Range Maps Research Articles

ST-DepthNet: A Spatio-Temporal Deep Network for Depth Completion Using a Single Non-Repetitive Circular Scanning Lidar

In this paper, we propose a novel depth image completion technique based on sparse consecutive measurements of a non-repetitive circular scanning (NRCS) Lidar, demonstrating the capabilities of a new, compact, and accessible sensor technology for dense range mapping of highly dynamic scenes. Our deep network called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ST-DepthNet</i> is composed of a spatio-temporally (ST) extended U-Net architecture, which accepts a very sparse range data sequence as input and produces a dense depth image stream of the same field-of-view ensuring a high level of spatial details and accuracy. For evaluation, we have constructed a new urban dataset, that – to our best knowledge as the first open Benchmark in this field – comprises various simulated and real-world NRCS Lidar data samples, allowing us to simultaneously train our model on synthetic data with Ground Truth, and to validate the result via real NRCS Lidar measurements. Using this new dataset, we have shown the superiority of our method against a densified depth map obtained from the raw sensor stream, and against two independent state-of-the-art deep-learning based Lidar-only depth completion methods.

A solution for perfect bioclimate envelopes that are imperfect for extirpated species

Present-day species distributions modeled with climate variables cannot provide potential future climate space for species that have contracted in range due to extirpations, regardless of abundant sample sizes within current ranges. My objective was to examine effects of range contractions on modeling of species to determine suitable space under climate change, exploring different approaches to modeling based on historical range maps. As examples of this issue, I estimated the current and future bioclimate envelopes of American bison (Bison bison) and elk (Cervus elaphus) from their current reduced ranges in the western United States compared to historical ranges immediately before extirpation. One solution for bioclimate envelope modeling is to generate presence samples from the historical range and pseudoabsence samples from outside of the historical range. By using the fullest climate space, the models identified areas of future suitable climate space that otherwise would be underpredicted (10%-27% of climate space, for these two examples) based on current ranges of species that have been extirpated from their historical range. Range contraction substantially reduced predictions of suitable climatic space under climate change. Therefore, species need to be evaluated for range extirpation before determining potential impacts of climate change on biodiversity conservation.

Data error propagation in stacked bioclimatic envelope models.

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence-only (Mahalanobis distance), presence-background (MAXENT), and presence-absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen-based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling.

A multimodal scanner coupling XRF, UV–Vis–NIR photoluminescence and Vis–NIR–SWIR reflectance imaging spectroscopy for cultural heritage studies

AbstractScanning x‐ray fluorescence (MA‐XRF), Reflectance Imaging Spectroscopy (RIS) and Luminescence Imaging Spectroscopy (LIS) are scientific methods extensively used in the investigation of artworks in Cultural Heritage Science. Nevertheless, these three highly complementary imaging methods have never been implemented in a single instrument. In this paper, we present a scanning system that is the first to simultaneously combine MA‐XRF, RIS, and LIS techniques, generating spatially aligned hyperspectral datasets. The specifications of the instrument and of the collected datacubes are described, together with the processing pipeline developed for the coupled exploitation and interpretation. The operation of this instrument, combining XRF with RIS and LIS mapping in an extended range (400–2500 nm and 200–1000 nm, respectively), is exemplified on an anonymous test painting, studied previously. A focus on a restored area of this painting allows to illustrates the complementarity of the three aforementioned methods, for the visualization and characterization of pigments, varnish, and binders.

Geographic Range vs. Occurrence Records in Plant Distribution Mapping: The Case of Arbutus in the Old World

Species mapping methods play a central role in biogeographical questions, as they may generate a domino effect on further works based on species distribution. In light of the massive recent increase in the availability of online occurrence data, we highlight the strengths and limitations of the mapping methods most widely used to display the geographic distribution of plants, namely geographic range maps and occurrence record maps. We use the modern distribution of the genus Arbutus in western Eurasia, North Africa, and Macaronesia, for which no occurrence record map has been published yet, to discuss critical issues in data collection and representation. The occurrence record map of A. unedo, A. andrachne, A. canariensis, and A. pavarii shows how well this mapping method captures the details of peripheral and isolated stands as well as the variability of population density. A number of biogeographical issues are addressed by this approach, including the determination of the chorological centre of gravity in relation to historical dynamics, genetic patterns in relation to range porosity, and the autochthony status of marginal stands. These issues constitute the necessary foundation for additional palaeobotanical research and ecological modelling to investigate the past-to-future dynamics of Arbutus and other species of the Mediterranean–Atlantic area.

Out of thin air: surveying tropical bat roosts through air sampling of eDNA.

Understanding roosting behaviour is essential to bat conservation and biomonitoring, often providing the most accurate methods of assessing bat population size and health. However, roosts can be challenging to survey, e.g., physically impossible to access or presenting risks for researchers. Disturbance during monitoring can also disrupt natural bat behaviour and present material risks to the population such as disrupting hibernation cycles. One solution to this is the use of non-invasive monitoring approaches. Environmental (e)DNA has proven especially effective at detecting rare and elusive species particularly in hard-to-reach locations. It has recently been demonstrated that eDNA from vertebrates is carried in air. When collected in semi-confined spaces, this airborne eDNA can provide remarkably accurate profiles of biodiversity, even in complex tropical communities. In this study, we deploy novel airborne eDNA collection for the first time in a natural setting and use this approach to survey difficult to access potential roosts in the neotropics. Using airborne eDNA, we confirmed the presence of bats in nine out of 12 roosts. The identified species matched previous records of roost use obtained from photographic and live capture methods, thus demonstrating the utility of this approach. We also detected the presence of the white-winged vampire bat (Diaemus youngi) which had never been confirmed in the area but was long suspected based on range maps. In addition to the bats, we detected several non-bat vertebrates, including the big-eared climbing rat (Ototylomys phyllotis), which has previously been observed in and around bat roosts in our study area. We also detected eDNA from other local species known to be in the vicinity. Using airborne eDNA to detect new roosts and monitor known populations, particularly when species turnover is rapid, could maximize efficiency for surveyors while minimizing disturbance to the animals. This study presents the first applied use of airborne eDNA collection for ecological analysis moving beyond proof of concept to demonstrate a clear utility for this technology in the wild.

Integrating presence‐only and presence–absence data to model changes in species geographic ranges: An example in the Neotropics

AbstractAimAnthropogenic changes such as land use and climate change affect species' geographic ranges, causing range shifts, contractions, or expansions. However, data on range dynamics are insufficient, heterogeneous, and spatially and temporally biased in most regions. Integrated species distribution models (IDMs) offer a solution as they can complement good quality presence‐absence data with opportunistically collected presence‐only data, simultaneously accounting for heterogeneous sampling effort. However, these methods have seen limited use in the estimation of temporal change of geographic ranges and are not yet widespread as they have a steep learning curve. Here we present a generalisable model and case example.LocationNeotropics ‐ Latin America.Taxon Herpailurus yagouaroundi. MethodsUsing data on presence‐absence and presence‐only on the jaguarundi (Herpailurus yagouaroundi), we modelled the species distribution at two time periods (2000–2013 and 2014–2021) using a Bayesian model based on Poisson point process in JAGS. Our model integrates the different data types while accounting for varying sampling effort and spatial effect. We predicted the species range at the two time periods and quantified their changes.ResultsBetween the two time periods, the jaguarundi has contracted its southern and northern range limits towards the equator but expanded its area of distribution over the entire species' range. Also, our results show that modelled geographic range, of either time period, is not entirely consistent with the current expert range map from IUCN.Main ConclusionsOur modelling approach provides a working example with the potential to address data gaps and biases in other taxa and regions. Given the increasing number of incidental data being generated by community‐derived initiatives in Latin America, IDMs can become a valuable source for species distribution modelling in the region. This is the first application of the IDM approach with temporal dimension and over the entire species' geographic range.

Human appropriation of net primary production as driver of change in landscape-scale vertebrate richness.

Land use is the most pervasive driver of biodiversity loss. Predicting its impact on species richness (SR) is often based on indicators of habitat loss. However, the degradation of habitats, especially through land-use intensification, also affects species. Here, we evaluate whether an integrative metric of land-use intensity, the human appropriation of net primary production, is correlated with the decline of SR in used landscapes across the globe. Global. Present. Birds, mammals and amphibians. Based on species range maps (spatial resolution: 20 km × 20 km) and an area-of-habitat approach, we calibrated a "species-energy model" by correlating the SR of three groups of vertebrates with net primary production and biogeographical covariables in "wilderness" areas (i.e., those where available energy is assumed to be still at pristine levels). We used this model to project the difference between pristine SR and the SR corresponding to the energy remaining in used landscapes (i.e., SR loss expected owing to human energy extraction outside wilderness areas). We validated the projected species loss by comparison with the realized and impending loss reconstructed from habitat conversion and documented by national Red Lists. Species-energy models largely explained landscape-scale variation of mapped SR in wilderness areas (adjusted R 2-values: 0.79-0.93). Model-based projections of SR loss were lower, on average, than reconstructed and documented ones, but the spatial patterns were correlated significantly, with stronger correlation in mammals (Pearson's r=0.68) than in amphibians (r=0.60) and birds (r=0.57). Our results suggest that the human appropriation of net primary production is a useful indicator of heterotrophic species loss in used landscapes, hence we recommend its inclusion in models based on species-area relationships to improve predictions of land-use-driven biodiversity loss.

Primary motor area-related injury of anterior central gyrus in Parkinson's disease with dyskinesia: A study based on MRS and Q-space

IntroductionBy using magnetic resonance spectroscopy (MRS) and Q-Space imaging technology, this research analyzes the imaging characteristics of white matter fibers in the primary motor cortex and posterior limbs of the subcortical internal capsule in parkinsonian patients with motor disorders. The correlation among the changes in axonal function and structure in the cerebral cortex and subcortical cortex and motor disorder is further revealed. MethodsFirst, motor function and clinical condition of 20 patients with Parkinson's disease is assessed the third section of the Unified Parkinson's Scale and H&Y Parkinson's Clinical Staging Scale. Magnetic resonance (MR) scanning is performed with 1H-MRS. Secondly, the range maps of N-acetylaspartic acid (NAA), Choline (Cho), and Creatine (Cr) in the region of interest (the primary motor area of anterior central cortex gyrus, i.e. M1 region) are obtained, and the ratios of NAA/Cr and Cho are calculated. Third, Q-Space MR diffusion imaging technique is used to collect Q-Space images, and a Dsi-studio workstation is used to post-process the images. The fraction anisotropic (FA), generalized fraction anisotropic (GFA), and apparent diffusion coefficient (ADC) parameters of Q-Space in the primary motor cortex and the region of interest in the posterior limb of the internal capsule are obtained. Finally, the parameters of MRS and Q-Space in the experimental group and the control group are further analyzed by SPSS statistical software. ResultsAfter assessing with Parkinson's score scale, there is obvious motor dysfunction in the experimental group. The average clinical stage of H&Y is 3.0 ± 0.31. In the analysis of MRS data, the ratio of NAA/Cr in the primary motor area of the anterior central gyrus in the experimental group is significantly lower than that in the control group (P < 0.05). In the ADC map obtained by Q-Space imaging technique, the ADC value in the primary motor area of the anterior central gyrus in the experimental group is higher than that in the control group (P < 0.05), and the difference is statistically significant (P < 0.05). There is no significant difference between the experimental group and the control group (P > 0.05) in FA and GFA values of the posterior limb of capsule to characterize the characteristics of white matter fibers. ConclusionsIn parkinsonian patients with motor dysfunction, there are apparent functional and structural changes in the primary motor area neurons and peripheral white matter of the anterior central gyrus, and no obvious damage to the axonal structure of the descending fibers in the cortex.

Clustering future scenarios based on predicted range maps

Abstract Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions. We propose an interpretable and flexible two‐step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method. We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied. The differences between species‐based and climate‐based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios.

Linking different resources to recognize vascular epiphyte richness and distribution in a mountain system in southeastern Mexico

Mesoamerican mountains are important centers of endemism and diversity of epiphytes. The Sierra Madre of Chiapas in southeastern Mexico is a mountainous region of great ecological interest due to its high biological richness. We present the first checklist of epiphytes for this region based on a compilation of various information sources. In addition, we determined the conservation status for each species based on the Mexican Official Standard (NOM-059-SEMARNAT-2010), endemism based on geopolitical boundaries, spatial completeness with inventory completeness index, richness distribution with range maps, and the relationship between climatic variables (temperature and rainfall) with species richness using generalized additive models. Our dataset includes 9,799 records collected between 1896 and 2017. Our checklist includes 708 epiphytes within 160 genera and 26 families; the most species-rich family was Orchidaceae (355 species), followed by Bromeliaceae (82) and Polypodiaceae (79). There were 74 species within a category of risk and 59 species considered endemic. Completeness of epiphyte richness suggests that sampling is still largely incomplete, particularly in the lower parts of the mountain system. Species and family range maps show the highest richness at high elevations, while geographically richness increases towards the southeast. Epiphyte richness increases with increased rainfall, although a unimodal pattern was observed along the temperature gradient with a species richness peak between 16 and 20 C°. The Sierra Madre of Chiapas forms a refuge to more than 40% of all epiphytes reported for Mexico and its existing network of protected areas overlaps with the greatest epiphyte richness.

Change in black bear range and distribution in Florida using two decadal datasets from 2001–2020

AbstractAmerican black bears (Ursus americanus) in Florida have increased in abundance from historically low numbers and currently number &gt;4,000 across 7 subpopulations. Biologists monitor the range of black bears to track their recovery for conservation and management efforts. We estimated geographic range of black bears in Florida for historical (2001–2010) and contemporary (2011–2020) periods using 12 sources of occurrence data collected from wildlife professionals and the public. We reduced data sets by subsampling protocols to account for localized spatiotemporal biases and for possible differences in sampling effort from increases in the human population between modeling periods. We developed models at 2 levels: a generalized boundary (range extent) and a more detailed delineation (occupied range). We developed range extent using a concave hull model and occupied range using kernel density estimation with a corresponding 97.5% isopleth. Between modeling periods, range extent increased by 13.4% and occupied range increased by 11.3%, with both range levels expanding and contracting in certain areas. The ranges indicated improved connectivity among bear subpopulations. We also produced a range map for research, management, and public use built upon contemporary data that partitioned the state into 4 levels representing the relative frequency of bear use (frequent, common, occasional, and rare). Range map levels at the occupied range, and especially frequent use areas, help focus bear research efforts (e.g., placement of hair corrals), management efforts (e.g., assess varying levels of risk to the public of bear conflicts), and conservation efforts (e.g., focusing habitat protection in areas with high use by bears).

Mapping nationally and globally at-risk species to identify hotspots for (and gaps in) conservation.

Protecting habitat of species at risk is critical to their recovery, but can be contentious. For example, protecting species that are locally imperilled but globally common is often thought to distract from protecting globally imperilled species. However, such perceived trade-offs are based on the assumption that threatened groups have little spatial overlap, which is rarely quantified. We compiled range maps of terrestrial species at risk in Canada to assess the geographic overlap of nationally and globally at-risk species with each other, among taxonomic groups, and with protected areas. While many nationally at-risk taxa only occur in Canada at their northern range edge, they are not significantly more peripheral in Canada than globally at-risk species. Further, 56% of hotspots of nationally at-risk taxa are also hotspots of globally at-risk species, undercutting the perceived trade-off in their protection. While strong spatial overlap across threat levels and taxa should facilitate efficient habitat protection, less than 7% of the area in Canada's at-risk hotspots is protected, and two-thirds of nationally and globally at-risk species in Canada have less than 10% of their Canadian range protected. Our results counter the perception that protecting nationally versus globally at-risk species are at odds, and identify critical areas to target as Canada strives to increase its protected areas and promote recovery of species at risk.

Beyond latitude: Temperature, productivity and thermal niche conservatism drive global body size variation in Odonata

AbstractAimSo far, latitudinal body size clines have been discussed primarily in the context of thermoregulation, sensu Bergmann. However, body size patterns are ambiguous in ectotherms, and this heterogeneity remains poorly understood. We tested whether Bergmann's rule and the resource availability rule, which states that energetic requirements determine species body size, apply to damselflies and dragonflies (Odonata). Furthermore, we hypothesized that the contrasting effects of thermoregulation and resource availability (e.g., productivity) can obscure the overall gradient in body size variation.LocationGlobal.Time periodContemporary.Major taxa studiedOdonata.MethodsUsing data for 43% of all odonate species described so far, we tested our hypotheses in phylogenetically and spatially comparative analyses at assemblage and species levels. For the distribution data, we integrated expert range maps and ecoregional ranges based on all available occurrence records. To distinguish between long‐term and evolutionarily recent responses of environmental drivers in body size, we constructed a phylogenetically informed classification of all odonate species and decomposed the body size into its phylogenetic and specific components for our subset of species.ResultsWe documented a weak positive relationship between body length and latitude but found strong and contrasting effects for temperature between dragonflies and damselflies and consistent positive effects for productivity that explained 35–57% of body size variation. Moreover, we showed a strong phylogenetic signal in sized‐based thermoregulation that shaped the distribution of dragonflies, but not of damselflies.Main conclusionsWe concluded that temperature, productivity and conservatism in size‐based thermoregulation synergistically determine the distribution of ectotherms, while the taxon‐specific importance of these factors can lead to contrasting and weak latitude–size relationships. Our results reinforce the importance of body size as a determinant of species distributions and responses to climate change.

The genus Astragalus (Leguminosae: Papilionoideae: Galegeae) in Mexico

Astragalus is the most diverse genus within flowering plants with almost 2500 species, it is widely distributed around the world, very abundant in temperate regions of the Northern Hemisphere, in central and western Asia, Turkey, Iran, and Afghanistan, central Asia, Europe, Mongolia, Siberia, northeastern China, and Japan. In the American continent it is especially abundant in Canada, western United States, Mexico, and South America. Given the diversity of Astragalus species present in Mexico, the recent description of at least 15 new species for this country, the absence of a recent study that encompasses all the new findings and that the last general study for Mexico dates back almost 60 years, we consider necessary a new taxonomic synopsis that encompasses all the new information, which is presented here. The study records 102 species of Astragalus in Mexico. This work is based essentially on the review of collections of specimen samples, type specimens in national and foreign herbaria, data bases, and the collection of specimens of this genus by the authors in the last 40 years and that covers practically the entire area where this genus is distributed in Mexico. In this study we recorded 102 species and 46 infraspecific taxa; seventy one species and 17 varieties are endemic to Mexico. Baja California (32), Chihuahua (25), Durango (23), Sonora (22), Coahuila (20), San Luis Potosí (17), Nuevo León (17), and Zacatecas (15) are the states with the greatest species richness. In Mexico, the diversity of Astragalus species decreases from north to south and from west to east. Most of the species of Astragalus in Mexico are distributed in the mountains, followed by low and arid plains, including coastal dunes, those last ones, exclusively of the northwestern region. There are no records of any Astragalus species in Campeche, Tabasco, Quintana Roo or Yucatan. The dichotomous keys are based mainly on the color of flowers, the shape of the pod and if the fruit is sessile or stipitate. For each species is included also a morphological description, habitat, altitudinal range, and distribution map.

A novel real-time multi-person activity recognition system using millimeter-wave SISO radar data

Most millimeter-wave radar-based multi-person activity recognition (MPAR) systems are the point cloud-based, which require multiple-input-multiple-output (MIMO) sensors and cause high cost and power consumption of the systems. To realize an MPAR system using only the single-input-single-output (SISO) radar, a novel recognition algorithm is proposed in this paper, which realizes MPAR through a newly designed SVM classification model based on range and Doppler feature maps. A new feature map synthesis method is proposed for efficient generation of training data and a real-time recognition system is built to evaluate the performance of the algorithms. The experimental results for three scenarios show that the proposed recognition algorithm outperforms the conventional point cloud-based algorithms in terms of average recognition accuracy.

Estimating the distribution of Oryzomys palustris, a potential key host in expanding rickettsial tick-borne disease risk.

Increasingly, geographic approaches to assessing the risk of tick-borne diseases are being used to inform public health decision-making and surveillance efforts. The distributions of key tick species of medical importance are often modeled as a function of environmental factors, using niche modeling approaches to capture habitat suitability. However, this is often disconnected from the potential distribution of key host species, which may play an important role in the actual transmission cycle and risk potential in expanding tick-borne disease risk. Using species distribution modeling, we explore the potential geographic range of Oryzomys palustris, the marsh rice rat, which has been implicated as a potential reservoir host of Rickettsia parkeri, a pathogen transmitted by the Gulf Coast tick (Amblyomma maculatum) in the southeastern United States. Due to recent taxonomic reclassification of O. palustris subspecies, we reclassified geolocated collections records into the newer clade definitions. We modeled the distribution of the two updated clades in the region, establishing for the first time, range maps and distributions of these two clades. The predicted distribution of both clades indicates a largely Gulf and southeastern coastal distribution. Estimated suitable habitat for O. palustris extends into the southern portion of the Mid-Atlantic region, with a discontinuous, limited area of suitability in coastal California. Broader distribution predictions suggest potential incursions along the Mississippi River. We found considerable overlap of predicted O. palustris ranges with the distribution of A. maculatum, indicating the potential need for extended surveillance efforts in those overlapping areas and attention to the role of hosts in transmission cycles.

Mismatch between IUCN range maps and species interactions data illustrated using the Serengeti food web.

Range maps are a useful tool to describe the spatial distribution of species. However, they need to be used with caution, as they essentially represent a rough approximation of a species' suitable habitats. When stacked together, the resulting communities in each grid cell may not always be realistic, especially when species interactions are taken into account. Here we show the extent of the mismatch between range maps, provided by the International Union for Conservation of Nature (IUCN), and species interactions data. More precisely, we show that local networks built from those stacked range maps often yield unrealistic communities, where species of higher trophic levels are completely disconnected from primary producers. We used the well-described Serengeti food web of mammals and plants as our case study, and identify areas of data mismatch within predators' range maps by taking into account food web structure. We then used occurrence data from the Global Biodiversity Information Facility (GBIF) to investigate where data is most lacking. We found that most predator ranges comprised large areas without any overlapping distribution of their prey. However, many of these areas contained GBIF occurrences of the predator. Our results suggest that the mismatch between both data sources could be due either to the lack of information about ecological interactions or the geographical occurrence of prey. We finally discuss general guidelines to help identify defective data among distributions and interactions data, and we recommend this method as a valuable way to assess whether the occurrence data that are being used, even if incomplete, are ecologically accurate.

Socio-ecological gap analysis to forecast species range contractions for conservation

Conservation requires both a needs assessment and prioritization scheme for planning and implementation. Range maps are critical for understanding and conserving biodiversity, but current range maps often omit content, negating important metrics of variation in populations and places. Here, we integrate a myriad of conditions that are spatially explicit across distributions of carnivores to identify gaps in capacity necessary for their conservation. Expanding on traditional gap analyses that focus almost exclusively on quantifying discordance in protected area coverage across a species' range, our work aggregates threat layers (e.g., drought, human pressures) with resources layers (e.g., protected areas, cultural diversity) to identify gaps in available conservation capacity (ACC) across ranges for 91 African carnivores. Our model indicated that all species have some portion of their range at risk of contraction, with an average of 15 percentage range loss. We found that the ACC differed based on body size and taxonomy. Results deviated from current perceptions of extinction risks for species with an International Union for Conservation of Nature (IUCN) threat status of Least Concern and yielded insights for species categorized as Data Deficient. Our socio-ecological gap analysis presents a geospatial approach to inform decision-making and resource allocation in conservation. Ultimately, our work advances forecasting dynamics of species' ranges that are increasingly vital in an era of great socio-ecological change to mitigate human-wildlife conflict and promote inclusive carnivore conservation across geographies.

AI and Blockchain-based source code vulnerability detection and prevention system for multiparty software development

With the growing demand for application software, there is a race among industries to develop software as quickly as possible. However, maintaining pace and ensuring bug-free software has become increasingly challenging in a work-from-home arrangement as software developers are not under constant supervision. It increases the possibility of buggy products, and traditional testing techniques fail to provide optimal performance. We propose an Artificial Intelligence (AI) and blockchain-based novel decentralized software testing system. The proposed system aims to detect and prevent vulnerable code by synergizing deep learning capabilities and smart-contract-powered blockchain. The vulnerability detection is performed automatically without relying on manually written rules. We propose a non-vulnerability score range map to classify the source code. Furthermore, we integrate an InterPlanetary File System (IPFS) to ensure efficient storage over the blockchain. We conduct a testbed-based experiment to demonstrate the effectiveness of AI and blockchain integration for secure code development and testing.