Optimizing ensembles of small models for predicting the distribution of species with few occurrences

1. Ensembles of Small Models (ESM) represent a novel strategy for species distribution modelling with few observations. ESMs are built by calibrating many small models and then averaging them into an ensemble model where the small models are weighted by their cross-validated scores of predictive performance. In a previous paper (Breiner, Guisan, Bergamini, & Nobis, Methods in Ecology and Evolution, 6, 1210-1218, 2015), we reported two major findings. First, ESMs proved largely superior to standard models in terms of model performance and transferability. Second, ESMs including different modelling techniques did not clearly improve model performance compared to single-technique ESMs. However, ESMs often require a large computation effort, which can become problematic when modelling large numbers of species. Given the appealing new perspectives offered by ESMs, it is especially important to investigate if some techniques yield increased performance while saving computation time and thus could be predominantly used for building ESMs. 2. Here, we present results from a reanalysis of a subset of the data used in Breiner etal. (2015). More specifically, we ran ESMs: (1) fitted with 10 modelling techniques separately (in Breiner etal., 2015 we used only three techniques); and (2) using various parameter options for each modelling technique (i.e., model tuning). 3. We show that ESMs vary in model performance and computation time across techniques, and some techniques are advantageous in terms of optimizing model performance and computation time (i.e., GLM, CTA and ANN). Including one of these modelling techniques could thus optimize computation time compared to using more computing-intensive techniques like GBM. Next, we show that parameter tuning can improve performance and transferability of ESMs, but often at the cost of computation time. Parameter tuning could therefore be used when computing resources are not a limiting factor. 4. These findings help improve the applicability and performance of ESMs when applied to large numbers of species

Including environmental niche information to improve IUCN Red List assessments

Aim Information on change in species’ environmental preferences (i.e., niche) is currently not included in IUCN Red List criteria, although such information is key for assessing whether species not only lose geographic range but also lose part of their realized niche. Here, using niche size quantification and niche-based species distribution models (SDMs), we test whether realized niche size and predicted potential range size provide additional information compared with the standard IUCN scores. Location Switzerland, national scale. Methods We simulated randomly, spatially directed, and ecologically directed local extinction events of varying magnitudes (10%, 30%, and 50% of occurrences). For a set of 148 representative vascular plant species, we tested how accurately the geographic versus niche measures pictured these extinction scenarios respectively. Results We found that changes in niche size often corresponded to changes in geographic space. However, there was considerable variation and, for many species, changes in geographic and in niche space delivered complementary information. IUCN criteria based on spatial projections of SDMs did not capture extinction events in most cases and often increased the modelled range size, even when up to 50% of the occurrences were removed by simulated extinction events. Main conclusion Our findings demonstrate that changes in niche size can provide valuable additional information and could be used more systematically to complement changes in range size for Red List assessments. In turn, change in SDM-predicted range size was not a good surrogate for classical extent of occurrence and area of occupancy criteria and should be used with caution. Further research is needed to assess whether and how spatial predictions of SDMs may be used to appropriately complement current IUCN criteria and to test whether our findings apply to other organisms and other spatial extents

Overcoming limitations of modelling rare species by using ensembles of small models

1. Species distribution models (SDMs) have become a standard tool in ecology and applied conservation biology. Modelling rare and threatened species is particularly important for conservation purposes. However, modelling rare species is difficult because the combination of few occurrences and many predictor variables easily leads to model overfitting. A new strategy using ensembles of small models was recently developed in an attempt to overcome this limitation of rare species modelling and has been tested successfully for only a single species so far. Here, we aim to test the approach more comprehensively on a large number of species including a transferability assessment. 2. For each species numerous small (here bivariate) models were calibrated, evaluated and averaged to an ensemble weighted by AUC scores. These 'ensembles of small models' (ESMs) were compared to standard Species Distribution Models (SDMs) using three commonly used modelling techniques (GLM, GBM, Maxent) and their ensemble prediction. We tested 107 rare and under-sampled plant species of conservation concern in Switzerland. 3. We show that ESMs performed significantly better than standard SDMs. The rarer the species, the more pronounced the effects were. ESMs were also superior to standard SDMs and their ensemble when they were independently evaluated using a transferability assessment. 4. By averaging simple small models to an ensemble, ESMs avoid overfitting without losing explanatory power through reducing the number of predictor variables. They further improve the reliability of species distribution models, especially for rare species, and thus help to overcome limitations of modelling rare species

The ATG16L1 risk allele associated with Crohn’s disease results in a Rac1-dependent defect in dendritic cell migration that is corrected by thiopurines

Thiopurines are commonly used drugs in the therapy of Crohn’s disease, but unfortunately only show a 30% response rate. The biological basis for the thiopurine response is unclear, thus hampering patient selection prior to treatment. A genetic risk factor associated specifically with Crohn’s disease is a variant in ATG16L1 that reduces autophagy. We have previously shown that autophagy is involved in dendritic cell (DC)-T-cell interactions and cytoskeletal regulation. Here we further investigated the role of autophagy in DC cytoskeletal modulation and cellular trafficking. Autophagy-deficient DC displayed loss of filopodia, altered podosome distribution, and increased membrane ruffling, all consistent with increased cellular adhesion. Consequently, autophagy-deficient DC showed reduced migration. The cytoskeletal aberrations were mediated through hyperactivation of Rac1, a known thiopurine target. Indeed thiopurines restored the migratory defects in autophagy-deficient DC. Clinically, the ATG16L1 risk variant associated with increased response to thiopurine treatment in patients with Crohn’s disease but not ulcerative colitis. These results suggest that the association between ATG16L1 and Crohn’s disease is mediated at least in part through Rac1 hyperactivation and subsequent defective DC migration. As this phenotype can be corrected using thiopurines, ATG16L1 genotyping may be useful in the identification of patients that will benefit most from thiopurine treatment

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology