Enhancing prioritization strategies of natural extracts: integrating heterogeneous data from metabolomics datasets and biological screenings into knowledge graphs - Luis-Manuel Quiros-Guerrero; Frederic Burdet; Olivier Kirchhoffer; Paola Haemmerli; Louis-Felix Nothias; Pierre-Marie Allard; Arnaud Gaudry; Jahn Nitschke; Nabil Hanna; Sébastien Moretti; Robin Engler; Florence Mehl; et al. (Phytochemistry Letters, 2025)

Climate change effects on animal and plant phylogenetic diversity in southern Africa - Dorothea V. Pio; Robin Engler; H. Peter Linder; Ara Monadjem; Fenton P.D. Cotterill; Peter J. Taylor; M. Corrie Schoeman; Benjamin W. Price; Martin H. Villet; Geeta Eick; Nicolas Salamin; Antoine Guisan (Global Change Biology, 2014)
Much attention has been paid to the effects of climate change on species' range reductions and extinctions. There is however surprisingly little information on how climate change driven threat may impact the tree of life and result in loss of phylogenetic diversity ( PD ). Some plant families and mammalian orders reveal nonrandom extinction patterns, but many other plant families do not. Do these discrepancies reflect different speciation histories and does climate induced extinction result in the same discrepancies among different groups? Answers to these questions require representative taxon sampling. Here, we combine phylogenetic analyses, species distribution modeling, and climate change projections on two of the largest plant families in the Cape Floristic Region (Proteaceae and Restionaceae), as well as the second most diverse mammalian order in Southern Africa (Chiroptera), and an herbivorous insect genus ( Platypleura ) in the family Cicadidae to answer this question. We model current and future species distributions to assess species threat levels over the next 70 years, and then compare projected with random PD survival. Results for these animal and plant clades reveal congruence. PD losses are not significantly higher under predicted extinction than under random extinction simulations. So far the evidence suggests that focusing resources on climate threatened species alone may not result in disproportionate benefits for the preservation of evolutionary history.

An Assessment of Forest Cover Trends in South and North Korea, From 1980 to 2010 - Robin Engler; Victor Teplyakov; Jonathan M. Adams (Environmental Management, 2014)

Climate change and plant distribution: local models predict high‐elevation persistence - CHRISTOPHE F. RANDIN; ROBIN ENGLER; SIGNE NORMAND; MASSIMILIANO ZAPPA; NIKLAUS E. ZIMMERMANN; PETER B. PEARMAN; PASCAL VITTOZ; WILFRIED THUILLER; ANTOINE GUISAN (Global Change Biology, 2009)
Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across‐scale comparisons have been made using data of different resolutions and geographic extents. Here, we assess whether climate change‐induced habitat losses predicted at the European scale (10 × 10′ grid cells) are also predicted from local‐scale data and modeling (25 m × 25 m grid cells) in two regions of the Swiss Alps. We show that local‐scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European‐scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine‐grain elevation range within 10 × 10′ cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local‐scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.

Prediction of plant species distributions across six millennia - Peter B. Pearman; Christophe F. Randin; Olivier Broennimann; Pascal Vittoz; Willem O. van der Knaap; Robin Engler; Gwenaelle Le Lay; Niklaus E. Zimmermann; Antoine Guisan (Ecology Letters, 2008)
The usefulness of species distribution models (SDMs) in predicting impacts of climate change on biodiversity is difficult to assess because changes in species ranges may take decades or centuries to occur. One alternative way to evaluate the predictive ability of SDMs across time is to compare their predictions with data on past species distributions. We use data on plant distributions, fossil pollen and current and mid‐Holocene climate to test the ability of SDMs to predict past climate‐change impacts. We find that species showing little change in the estimated position of their realized niche, with resulting good model performance, tend to be dominant competitors for light. Different mechanisms appear to be responsible for among‐species differences in model performance. Confidence in predictions of the impacts of climate change could be improved by selecting species with characteristics that suggest little change is expected in the relationships between species occurrence and climate patterns.

Using Niche‐Based Models to Improve the Sampling of Rare Species - ANTOINE GUISAN; OLIVIER BROENNIMANN; ROBIN ENGLER; MATHIAS VUST; NIGEL G. YOCCOZ; ANTHONY LEHMANN; NIKLAUS E. ZIMMERMANN (Conservation Biology, 2006)
Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche‐based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model‐based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.