Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

Aim: Soil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. Location: Island of Ireland. Methods: We tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. Results: Using spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main Conclusions: Spatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.

Dynamic Species Distribution Models in the Marine Realm: Predicting Year-Round Habitat Suitability of Baleen Whales in the Southern Ocean

Species distribution models (SDMs) relate species information to environmental conditions to predict potential species distributions. The majority of SDMs are static, relating species presence information to long-term average environmental conditions. The resulting temporal mismatch between species information and environmental conditions can increase model inference’s uncertainty. For SDMs to capture the dynamic species-environment relationships and predict near-real-time habitat suitability, species information needs to be spatiotemporally matched with environmental conditions contemporaneous to the species’ presence (dynamic SDMs). Implementing dynamic SDMs in the marine realm is highly challenging, particularly due to species and environmental data paucity and spatiotemporally biases. Here, we implemented presence-only dynamic SDMs for four migratory baleen whale species in the Southern Ocean (SO): Antarctic minke, Antarctic blue, fin, and humpback whales. Sightings were spatiotemporally matched with their respective daily environmental predictors. Background information was sampled daily to describe the dynamic environmental conditions in the highly dynamic SO. We corrected for spatial sampling bias by sampling background information respective to the seasonal research efforts. Independent model evaluation was performed on spatial and temporal cross-validation. We predicted the circumantarctic year-round habitat suitability of each species. Daily predictions were also summarized into bi-weekly and monthly habitat suitability. We identified important predictors and species suitability responses to environmental changes. Our results support the propitious use of dynamic SDMs to fill species information gaps and improve conservation planning strategies. Near-real-time predictions can be used for dynamic ocean management, e.g., to examine the overlap between habitat suitability and human activities. Nevertheless, the inevitable spatiotemporal biases in sighting data from the SO call for the need for improving sampling effort in the SO and using alternative data sources (e.g., passive acoustic monitoring) in future SDMs. We further discuss challenges of calibrating dynamic SDMs on baleen whale species in the SO, with a particular focus on spatiotemporal sampling bias issues and how background information should be sampled in presence-only dynamic SDMs. We also highlight the need to integrate visual and acoustic data in future SDMs on baleen whales for better coverage of environmental conditions suitable for the species and avoid constraints of using either data type alone.

The Phanaeus tridens species group (Coleoptera: Scarabaeoidea): a dung beetle group with genital morphological stasis but a changing ecological niche

The Phanaeus tridens species group is revised and found to consist of twelve species: P. tridens Castelnau, 1840, P. moroni Arnaud, 2001 stat. rev., P. balthasari Arnaud, 2001 stat. rev., P. daphnis Harold, 1863, P. coeruleus Bates, 1887 stat. rev., P. herbeus Bates, 1887 stat. rev., P. substriolatus Balthasar, 1939 stat. rev., P. furiosus Bates, 1887, P. pseudofurcosus Balthasar, 1939 stat. rev., P. nimrod Harold, 1863, P. victoriae Moctezuma sp. nov., and P. eximius Bates, 1887. The majority of the name-bearing types of the group were revised. The neotype for P. tridens is suggested herein. The following junior subjective synonymies are recognized: P. frankenbergeri Balthasar, 1939 = P. tridens Castelnau, 1840, P. tricornis Olsoufieff, 1924 = P. herbeus Bates, 1887, and P. babori Balthasar, 1939 = P. nimrod Harold, 1863; while P. furcosus Felsche, 1901 = P. furiosus Bates, 1887 is recognized as a junior objective synonymy. The species within the P. tridens species group are diagnosed by the morphology of the pronotum and elytra, while the genital morphology of males is found to be homogeneous and uninformative for species delimitation. Most species within the group show a wide diversity of colouration (showing green, red, and blue chromatic phases). This probably led to taxonomical confusion by previous authors. Here, we present a new identification key, species distribution models. Habitus photographs and character illustrations for all the species within the group are provided. The climatic niches overlap widely in P. herbeus and P. daphnis, but the other species within the group show a reduced overlap in their climatic niches. Consequently, the P. tridens species group is proposed as a case of morphological stasis that might be explained by a trade-off between the evolution of pronotal structures and genitalia, while differences in the ecological niche might promote speciation.