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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.

Data needed for macroecological analyses are difficult to compile and often hidden away in supplementary material under non-standardized formats. Phylogenies, range data, and trait data often use conflicting taxonomies and require ad hoc decisions to synonymize species or fill in large amounts of missing data. Furthermore, most available data sets ignore the large impact that humans have had on species ranges and diversity. Ignoring these impacts can lead to drastic differences in diversity patterns and estimates of the strength of biological rules. To help overcome these issues, we assembled PHYLACINE, The Phylogenetic Atlas of Mammal Macroecology. This taxonomically integrated platform contains phylogenies, range maps, trait data, and threat status for all 5,831 known mammal species that lived since the last interglacial (∼130,000 years ago until present). PHYLACINE is ready to use directly, as all taxonomy and metadata are consistent across the different types of data, and files are provided in easy-to-use formats. The atlas includes both maps of current species ranges and present natural ranges, which represent estimates of where species would live without anthropogenic pressures. Trait data include body mass and coarse measures of life habit and diet. Data gaps have been minimized through extensive literature searches and clearly labelled imputation of missing values. The PHYLACINE database will be archived here as well as hosted online so that users may easily contribute updates and corrections to continually improve the data. This database will be useful to any researcher who wishes to investigate large-scale ecological patterns. Previous versions of the database have already provided valuable information and have, for instance, shown that megafauna extinctions caused substantial changes in vegetation structure and nutrient transfer patterns across the globe.

There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

Many species distribution models (SDMs) are built with precise but geographically restricted presence–absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species’ niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

1. Range maps represent the geographic distribution of species, and they are commonly used to determine species coverage within protected areas and to find additional places needing protection. However, range maps are characterized by commission errors, where species are thought to be present in locations where they are not. When available, habitat suitability models can reduce commission errors in range maps, but these models are not always available. Adopting a coarse spatial resolution is often seen as an alternative approach for reducing the effect of commission errors, but this comes with poorly explored conservation trade-offs. 2. Here, we characterize these trade-offs by identifying scenarios of protected area expansion for the world's threatened terrestrial mammals under different resolutions (10-200 km) and distribution data deriving from range maps and habitat suitability models. 3. We found that planning new protected areas using range maps results in an overestimation of the species protection level when compared with habitat suitability models (which are more closely related to species presence). This overestimation increases when more area is selected for protection and is higher when higher spatial resolutions are employed. 4. Adopting coarse resolutions reduced the overestimation of species protection and also halved the spatial incongruence between protected areas prioritized from range maps or habitat suitability models. However, this came at a very high cost, with an area of up to four times greater (12 M km² vs. 3 M km²) needed to adequately protect all species. 5. Synthesis and applications. Our findings demonstrate that adopting coarse resolutions in protected area planning results in unsustainable increases in costs, with limited benefits in terms of reducing the effect of commission errors in species range maps. We recommend that, if some level of uncertainty is acceptable to practitioners, using range maps at resolutions of 20-30 km is the best compromise for reducing the effect of commission errors while maintaining cost-efficiency in conservation analyses.

Aim The International Union for Conservation of Nature (IUCN) expert range maps and the Global Biodiversity Information Facility (GBIF) species occurrence data are commonly used to estimate species’ geographic range. Macroecological studies often cross‐reference geographic range data with a climate dataset, to extract the mean environmental conditions encountered by a species within its geographic range. We aimed to assess the consistency of the environmental data estimates derived from IUCN versus GBIF geographic range data, and to test whether such differences may affect estimates of ecogeographical relationships, such as Bergmann's rule. Location Worldwide. Time period Around 2000. Taxa Rodents (Rodentia). Methods We first assessed the correlation between environmental data estimates (19 bioclimatic variables and elevation) derived from IUCN versus GBIF geographic range data of 1,315 rodent species, comparing how range size, conservation status, habitat, zoogeographic realm or elevation changed these correlations. Then, we compared the association between body mass and climate (mean temperature and precipitation) when the latter are derived from IUCN or GBIF data. Results There was high correlation between environmental data estimates derived from IUCN versus GBIF data, especially when excluding GBIF occurrences outside of IUCN polygons. Species’ characteristics, or using the mean or median, did not change the consistency between estimates. Overall, GBIF occurrence data and IUCN range maps produced similar patterns of body mass—climate correlations. Main conclusions At the large spatial and taxonomic scale employed in this study, there does not seem to be any considerable differences in the average environmental data estimates derived from IUCN versus GBIF geographic range data. This result indicates that both sources of geographic range data could be used independently or in concert for macroecological inferences that involve summarizing species’ niches by a single estimate of the average of their used environments.

Aim Species distribution maps are essential for assessing extinction risk and guiding conservation efforts. However, most come sourced as expert‐drawn range maps with known issues of accuracy or are developed with overly complex modelling procedures. Thus, data‐driven alternatives that are accessible and reliable are a welcome addition to the spatial conservation toolkit. Here, we developed a geospatial workflow to refine the distribution of a species from its extent of occurrence (EOO) to area of habitat (AOH) within the species range map. The range maps are produced with an inverse distance weighted (IDW) interpolation procedure using presence and absence points derived from primary biodiversity data. Location The Americas (North, South, Central America and the Caribbean). Methods As a case study, we mapped the distribution of 723 resident forest birds in the Americas and assessed their performance in comparison with expert‐drawn range maps. We evaluated differences in accuracy, spatial overlap, range map size and derived AOH. Results The geospatial workflow generated IDW range maps with a higher overall accuracy (87% versus 62%) and fewer errors of omission (<1%) and commission (14%) than the expert range maps (28% both errors). The spatial overlap between both datasets was low (35%), but the agreement increased in areas of high probability of occurrence (68%). We did not find significant differences in range size, but the AOH derived from the expert‐drawn range maps was consistently smaller than the estimates from the IDW range maps. Main Conclusions Our geospatial workflow provides a straightforward approach to accurately map species ranges and the estimation of area of habitat (AOH) for conservation planning and decision‐making. Conversely, procedures that refine expert‐drawn range maps to obtain AOH risk producing biased estimates for local‐scale applications.

Aim A limitation of species distribution models (SDMs) is that species with low sample sizes are difficult to model. Yet, it is often important to know the habitat associations of poorly known species to guide conservation efforts. Techniques have been proposed for modelling species’ distributions from a few records, but their performance relative to one another has not been compared. Because these models are built and evaluated with small data sets, sampling error could cause severely biased sampling in environmental space. As a result, SDMs are likely to underpredict geographic distributions given small sample sizes. We perform the first comparison of methods explicitly promoted or developed for predicting the geographic ranges of species with very low sample sizes. Location North Carolina, USA. Taxon South Mountains Grey‐cheeked Salamander (Plethodon meridianus). Methods Using the sparse, existing georeferenced records of P. meridianus, we built SDMs using a range of methods that previous researchers have argued should work for low sample sizes. We then tested each SDM’s ability to accurately predict independent survey data that were not georeferenced prior to our study. We compared SDMs using omission error and AUC. Results Roughly half of the models successfully predicted survey records in the range centre, and all models had high omission error rates in the range exterior. In the range of interior or exterior, the ‘ensemble of small models’ technique produced SDMs with high omission error rates. Spatial filtering had a negligible impact on model performance. Most, but not all, models outperformed predictions using distance from known populations. Using one of the best‐performing methods, we developed an improved range map of P. meridianus. Main Conclusions Geographically peripheral populations were difficult to predict for all SDMs, though some methods were clearly inferior for our data set. We recommend that when sample sizes are low, researchers use Maxent with species‐specific model settings.

Aim: To determine whether the method used to build distributional maps from raw data influences the representation of two principal macroecological patterns: the latitudinal gradient in species richness and the latitudinal variation in range sizes (Rapoport's rule). Location: World-wide. Methods: All available distribution data from the Global Biodiversity Information Facility (GBIF) for those fish species that are members of orders of fishes with only marine representatives in each order were extracted and cleaned so as to compare four different procedures: point-to-grid (GBIF maps), range maps applying an α-shape [GBIF-extent of occurrence (EOO) maps], the MaxEnt method of species distribution modelling (GBIF-MaxEnt maps) and the MaxEnt method but restricted to the area delimited by the α-shape (GBIF-MaxEnt-restricted maps). Results: The location of hotspots and the latitudinal gradient in species richness or range sizes are relatively similar in the four procedures. GBIF-EOO maps and most GBIF-MaxEnt-maps provide overestimations of species richness when compared with those present in a priori well-surveyed cells. GBIF-EOO maps seem to provide more reasonable world macroecological patterns. MaxEnt can erroneously predict the presence of species in environmentally similar cells of another hemisphere or in other regions that lie outside the range of the species. Limiting this overpredictive capacity, as in the case of GBIF-MaxEnt-restricted maps, seems to mimic the frequency of observations derived from a simple point-to-grid procedure, with the utility of this procedure consequently being limited. Main conclusions: In studies of macroecological patterns at a global scale, the simple α-shape method seems to be a more parsimonious option for extrapolating species distributions from primary data than are distribution models performed indiscriminately and automatically with MaxEnt. GBIF data may be used in macroecological patterns if original data are cleaned, autocorrelation is corrected and species richness figures do not constitute obvious underestimations. Efforts therefore should focus on improving the number and quality of records that can serve as the source of primary data in macroecological studies.

Aim Species distribution models (SDMs) use the locations of collection records to map the distributions of species, making them a powerful tool in conservation biology, ecology and biogeography. However, the accuracy of range predictions may be reduced by temporally autocorrelated biases in the data. We assess the accuracy of SDMs in predicting the ranges of tropical plant species on the basis of different sample sizes while incorporating real-world collection patterns and biases. Location Tropical South American moist forests. Methods We use dated herbarium records to model the distributions of 65 Amazonian and Andean plant species. For each species, we use the first 25, 50, 100, 125 and 150 records collected and available for each species to analyse changes in spatial aggregation and climatic representativeness through time. We compare the accuracy of SDM range estimates produced using the time-ordered data subsets to the accuracy of range estimates generated using the same number of collections but randomly subsampled from all available records. Results We find that collections become increasingly aggregated through time but that additional collecting sites are added resulting in progressively better representations of the species' full climatic niches. The range predictions produced using time-ordered data subsets are less accurate than predictions from random subsets of equal sample sizes. Range predictions produced using time-ordered data subsets consistently underestimate the extent of ranges while no such tendency exists for range predictions produced using random data subsets. Main conclusions These results suggest that larger sample sizes are required to accurately map species ranges. Additional attention should be given to increasing the number of records available per species through continued collecting, better distributed collecting, and/or increasing access to existing collections. The fact that SDMs generally under-predict the extent of species ranges means that extinction risks of species because of future habitat loss may be lower than previously estimated.