Explore the vast range of titles available.

Global change is expected to expand and shrink species' distributions in complex ways beyond just retraction at warm edges and expansion at cool ones. Detecting these changes is complicated by the need for robust baseline data for comparison. For instance, gaps in species' distributions may reflect long‐standing patterns, recent shifts, or merely insufficient sampling effort. We investigated an apparent gap in the distribution of the American pika, Ochotona princeps, along the North American Sierra Nevada. Historical records from this region are sparse, with ~100 km separating previously documented pika‐occupied sites. Surveys during 2014–2023 confirmed that the gap is currently unoccupied by pikas, and evidence of past occurrence indicates that the gap has expanded over time, likely due to contemporary global change. Sites lacking evidence of past pika occurrence were climatically and geographically more distant from sites with signs of recent (former) occurrence and currently occupied sites. Formerly and currently occupied sites were partially climatically distinct, suggesting either metapopulation‐like dynamics or an extinction debt that may eventually result in further population losses at the edge of suitable climate space. The Feather River gap aligns with one of several “low points” in the otherwise continuous boreal‐like conditions spanning the Cascade Range and Sierra Nevada and is coincident with discontinuities in ranges of other mammals. These results highlight the potential for climate‐driven fragmentation and range retraction in regions considered climatically and geographically interior to a species' overall distribution.

Aim: To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine-learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data. Innovation: Modelling rare species' distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non-detections (i.e. class imbalance). Citizen science data can provide a cost-effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under-sample the majority class (non-detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under-sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor). Main Conclusions: Spatial under-sampling increased the accuracy of each model and outperformed the approach typically used to direct under-sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

Becoming literate has been argued to have a range of social, economic and psychological effects. Less examined is the extent to which repercussions of becoming literate may vary as a function of writing system variation. A salient way in which writing systems differ is in their directionality. Recent studies have claimed that directional biases in a variety of spatial domains are attributable to reading and writing direction. This claim is the focus of the present paper, which considers the scope and possible mechanisms underlying script directionality effects in spatial cognition, with particular attention to domains with real-world relevance. Three questions are addressed: (1) What are possible mediating and moderator variables relevant to script directionality effects in spatial cognition? (2) Does script directionality exert a fixed or a malleable effect? and (3) How can script directionality effects be appropriately tested? After discussing these questions in the context of specific studies, we highlight general methodological issues in this literature and provide recommendations for the design of future research.

The Darwinian shortfall, i.e. the lack of knowledge of phylogenetic relationships, significantly impedes our understanding of evolutionary drivers of global patterns of biodiversity. Spatial bias in the Darwinian shortfall, where phylogenetic knowledge in some regions is more complete than others, could undermine eco‐ and biogeographic inferences. Yet, spatial biases in phylogenetic knowledge for major groups – such as plants – remain poorly understood. Using data for 337 023 species (99.7%) of seed plants (Spermatophyta), we produced a global map of phylogenetic knowledge based on regional data and tested several potential drivers of the observed spatial variation. Regional phylogenetic knowledge was defined as the proportion of the regional seed plant flora represented in GenBank's nucleotide database with phylogenetically relevant data. We used simultaneous autoregressive models to explain variation in phylogenetic knowledge based on three biodiversity variables (species richness, range size and endemism) and six socioeconomic variables representing funding and accessibility. We compared observed patterns and relationships to established patterns of the Wallacean shortfall (the lack of knowledge of species distributions). We found that the Darwinian shortfall is strongly and significantly related to the macroecological distribution of species' range sizes. Small‐ranged species were significantly less likely to have phylogenetic data, leading to a concentration of the Darwinian shortfall in species‐rich, tropical countries where range sizes are small on average. Socioeconomic factors were less important, with significant but quantitatively small effects of accessibility and funding. In conclusion, reducing the Darwinian shortfall and smoothen its spatial bias will require increased efforts to sequence the world's small‐ranged (endemic) species.

The latitudinal biodiversity gradient (LBG), in which species richness decreases from tropical to polar regions, is a pervasive pattern of the modern biosphere. Although the distribution of fossil occurrences suggests this pattern has varied through deep time, the recognition of palaeobiogeographic patterns is hampered by geological and anthropogenic biases. In particular, spatial sampling heterogeneity has the capacity to impact upon the reconstruction of deep time LBGs. Here we use a simulation framework to test the detectability of three different types of LBG (flat, unimodal and bimodal) over the last 300 Myr. We show that heterogeneity in spatial sampling significantly impacts upon the detectability of genuine LBGs, with known biodiversity patterns regularly obscured after applying the spatial sampling window of fossil collections. Sampling-standardization aids the reconstruction of relative biodiversity gradients, but cannot account for artefactual absences introduced by geological and anthropogenic biases. Therefore, we argue that some previous studies might have failed to recover the ‘true’ LBG type owing to incomplete and heterogeneous sampling, particularly between 200 and 20 Ma. Furthermore, these issues also have the potential to bias global estimates of past biodiversity, as well as inhibit the recognition of extinction and radiation events.

Spatial biases are an intrinsic feature of occurrence data used in species distribution models (SDMs). Thinning species occurrences, where records close in the geographic or environmental space are removed from the modeling procedure, is an approach often used to address these biases. However, thinning occurrence data can also negatively affect SDM performance, given that the benefits of removing spatial biases might be outweighed by the detrimental effects of data loss caused by this approach. We used real and virtual species to evaluate how spatial and environmental thinning affected different performance metrics of four SDM methods. The occurrence data of virtual species were sampled randomly, evenly spaced, and clustered in the geographic space to simulate different types of spatial biases, and several spatial and environmental thinning distances were used to thin the occurrence data. Null datasets were also generated for each thinning distance where we randomly removed the same number of occurrences by a thinning distance and compared the results of the thinned and null datasets. We found that spatially or environmentally thinned occurrence data is no better than randomly removing them, given that thinned datasets performed similarly to null datasets. Specifically, spatial and environmental thinning led to a general decrease in model performances across all SDM methods. These results were observed for real and virtual species, were positively associated with thinning distance, and were consistent across the different types of spatial biases. Our results suggest that thinning occurrence data usually fails to improve SDM performance and that the use of thinning approaches when modeling species distributions should be considered carefully.

This study investigated the impact of target template variation or consistency on attentional bias in location probability learning. Participants conducted a visual search task to find a heterogeneous shape among a homogeneous set of distractors. The target and distractor shapes were either fixed throughout the experiment (target-consistent group) or unpredictably varied on each trial (target-variant group). The target was often presented in one possible search region, unbeknownst to the participants. When the target template was consistent throughout the biased visual search, spatial attention was persistently biased toward the frequent target location. However, when the target template was inconsistent and varied during the biased search, the spatial bias was attenuated so that attention was less prioritized to a frequent target location. The results suggest that the alternative use of target templates may interfere with the emergence of a persistent spatial bias. The regularity-based spatial bias depends on the number of attentional shifts to the frequent target location, but also on search-relevant contexts.

Aim Species distribution models (SDMs) are widely used to forecast potential range expansion of invasive species. However, invasive species occurrence datasets often have spatial biases that may violate key SDM assumptions. In this study, we examined alternative methods of spatial bias correction and multiple methods for model evaluation for seven invasive plant species. Location North America. Taxon Common Tansy (Tanacetum vulgare), Wild Parsnip (Pastinaca sativa), Leafy Spurge (Euphorbia virgata), Common Teasel (Dipsacus fullonum), Brown Knapweed (Centaurea jacea), Black Swallowwort (Vincetoxicum nigrum) and Dalmatian Toadflax (Linaria dalmatica). Methods We employed bias‐correction measures for both occurrence sampling and background sampling inputs in a factorial design for Maxent resulting in six potential models for each species. We evaluated our models for complexity, model fit and using commonly employed evaluation metrics: AUC, partial AUC, the continuous Boyce index and sensitivity. We then developed a structured process for model selection. Results Models developed without occurrence or background bias correction often were overly complex and did not transfer well to expanding range fronts. Conversely, models that employed occurrence and/or background bias‐correction measures were less complex, had better AICc scores and had greater projection into incipient areas. These simpler models were also more likely to be selected when evaluated using a process that integrated multiple evaluation metrics. We found that invasion history (e.g. established versus incipient) was associated with the effectiveness of spatial bias correction techniques. Main Conclusions While challenges exist in building climate‐based correlative species distribution models for invasive species, we found that methods relying on maximizing AUC performed poorly for invasive species. We advocate for the use of multiple and diverse metrics for model evaluation. Users of species distribution models need to incorporate explicit consideration of model discrimination, model fit and model complexity into their decision‐making processes if they are to build biologically realistic models.

Biases in invasion science have led to a taxonomic focus on plants, particularly a subset of well-studied plants, and a geographic focus on invasions in Europe and North America. While broader, country-level geographic biases are well known, it is unclear whether these biases extend to a finer scale. This study assessed whether research sites for ten well-studied invasive plants in the U.S. are geographically biased relative to each species’ known invaded range. We compared the distribution, climate, specific geographic variables related to land type (public or private), proximity to roads and universities, and state noxious weed status of research sites reported in 735 scientific articles to the locations of manager records from EDDMapS and iMap Invasives. We attributed each scientific article to one of five study types: impact, invasive trait, mapping, management, and recipient community traits. While the number of research sites was much smaller than the number of manager records, they generally encompassed similar geographies. However, research sites tended to skew towards species’ warm range margins. For all but one species, at least one study type encompassed a significantly different climate space from manager records, suggesting that some level of climatic bias is common. Impact and management studies occurred within the same climate space for all species, suggesting that these studies focus on similar areas—likely those with the greatest impacts and management needs. Manager records were more likely to be found near roads, which are both habitats and vectors for invasive plants, and on public land. Research sites were more likely to be found near a college or university. Overall, we did not find evidence for substantial geographic biases in research studies of these well-studied species, suggesting that researchers are generally doing a good job of exploring the impacts, traits, and management implications of invasions across the extents of the invaded range. However, the consistent climatic biases and spatial clustering of specific study types suggests that researchers and managers should use caution when developing inference for understudied geographic areas.

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 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. (Diversity and Distributions, 24, 460) proposed a method for under‐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 spatially stratified 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 spatially stratified under‐sampling method of Robinson et al. (Diversity and Distributions, 24, 460) by using biological records to train species distribution models of rare millipedes. Results Using spatially stratified under‐sampled data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The spatial pattern of under‐sampling affected model performance. Training data that was under‐sampled in a spatially stratified way sometimes produced worse models than did data that was under‐sampled in an unstratified way. Geographic coordinates were as good as or better than environmental variables for predicting distributions of one out of six species. Main Conclusions Spatially stratified under‐sampling improved prediction performance of species distribution models for rare millipedes. Spatially stratified under‐sampling was most effective for rarer species, although unstratified under‐sampling was sometimes more effective. The good prediction performance of models using geographic coordinates is promising for modelling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.