and creativity can foster new ways to address longstanding problems to create better futures for people and the planet.

The destruction of ecosystems for vegetable oil production represents a major cause of global biodiversity loss and greenhouse gas emissions1. Over the last two decades, oil palm, in particular, has caused societal concern due to its high impacts on biodiverse and carbon-dense tropical rainforests2–8, leading to calls to source vegetable oils from alternative oil-producing crops. However, given the high yields of oil palm, how does that damage compare with other oil crops that require more land? Here, we estimate the carbon and biodiversity footprints, per unit of oil produced, of the world’s five major vegetable oil crops. We find that oil palm has the lowest carbon loss and species richness loss per-tonne-oil, but has a larger impact on range-restricted species than sunflower and rapeseed. We go on to identify global areas for oil crop expansion that will minimise future carbon and biodiversity impacts, and argue that closing current yield gaps and optimising the location of future growing areas will be much more effective at reducing future environmental impacts of global vegetable oil production than substituting any one crop for another.

CITATION: Maas, B. et al. 2020. Academic leaders must support inclusive scientific communities during COVID-19. Nature Ecology and Evolution, doi:10.1038/s41559-020-1233-3.

Abstract A controversy at the 2016 IUCN World Conservation Congress on the topic of closing domestic ivory markets (the 007, or so-called James Bond, motion) has given rise to a debate on IUCN's value proposition. A cross-section of authors who are engaged in IUCN but not employed by the organization, and with diverse perspectives and opinions, here argue for the importance of safeguarding and strengthening the unique technical and convening roles of IUCN, providing examples of what has and has not worked. Recommendations for protecting and enhancing IUCN's contribution to global conservation debates and policy formulation are given.

Large uncertainties in terrestrial carbon stocks and sequestration predictions result from insufficient regional data characterizing forest structure. This study uses satellite waveform lidar from ICESat to estimate regional forest structure in central New England, where each lidar waveform estimates fine-scale forest heterogeneity. ICESat is a global sampling satellite, but does not provide wall-to-wall coverage. Comprehensive, wall-to-wall ecosystem state characterization is achieved through spatial extrapolation using the random forest machine-learning algorithm. This forest description allows for effective initialization of individual-based terrestrial biosphere models making regional carbon flux predictions. Within 42/43.5 N and 73/71.5 W, aboveground carbon was estimated at 92.47 TgC or 45.66 MgC ha−1, and net carbon fluxes were estimated at 4.27 TgC yr−1 or 2.11 MgC ha−1 yr−1. This carbon sequestration potential was valued at 47% of fossil fuel emissions in eight central New England counties. In preparation for new lidar and hyperspectral satellites, linking satellite data and terrestrial biosphere models are crucial in improving estimates of carbon sequestration potential counteracting anthropogenic sources of carbon.

Biophysical conditions, including climate, environmental stress, and habitat availability, are key drivers of many ecological processes (e.g., community assembly and productivity) and associated ecosystem services (e.g., carbon sequestration and fishery production). Furthermore, anthropogenic impacts such as coastal development and fishing can have drastic effects on the structure and function of marine ecosystems. Scientists need to account for environmental variation and human impacts to accurately model, manage, and conserve marine ecosystems. Although there are many types of environmental data available from global remote sensing and open-source data products, some are inaccessible to potential end-users because they exist as global layers in high temporal and spatial resolutions which require considerable computational power to process. Additionally, coastal locations often suffer from missing data or data quality issues which limit the utility of some global marine products for coastal sites. Herein we present the Marine Socio-Environmental Covariates dataset for the global oceans, which consists of environmental and anthropogenic variables summarized in ecologically relevant ways. The dataset includes four sets of environmental variables related to biophysical conditions (net primary productivity models corrected for shallow-water reflectance, wave energy including sheltered-coastline corrections) and landscape context (coral reef and land cover within varying radii). We also present two sets of anthropogenic variables, human population density (within varying radii) and distance to large population center, which can serve as indicators of local human impacts. We have paired global, summarized layers available for download with an online data querying platform that allows users to extract data for specific point locations with finer control of summary statistics. In creating these global layers and online platform, we hope to make the data accessible to a wide array of end-users with the goal of advancing marine ecosystem studies.

The rate at which animals are vanishing from this planet is one of the signatures of this age, as sure a sign of human dominance as our impact on Earth's nitrogen, phosphorus, and carbon cycles. This disappearance of animals from the world's ecosystems is generally a by-product of human activity, not an intentional act. Animals do matter to people, but on balance, they matter less than food, jobs, energy, money, and development. As long as we continue to view animals in ecosystems as irrelevant to these basic demands, animals will lose.