Spatial Bias Research Articles

A snapshot of online wildlife trade: Australian e-commerce trade of native and non-native pets

The international trade of non-domesticated pets impacts both conservation and biosecurity via the harvest and release of live animals beyond their native distributions. The extent to which individual countries mitigate these impacts via regulation of trade is inconsistent, as is their capacity to monitor internet facilitated trade. We investigated the online trade of vertebrate pets within Australia, a country with a reputation for relatively stringent pet-importation regulations and world-class border biosecurity. Using semi-automated data mining (i.e., webscraping) techniques, we collected online pet trade data over the course of 14 weeks from 12 Australian e-commerce platforms selected using an a priori set of search terms. We analysed spatial, temporal and taxonomic biases in trade and identified instances of high rates of trade in: (i) threatened species, (ii) non-native species, (iii) and species not permissible for live import. We identified over 100,000 individual live animals across 1192 species, including: 667 non-native species for sale within Australia from 03/12/2019 to 20/03/2020 (mammals were excluded from our analysis). Our findings constitute a much greater scale (in terms of abundance and richness) of non-native species trade than previously recorded in Australia. Substantial changes to legislative control of domestically traded pets are needed at the national level to reduce the volume of non-native pets that may contribute to the establishment of invasive species in Australia. We suggest that contemporary examples of permit systems applied to native taxa may provide a valuable template for the implementation of such changes.

Multiscale Entropy-Based Surface Complexity Analysis for Land Cover Image Semantic Segmentation

Recognizing and classifying natural or artificial geo-objects under complex geo-scenes using remotely sensed data remains a significant challenge due to the heterogeneity in their spatial distribution and sampling bias. In this study, we propose a deep learning method of surface complexity analysis based on multiscale entropy. This method can be used to reduce sampling bias and preserve entropy-based invariance in learning for the semantic segmentation of land use and land cover (LULC) images. Our quantitative models effectively identified and extracted local surface complexity scores, demonstrating their broad applicability. We tested our method using the Gaofen-2 image dataset in mainland China and accurately estimated multiscale complexity. A downstream evaluation revealed that our approach achieved similar or better performance compared to several representative state-of-the-art deep learning methods. This highlights the innovative and significant contribution of our entropy-based complexity analysis and its applicability in improving LULC semantic segmentations through optimal stratified sampling and constrained optimization, which can also potentially be used to enhance semantic segmentation under complex geo-scenes using other machine learning methods.

Assessing differences in safety perceptions using GeoAI and survey across neighbourhoods in Stockholm, Sweden

The safety perception of the built environment, rather than the sheer number of crimes and incivility behavior, is a fundamental driver of public policies intended to improve urban safety. Traditional surveys often capture neighborhood residents’ perceived safety, but may not fully reflect the perceptions of people who are unfamiliar with the area. In this study, focused on the city of Stockholm, Sweden, we develop a geospatial artificial intelligence (GeoAI) approach using street view images and recruiting locals to create a measure of citywide residents’ safety perceptions. We compare the measures from the survey based on neighborhood residents’ responses with those from the GeoAI approach to better understand the relationship between these safety measures. We model the two forms of safety perceptions and their disparities (i.e., perception bias) as a function of the city’s land use and its socio-demographics. Results confirm that while the GeoAI-based measures better capture people’s instant impressions of the built environment across the city, the survey-based measures reflect their overall daily experiences of specific areas. Regions that appear to be economically vibrant and have inner-city streetscapes are perceived as safe places from visual appearance but are not always perceived as such by residents. Older adults tend to overestimate their likelihood of being victimized by crime, which may enlarge perception bias. The study concludes by critically assessing the potential ethical issues (e.g., spatial bias, population bias) in the proposed methodology and making suggestions for future research.

Assessing key influences on the distribution and life-history of Arctic and boreal Calanus: are online databases up to the challenge?

Despite the importance of calanoid copepods to healthy ecosystem functioning of the Arctic Ocean and Subarctic Seas, many aspects of their biogeography, particularly in winter months, remain unresolved. At the same time, online databases that digitize species distribution records are growing in popularity as a tool to investigate ecological patterns at macro scales. The value of such databases for Calanus research requires investigation - the long history of Calanus sampling holds promise for such databases, while conditions at high latitudes may impose limits through spatial and temporal biases. We collated records of three Calanus species (C. finmarchicus, C. glacialis, and C. hyperboreus) from the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF) providing over 230,000 unique records spanning 150 years and over 100 individual datasets. After quality control and cleaning, the latitudinal and vertical distribution of occurrences were explored, as well as the completeness of informative metadata fields. Calanus sampling was found to be temporally and spatially biased towards surfacemost layers (<10m) in spring and summer. Only 3.5% of records had an average collection depth ≥400m, approximately half of these in months important for diapause. Just over 40% of records lacked associated information on sampling protocol while 11% of records lacked life-stage information. OBIS data contained fields for maximum and minimum collection depth and so were subset into discrete “shallow summer” and “deep winter” life cycle phases and matched to sea-ice and temperature conditions. 23% of OBIS records north of 66° latitude were located in regions of seasonal sea-ice presence and occurrences show species-specific thermal optima during the shallow summer period. The collection depth of C. finmarchicus was significantly different to C. hyperboreus during the deep winter. Overall, online databases contain a vast number of Calanus records but sampling biases should be acknowledged when they are used to investigate patterns of biogeography. We advocate efforts to integrate additional data sources within online portals. Particular gaps to be filled by existing or future collections are (i) widening the spatial extent of sampling during spring/summer months, (ii) increasing the frequency of sampling during winter, particularly at depths below 400m, and (iii) improving the quality, quantity and consistency of metadata reporting.

Stronger spatial bias induced more by numbers in mind than numbers in eye: Evidence from event-related potentials

The relationship between number and space is an important issue in numerical cognition. The spatial-numerical association of response codes (SNARC) effect is a classic example of the association between numbers and spaces. It refers to the phenomenon whereby left-handed responses occur faster to small number and right-handed responses occur faster to large number. The current study explored the shared and distinct neural correlates of the SNARC effect considering numbers in eye and numbers in mind, by using event-related potentials (ERPs) technology. In each trial of the task, participants were asked to press freely one of two keys as a response to a number presented visually (numbers in eye) or via imagination (numbers in mind). The behavioral results indicated that the free-choice key presses were affected by the magnitudes of the numbers either in eye or in mind. Electrophysiological results observed that the SNARC effect appeared only in the 110–140 ms time window for numbers in eye. In contrast, for numbers in mind, the SNARC effect appeared during a longer time window (110–330 ms). These results suggest that both, numbers in eye and numbers in mind, can induce spatial bias at the early stimulus-representation stage, but the time duration of the spatial bias is longer for numbers in mind than numbers in eye. This may reflect a closer connection between numbers in mind and mental number line.

Spatial biases in approximate arithmetic are subject to sequential dependency effects and dissociate from attentional biases

The notion that mental arithmetic is associated with shifts of spatial attention along a spatially organised mental number representation has received empirical support from three lines of research. First, participants tend to overestimate results of addition and underestimate those of subtraction problems in both exact and approximate formats. This has been termed the operational momentum (OM) effect. Second, participants are faster in detecting right-sided targets presented in the course of addition problems and left-sided targets in subtraction problems (attentional bias). Third, participants are biased toward choosing right-sided response alternatives to indicate the results of addition problems and left-sided response alternatives for subtraction problems (Spatial Association Of Responses [SOAR] effect). These effects potentially have their origin in operation-specific shifts of attention along a spatially organised mental number representation: rightward for addition and leftward for subtraction. Using a lateralised target detection task during the calculation phase of non-symbolic additions and subtractions, the current study measured the attentional focus, the OM and SOAR effects. In two experiments, we replicated the OM and SOAR effects but did not observe operation-specific biases in the lateralised target-detection task. We describe two new characteristics of the OM effect: First, a time-resolved, block-wise analysis of both experiments revealed sequential dependency effects in that the OM effect builds up over the course of the experiment, driven by the increasing underestimation of subtraction over time. Second, the OM effect was enhanced after arithmetic operation repetition compared to trials where arithmetic operation switched from one trial to the next. These results call into question the operation-specific attentional biases as the sole generator of the observed effects and point to the involvement of additional, potentially decisional processes that operate across trials.

An immersive virtual reality system for ecological assessment of peripersonal and extrapersonal unilateral spatial neglect

BackgroundUnilateral spatial neglect (USN) is a debilitating neuropsychological syndrome that often follows brain injury, in particular a stroke affecting the right hemisphere. In current clinical practice, the assessment of neglect is based on old-fashioned paper-and-pencil and behavioral tasks, and sometimes relies on the examiner’s subjective judgment. Therefore, there is a need for more exhaustive, objective and ecological assessments of USN.MethodsIn this paper, we present two tasks in immersive virtual reality to assess peripersonal and extrapersonal USN. The tasks are designed with several levels of difficulty to increase sensitivity of the assessment. We then validate the feasibility of both assessments in a group of healthy adult participants.ResultsWe report data from a study with a group of neurologically unimpaired participants (N = 39). The results yield positive feedback on comfort, usability and design of the tasks. We propose new objective scores based on participant’s performance captured by head gaze and hand position information, including, for instance, time of exploration, moving time towards left/right and time-to-reach, which could be used for the evaluation of the attentional spatial bias with neurological patients. Together with the number of omissions, the new proposed parameters can result in lateralized index ratios as a measure of asymmetry in space exploration.ConclusionsWe presented two innovative assessments for USN based on immersive virtual reality, evaluating the far and the near space, using ecological tasks in multimodal, realistic environments. The proposed protocols and objective scores can help distinguish neurological patients with and without USN.

DeepFixCX: Explainable privacy‐preserving image compression for medical image analysis

AbstractExplanations of a model's biases or predictions are essential to medical image analysis. Yet, explainable machine learning approaches for medical image analysis are challenged by needs to preserve privacy of patient data, and by current trends in deep learning to use unsustainably large models and large datasets. We propose DeepFixCX for explainable and privacy‐preserving medical image compression that is nimble and performant. We contribute a review of the field and a conceptual framework for simultaneous privacy and explainability via tools of compression. DeepFixCX compresses images without learning by removing or obscuring spatial and edge information. DeepFixCX is ante‐hoc explainable and gives privatized post hoc explanations of spatial and edge bias without accessing the original image. DeepFixCX privatizes images to prevent image reconstruction and mitigate patient re‐identification. DeepFixCX is nimble. Compression can occur on a laptop CPU or GPU to compress and privatize 1700 images per second of size 320 × 320. DeepFixCX enables use of low memory MLP classifiers for vision data; permitting small performance loss gives end‐to‐end MLP performance over 70× faster and batch size over 100× larger. DeepFixCX consistently improves predictive classification performance of a Deep Neural Network (DNN) by 0.02 AUC ROC on Glaucoma and Cervix Type detection datasets, and can improve multi‐label chest x‐ray classification performance in seven of 10 tested settings. In all three datasets, compression to less than 5% of original number of pixels gives matching or improved performance. Our main novelty is to define an explainability versus privacy problem and address it with lossy compression.This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Commercial, Legal, and Ethical Issues > Security and Privacy Fundamental Concepts of Data and Knowledge > Big Data Mining

Minimizing Data Waste: Conservation in the Big Data Era

Minimizing Data Waste: Conservation in the Big Data Era

Finding patterns of atoll morphometrics at a range of spatial scales

Small Island Nations, often comprised of Atolls, are at considerable risk of climate change impacts from sea-level rise to coral acidification to increasing cyclone intensity; understanding how they will change in the coming century is vital for climate mitigation and resiliency. However, the morphology of atolls are not well quantified or summarized. In this work, we calculate modern atoll morphometrics on a global scale including 3,786 motu and 593 reef flats on 154 atolls. Temporal composites of Landsat imagery are created for 4 years (2015–2018), and are classified into motu, reef flat, open water/lagoon via unsupervised classification. Morphometrics, including widths, lengths, and area, are computed for each motu and reef flat of the atoll in Python, creating a database of atolls and their associated morphometrics. Consistency in automated processing of atoll morphometrics removes spatial and user bias, enabling a better understanding of geographic patterns of atoll area and other morphometrics. Distinct differences in the distribution of motu are seen on a regional scale in French Polynesia, while globally, wider reef flats and larger motu are found closest to the equator. Globally, there is a consistent reef flat width in front of large motu (>1 km length) of 184 ± 93 m (n = 724). Our atoll morphometric database creates a baseline of current atoll characteristics that can be expanded upon in the future and used for evaluating temporal changes to atoll islands.

Word-centred neglect dyslexia as an inhibitional deficit: A single case study

Word-centred neglect dyslexia is most commonly characterised as consequence of visuospatial neglect rather than an independent condition. However, recent research has suggested that this deficit may be dissociable from spatial attentional biases. This study aims to provide preliminary evidence investigating alternative mechanisms which could account for cases of word-centred neglect dyslexia which cannot be explained by visuospatial neglect.Patient EF is a chronic stroke survivor who presented with clear right-lateralised word-centred neglect dyslexia in conjunction with severe left egocentric neglect and left hemianopia following a right PCA stroke. The severity of EF's neglect dyslexia was not found to be affected by factors which modulate the severity of visuospatial neglect. EF demonstrated an intact ability to identify all letters in words, but reliably committed neglect dyslexia errors when subsequently reading the same words as a whole. EF did not exhibit neglect dyslexic impairment in standardised spelling, word-meaning matching, and word-picture matching tasks. Critically, EF exhibited marked cognitive inhibition impairment and committed neglect dyslexia errors which were characterised by misreading less familiar target words as more familiar responses.This behavioural pattern cannot be clearly accounted for by theories which characterize word-centred neglect dyslexia as a consequence of neglect. Instead, this data suggests that this case of word-centred neglect dyslexia may be related to a deficit of cognitive inhibition. Overall, these novel findings call for reevaluation of the dominant model of word-centred neglect dyslexia.

Point‐scale habitat and weather patterns influence the distribution of regal fritillaries in the central United States

AbstractConservationists and scientists throughout the world consider butterflies important indicators of broadscale factors affecting global biodiversity. And like other facets of biodiversity, declines in many butterfly populations appear related to human activities, and the potential of their extinction could jeopardize key ecosystem services. The regal fritillary (Argynnis idalia), a butterfly associated with tall‐ and mixed‐grass prairies in the United States, is a species currently being considered for federal protection under the Endangered Species Act. As with many butterflies, a paucity of data makes evidence‐based assessments of their population and distributional status difficult. Citizen science data provide one possible source of broadscale information about this species, but using these data is not simple because of spatial scaling and sampling bias problems. Here, we developed a model to analyze a data set from the citizen science program called the Fourth of July Butterfly Count (4JC) to make inferences about regal fritillary abundance in the Great Plains and Midwest regions of the United States. More specifically, we aimed to determine the extent to which grassland fragmentation, spring weather conditions, or other spatial factors were correlated with the intensity of use within 4JC survey areas. Our findings indicated that regal fritillaries used locations surrounded by grassland and with minimal forest cover. We included smoothing spline terms in our model, which indicated other sources of spatial structuring in these data. It is possible that these additional sources of variation could reflect variation in habitat quality. We also found that local averaged spring weather variables indicated that regal fritillary abundance varied over a wider range of precipitation conditions, but a narrower range of temperature conditions. Further analyses indicated that a systematic increase in temperature from climate change could lead to a pronounced northward shift in the regal fritillary's distribution. These findings have implications for the development of a recovery plan, since such a plan will need to outline a strategy that accounts for a range of patch sizes whose quality appears to vary spatially, as well as address numerous uncertainties about the future.

Tidal Pumping Controls Dissolved Organic Matter Properties and Outwelling From Mangrove Groundwater to Coastal Water

AbstractDissolved organic matter (DOM) in highly productive intertidal mangroves is an important carbon and nutrient source to the coastal ocean. In highly dynamic mangrove intertidal systems, both tidal pumping or geochemical factors can control DOM characteristics. The influence of groundwater flow on DOM properties and fluxes remains poorly understood. Here, we compared the concentrations, sources, and spectroscopic characteristics of DOM across a recharge‐discharge gradient. We used a three‐dimensional array sampling strategy to reduce spatial biases, groundwater monitoring to quantify tidally driven exchange, and spectrofluoroscopy to quantify DOM fluorescent components. The results showed that tidal hydrology played the key role driving groundwater DOM properties in both horizontal (i.e., from the mangrove to the sea) and vertical directions. Due to the high spatial heterogeneity of dissolved inorganic (DIC) and organic (DOC) carbon concentrations in mangrove groundwater, carbon outwelling showed a high degree of variability at the intertidal scale (54.8–234.8 μmol m−2 d−1 for DIC and 2.5–12.4 μmol m−2 d−1 for DOC). The contribution of terrestrial DOM gradually decreased toward the ocean, but the microbial contributions for recently produced autochthonous DOM increased. Deep groundwater received less terrestrial DOM and experienced anaerobic microbial mineralization, resulting in lower organic carbon than shallow groundwater. Greater variations in spectroscopic characteristics were found in shallow groundwater than in deep groundwater. Overall, tidally driven surface water‐groundwater interactions control the variability in DOM properties and how DOM contributes to carbon budgets and sequestration in mangrove groundwater.

Science Fiction and Native Epistemologies

Science Fiction and Native Epistemologies David M. Higgins (bio) Miriam C. Brown Spiers. Encountering the Sovereign Other: Indigenous Science Fiction. Michigan State UP, 2021. xl+143 pp. $39.95 pbk & ebk. Encountering the Sovereign Other argues that Indigenous science fiction approaches encounters with alterity from a richly ethical perspective informed by the spatial and communal grounding of tribally specific Native worldviews. If Euro-American colonial perspectives are often characterized by violent dynamics of abjection that define the self in contrast against an alien Other (and are driven by a spatial bias privileging outward expansion and a temporal emphasis on future-oriented progress), Miriam C. Brown Spiers suggests that tribally informed Indigenous worldviews, in contrast, can enable individuals “to accept the presence of an Other without feeling threatened, without getting swept up in a narrative of Progress and assimilation” (xxxviii). Furthermore, people with Indigenous values may be “more likely to allow lived experiences to inform their decisions, whereas those raised in Euro-American culture are more likely to try to force reality to fit into their preconceived notions of how the world should be” (xxxviii). This is a vital difference, Spiers argues, because it can engender dramatically different responses to encounters with alterity. She observes examples of this in Indigenous science fiction, where Native characters are often “better prepared to accept and respond to elements of cognitive estrangement than their non-Native counterparts” (xxx). When encountering what Darko Suvin describes as the “strange newness” of a science-fictional novum (Suvin 4), for example, Spiers notes that sf characters with conventional Euro-American worldviews “often refuse to acknowledge that a problem exists because it does not fit into their fixed notions of the world; meanwhile, Native characters recognize the presence of a novum, assess its effects on the community, and react accordingly” (xxx). Indigenous science fictions, in other words, envision ethical encounters with sovereign Others, and Native characters can often interact with strange Others in full and mutual reciprocity rather than reducing Others to mere negative reflections of the self. The book’s four chapters each offer close readings of key texts that exemplify this tendency. Spiers first examines The Ballad of Billy Badass and the Rose of Turkestan (1999) by William Sanders, a novel in which a Cherokee Gulf War veteran (Billy) and a Kazakh scientist (Janna) cultivate cross-cultural alliances and affinities in order to defeat a radiation monster summoned by colonialist New Age trash magic. Next, she turns her attention to Stephen Graham Jones’s It Came from Del Rio (2010), which tells the story of a US citizen named Dodd Raines who flees to Mexico after a failed bank robbery and eventually becomes a radiation monster himself after attempting [End Page 111] to smuggle moon rocks across the southern border. Raines’s transformation illuminates the coloniality of both race and citizenship, Spiers argues, and his journey provokes readers to recognize the personhood of Others who are dehumanized as they attempt to cross national boundaries. After this, she offers a reading of Field of Honor (2004) by D.L. Birchfield—a “shukha anumpa” or “Choctaw hogwash” alternate history in which a paranoid Vietnam veteran (Patrick Pushmataha McDaniel) discovers a hidden Choctaw community that has avoided removal to Oklahoma by retreating into underground caverns (49). This community, Ishtaboli, preserves Choctaw custom and heritage, eschews colonialist Euro-American cultural norms, and supports people from other non-Choctaw tribes by recognizing and honoring their unique cultures and traditions. Finally, Spiers concludes with a discussion of Riding the Trail of Tears (2011) by Blake M. Hausman, a story in which digital spirits (known as Little People or Nunnehi) subvert a VR re-enactment of Cherokee removal in order to help the novel’s protagonist (Tallulah) to disrupt the continuing cycle of violence that requires her to perform Native identity for tourists and endlessly repeat a debilitating narrative of Cherokee defeat and victimization. Spiers argues that the novel’s recognition of digital Cherokee as real people “reinforces an Indigenous perspective in which nonhuman creatures are recognized as peoples who are deserving of respect and compassion,” and this encourages readers to “acknowledge the humanity of the Other and respond ethically to the Other’s demand...

Integrating multiple data sources improves prediction and inference for upland game bird occupancy models

AbstractBird populations have declined across North America over the past several decades. Bird monitoring programs are essential for monitoring populations, but often must strike a balance between efficiency of data collection and spatial biases. Species- or habitat-specialist-specific monitoring programs may be helpful for increasing efficiency of sampling and understanding effects of management actions, but may be subject to preferential sampling bias if they are used to assess large-scale occupancy or abundance and monitoring is largely focused in high-quality habitat. More general monitoring programs, such as the North American Breeding Bird Survey (BBS) and eBird, may not preferentially sample specialists’ habitats but are subject to other forms of bias and often do not efficiently sample specialists’ habitats. We used an integrated occupancy model combining data from eBird, BBS, and Illinois state surveys of upland game bird habitat areas to estimate drivers of Northern Bobwhite (Colinus virginianus) and Ring-Necked Pheasant (Phasianus colchicus) occupancy and compare inference from single-visit, multi-visit, and integrated monitoring programs. We fit sets of candidate models using every combination of the 3 datasets except for eBird by itself, to better understand how differences in spatial biases between programs affect ecological inference. We found that, for both bobwhite and pheasant, state surveys of upland habitat increased the predictive ability of models, and BBS data usually improved inference on occupancy parameters when it was integrated with other data sources. Integrating multiple data sources partially resolved the spatial gaps in each monitoring program, while also increasing precision of parameter estimates. Integrated models may be capable of combining the higher sampling efficiency of targeted monitoring programs with the more even spatial coverage of broad-scale monitoring programs.

Detecting Genuine Versus Pseudo-Absence in The Fossil Record: Applications of Occupancy Modelling For Conservation Palaeobiology

Palaeontological data provide a unique avenue to evaluate the impact of climatic, habitat and ecosystem change over longer temporal scales than typically examined in ecology and conservation, contributing critical data on extinction dynamics that can help contextualize the current biodiversity crisis. However, the fossil record is biased by a variety of factors. In particular, the issue of data absence causes a genuine concern when attempting to discern spatial patterns. Does the lack of a fossil occurrence indicate genuine absence or imperfect detection (i.e., pseudo-absence)? Failing to quantify, discern and mitigate both the main drivers and impacts of data absence will have major implications for any attempt to reconstruct past diversity dynamics, limiting the applicability of paleontological data for addressing questions pertaining to present-day biodiversity. Occupancy modelling, a technique commonly applied in the fields of ecology and conservation, provides a novel way to evaluate the impact of both spatial and temporal biases on the fossil record. By distinguishing between true (taxon genuinely absent) and false (taxon present, but not observed) absences, occupancy modelling produces independent and simultaneous probability estimates for both occupancy and detection. Here, we show how paleontological occurrences can be adapted for use alongside relevant modern and paleo covariate data in both single season models run using the R package ‘unmarked’ and dynamic occupancy models using a Bayesian framework. We additionally test the impact of varying spatial scale, as well as uneven numbers of repeated site visits, on model outcomes, and provide recommendations for conservation paleobiologists intending to run these models. Finally, we outline additional benefits of applying occupancy modelling within conservation paleobiology.

The perfect match? Assessment of excess commute and transport externalities using an agent-based transport model

Excess commute (EC) refers to the sub-optimal allocation of workers to jobs in an urban region in terms of minimal commute distances. This article investigated EC for the Munich metropolitan area using a microscopic agent-based modeling suite. We first considered the optimization strategy, comparing the traditional zone-based transportation problem to a microscopic routine. Zone-based optimization is computationally efficient, but it underestimates EC due to spatial biases, even when the zone system is highly disaggregate. We then applied the optimized job-worker assignment to a microscopic travel demand model, assigning workers to jobs of the same job sector at a shorter distance from their home, accounting for multi-worker households. With shorter commutes, there was a substantial shift toward active modes. However, we only observed moderate impacts on total travel demand, with the overall effect dampened due to compensatory behavior between mandatory and discretionary travel. Finally, the outputs from the demand model were used by a microscopic assignment model to calculate externalities attributable to EC. Because of the complexity of travel demand, we conclude that the EC indicator alone would overestimate the benefits of policies targeting jobs-housing balance.

The undetectability of global biodiversity trends using local species richness

Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e. time between original survey and re‐survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility) and spatial sampling biases (i.e. site‐selection biases). We use PREDICTS model‐based estimates as a proxy for the real‐world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30‐year period, the number of sites for detecting trends doubled for a decade, increased 10‐fold within three years and yearly trends were undetectable. Measurement errors had a non‐linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.

Likelihood-based bilateral filters for pre-estimated basis sinograms using photon-counting CT.

Noise amplification in material decomposition is an issue for exploiting photon-counting computed tomography (PCCT). Regularization techniques and neighborhood filters have been widely used, but degraded spatial resolution and bias areconcerns. This paper proposes likelihood-based bilateral filters that can be applied to pre-estimated basis sinograms to reduce the noise while minimally affecting spatial resolution andaccuracy. The proposed method needs system models (e.g., incident spectrum, detector response) to calculate the likelihood. First, it performs maximum likelihood (ML)-based estimation in the projection domain to obtain basis sinograms. The estimated basis sinograms suffer from severe noise but are asymptotically unbiased without degrading spatial resolution. Then it calculates the neighborhood likelihoods for a given measurement at the center pixel using the neighborhood estimates and designs the weights based on the distance of likelihoods. It is also analyzed in terms of statistical inference, and then two variations of the filter are introduced: one that requires the significance level instead of the empirical hyperparameter. The other is a measurement-based filter, which can be applied when accurate estimates are given without the system models. The proposed methods were validated by analyzing the local property of noise and spatial resolution and the global trend of noise and bias using numerical thorax and abdominal phantoms for a two-material decomposition (water and bone). They were compared to the conventional neighborhood filters and the model-based iterative reconstruction with an edge-preserving penalty applied in the basisimages. The proposed method showed comparable or superior performance for the local and global properties to conventional methods in many cases. The thorax phantom: The full width at half maximum (FWHM) decreased by -2%-31% (-2 indicates that it increased by 2% compared to the best performance from conventional methods), and the global bias was reduced by 2%-19% compared to other methods for similar noise levels (local: 51% of the ML, global: 49%) in the water basis image. The FWHM decreased by 8%-31%, and the global bias was reduced by 9%-44% for similar noise levels (local: 44% of the ML, global: 36%) in the CT image at 65 keV. The abdominal phantom: The FWHM decreased by 10%-32%, and the global bias was reduced by 3%-35% compared to other methods for similar noise levels (local: 66% of the ML, global: 67%) in the water basis image. The FWHM decreased by up to -11%-47%, and the global bias was reduced by 13%-35% for similar noise levels (local: 71% of the ML, global: 70%) in the CT image at 60 keV. This paper introduced the likelihood-based bilateral filters as a post-processing method applied to the ML-based estimates of basis sinograms. The proposed filters effectively reduced the noise in the basis images and the synthesized monochromatic CT images. It showed the potential of using likelihood-based filters in the projection domain as a substitute for conventional regularization or filteringmethods.

MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models.

Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced-biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was assessed based on recently identified localities of the species. The results indicated that although the fine-tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out-of-bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over-predict nonsampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced-biased data in species distribution modeling approaches.