Environmental Characteristics Research Articles

PAFPT: Progressive aggregator with feature prompted transformer for underwater image enhancement

The optical characteristics of underwater environments often result in distorted underwater images, which makes it difficult to meet the needs of complex underwater environment perception. The demand for obtaining high-quality underwater images presents challenges for existing underwater image enhancement methods. In this paper, a progressive aggregator with feature prompted transformer for underwater image enhancement is presented. First, a feature prompted transformer module consisting of a feature convolutional transposed attention and a prompted feedforward neural network is proposed. It integrates feature attention mechanisms and adjusts input features through prompt weights to better focus on global information while enhancing local details, enriching texture, removing blurring, and correcting color deviation. Concurrently, a progressive aggregator consisting of four stages of up-sampling aggregation blocks is constructed to transfer features effectively and facilitate the aggregation of features of different scales at different stages to realize comprehensive attention to image details and semantic information. Besides, a region reconstruction unit is designed, and a reconstruction attention mechanism is introduced to pay attention to the bottleneck localized region to suppress noise. The performance of our method is thoroughly evaluated on public underwater image datasets: EUVP, UIEB, RUIE and SQUID. The quantitative and qualitative results indicate that our method outperforms nine state-of-the-art methods in both subjective perception and evaluation metrics, demonstrating excellent learning and generalization abilities. Additionally, the excellent results reflect the significant performance gains it brings to downstream visual applications.

Exploration of the Traffic Safety of Battery Electric Vehicles: A Case Study of Tesla Vehicle-Involved Crashes in Pennsylvania, USA

After decades of efforts, the number of battery electric vehicles (BEV) has increased greatly around the world. Meanwhile, their unique technical features also bring many new traffic safety challenges. Using traffic crash data from Pennsylvania from 2018 to 2021, this study aims to identify characteristics and trends of BEV crashes by comparing those crashes involving Tesla vehicles (the premier BEV brand in the auto market of the United States) with crashes involving internal combustion engine vehicles (ICEV). First, Tesla and ICEV crashes are compared for crash severity, collision type, spatial and temporal distributions, and environmental features. Tesla crashes show no significant difference from those of ICEVs in severity, but they have many more rear-end and angle collisions. In addition, Tesla crashes are found to concentrate in peak hours and around noon, occurring mainly in urban areas, at intersections, and on state roads. A logistic regression model is then built to identify the important factors influencing the severity of Tesla crashes. The findings are expected to provide new insights which will help researchers understand better traffic safety issues around BEVs.

SOAMC: A Semi-Supervised Open-Set Recognition Algorithm for Automatic Modulation Classification

Traditional automatic modulation classification methods operate under the closed-set assumption, which proves to be impractical in real-world scenarios due to the diverse nature of wireless technologies and the dynamic characteristics of wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms of detection effectiveness and computational complexity. To address the limitations of modulation classification and recognition in open-set scenarios, this paper proposes a semi-supervised open-set recognition approach, termed SOAMC (Semi-Supervised Open-Set Automatic Modulation Classification). The primary objective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples is manually labeled. The proposed method consists of three key stages: (1) A signal recognition pre-training model is constructed using data augmentation and adaptive techniques to enhance robustness. (2) Feature extraction and embedding are performed via a specialized extraction network. (3) Label propagation is executed using a graph convolutional neural network (GCN) to efficiently annotate the unlabeled signal samples. Experimental results demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limited amounts of labeled data and high signal similarity. These findings are critical for the practical identification of complex and diverse modulation signals in real-world wireless communication systems.

EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection

In power inspection, it is crucial to accurately and regularly monitor the status of isolation switches to ensure the stable operation of power systems. However, current methods for detecting the open and closed states of isolation switches based on image recognition still suffer from low accuracy and high edge deployment costs. In this paper, we propose a lightweight object detection model, EMB-YOLO, to address this challenge. Firstly, we propose an efficient mobile inverted bottleneck convolution (EMBC) module for the backbone network. This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. Furthermore, an ELA attention mechanism is used in the EMBC module to enhance the extraction of horizontal and vertical isolation switch features in complex environments. Finally, we proposed an efficient-RepGDFPN fusion network. This network integrates feature maps from different levels to detect isolation switches at multiple scales in monitoring scenarios. An isolation switch dataset was self-built to evaluate the performance of the proposed EMB-YOLO. The experimental results demonstrated that the proposed method achieved superior detection performance on our self-built dataset, with a mean average precision (mAP) of 87.2%, while maintaining a computational cost of only 6.5×109 FLOPs and a parameter size of just 2.8×106 bytes.

The Wolf Spider Tigrosa helluo Uses Visual Associative and Beacon Landmarks During Water Maze Navigation Tasks

ABSTRACTWolf spiders can learn simple spatial navigation tasks. Previous studies have shown that the wolf spider Tigrosa helluo can use environmental edge features (reference frame landmarks) to learn the location of a dry target in flooded T‐mazes; however, the relative importance of different types or numbers of landmark cues to spatial learning remains unknown. We used a modified open arena water maze and recorded the ability of adult female T. helluo to find a target reward (a dark and dry cup) among cups that were identical to the target but flooded. We measured variation in spatial learning by measuring time to target with no landmark (control), with a beacon (a landmark that is part of the target), with an associative cue (a landmark associated with a specific navigational action), and with both a beacon and an associative cue (N = 92 subjects, n = 23 per landmark cue treatment). For each treatment, we tested females for five trials each on four consecutive days, with the last trial on the fourth day having an altered target location, totaling 19 training trials and one reversal trial (1840 trials). We found that spiders took significantly less time to find the target over subsequent trials within a day and learned more quickly when landmark cues were present, but we found no difference in the type or number of landmark features in the meantime to target entrance. After learning a target location, moving the landmark significantly increased the mean time to target entrance in the combined beacon and associative cue treatment relative to other treatments. Our results indicate that wolf spiders use visual beacons and associative cue landmarks alone or in combination and that performance improves across trials when landmarks are present and deteriorates more when multiple landmarks are moved.

Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules

Abstract We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spec- tra of organic molecules originating from the ionization energies of 1s electrons in carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed training dataset through high-throughput calculations of core-electron binding energies (CEBEs) for 12,880 small organic molecules in the bigQM7ω dataset, employing the ∆-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy using the ∆-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn–Sham eigenvalues. Upon application to 208 com- binatorially substituted uracil molecules, larger than those in the training set, our analyses reveal that while the models may not yield quantitatively accurate predictions of CEBEs on a molecule-by-molecule basis, they do exhibit a strong linear correlation, which proves valuable for virtual high-throughput screening purposes. We present the dataset and models as the Python module, cebeconf, to facilitate further explorations.

Incidence of agroecological-based practices in urban vegetable gardens: effect on crop diversity and production levels

ABSTRACT Agroecological-based practices (ABPs) play a vital role in urban vegetable gardens, enhancing the quality of life. However, factors driving ABP implementation and their impact on crop production and diversity are not fully understood. Our study assessed the influence of socio-economic and environmental characteristics on ABP adoption, crop diversity, and total weight vegetable production in San Carlos de Bariloche, Argentina. Through interviews and a multi-model inference approach, we found that ABP implementation is tied to gardener experience and formative assets (e.g. horticultural experience, specific training). Crop diversity, integral to ABP, is affected by farming practices and farmer characteristics (Farm animals, Pollinator-friendly practices, Experience, Additional training and Environmental knowledge). The Total weight produced was primarily influenced by the type of garden, the gardener’s experience and some ABPs. Long-standing commercial and community objectives and the use of farm animals and commercial seed led to greater production. Strengthening key drivers, such as farmer training, through public policies (e.g. income generation, financing, specific training, land access) can bolster the role of urban gardens in providing a healthy environment and promoting food sovereignty.

Comparative ecological analysis and predictive modeling of tick-borne pathogens.

Tick-borne diseases constitute the predominant vector-borne health threat in North America. Recent observations have noted a significant expansion in the range of the black-legged tick (Ixodes scapularis Say, Acari: Ixodidae), alongside a rise in the incidence of diseases caused by its transmitted pathogens: Borrelia burgdorferi Johnson (Spirochaetales: Spirochaetaceae), Babesia microti Starcovici (Piroplasmida: Babesiidae), and Anaplasma phagocytophilium Zhu (Rickettsiales: Anaplasmataceae), the causative agents of Lyme disease, babesiosis, and anaplasmosis, respectively. Prior research identified environmental features that influence the ecological dynamics of I. scapularis and B. burgdorferi that can be used to predict the distribution and abundance of these organisms, and thus Lyme disease risk. In contrast, there is a paucity of research into the environmental determinants of B. microti and A. phagocytophilium. Here, we use over a decade of surveillance data to model the impact of environmental features on the infection prevalence of these increasingly common human pathogens in ticks across New York State (NYS). Our findings reveal a consistent northward and westward expansion of B. microti in NYS from 2009 to 2019, while the range of A. phagocytophilum varied at fine spatial scales. We constructed biogeographic models using data from over 650 site-year visits and encompassing more than 250 environmental variables to accurately forecast infection prevalence for each pathogen to a future year that was not included in model training. Several environmental features were identified to have divergent effects on the pathogens, revealing potential ecological differences governing their distribution and abundance. These validated biogeographic models have applicability for disease prevention efforts.

An Analysis of Neighborhood Characteristics Associated with Single-Victim, Multi-Victim, and Mass Shootings

ABSTRACT Despite the growing prevalence of mass shootings, scarce research has examined their relationships with neighborhood-level attributes such as sociodemographic or environmental characteristics. This study used negative binomial regression models to examine and compare how sociodemographic factors and the built environment were associated with neighborhood counts of single, double, triple, and mass shootings in the city of Philadelphia, PA during a six-year period (2018–2023). Effects varied both within and between predictors across models. The results demonstrate that neighborhood-level characteristics associated with mass shootings may diverge from those related to other shootings, deviating in some cases from the extant gun violence research.

Do You Dare to Speak Up at Work? A Study on Organizational Conditions Enabling or Discouraging Employee Voice

ABSTRACT Employee voice is part of internal communication, and this study extends current knowledge concerning the organizational conditions conducive to employee voice, namely: participation in decision making, consultation, information sharing, sensemaking and interactive communication, voice systems, formal policies regulating employees’ freedom of speech, solicitation of informal voice, implementation of corrective actions and feedback to employees voicing concerns. This study specifically examines the organizational and environmental characteristics that shape these organizational conditions that promote employee voice. A SEM analysis shows: the positive influence of the organizational and environmental characteristics of business strategy, organizational model, labor market conditions and company size. In addition, the mediating role of managers’ implicit beliefs that employees are self-interested, that management knows best and that dissent is undesirable was clearly emphasized.

Ant‐Pollination: Conditional Outcomes Depending on Environmental Features and Species Interactions

ABSTRACTThe intricate web of interactions in nature has led to unexpected partnerships between plants and animals, greatly influencing ecosystem dynamics. In this discussion, we'll explore the Pollination Syndrome Hypothesis and consider a scenario where a partnership with unexpected pollinators emerges. We suggest that ant pollination has been largely overlooked, but ants can surprisingly play a crucial role and have a history of success in pollinating dry system plants. We will present both negative and positive aspects that must be observed in ant‐pollination syndrome. Additionally, we aim to explore the often‐underestimated phenomenon of ant pollination and the overlooked tiny, discreet, unnoticed flowers found in our fields and forests, especially in dry ecosystems, savannas and mountain fields, which may hold numerous undiscovered secrets about these interactions. We encourage young biologists to engage in natural history observations and to perform manipulative experiments directly in the field to understand the true nature of these successful ant‐pollinations.

Remote Sensing Evaluation of Trophic Status in the Daihai Lake Based on Fuzzy Classification

Trophic state index (TSI) is a critical ecological and environmental issue in water resource management that has garnered significant attention. Given the complexity of optical characteristics in aquatic environments, this study employs fuzzy classification methods (FCM) and composite nutrient status indices to meticulously classify in-situ remote sensing reflectance data, aiming to develop evaluation models for different nutrient status categories to facilitate the assessment of the Daihai River in Inner Mongolia, China. Subsequently, we applied this model to MSI data to analyze the nutrient status of Daihai Lake from 2016 to 2021. Furthermore, a structural equation model (SEM) was utilized to explore the primary driving factors influencing nutrient status. The results indicated that the water bodies in Daihai Lake can be broadly classified into three categories, with the nutrient status models demonstrating robust performance for each category (R2 = 0.80, R2 = 0.83, and R2 = 0.74). Comparisons were made between nutrient status accuracies obtained through the NCM and FCM based on measured data, yielding R2 values of 0.74 and 0.85, respectively. Furthermore, the TSI results derived from MSI inversion were validated, with NCM achieving an R2 of 0.49, RMSE of 6.88, and MAPE of 10.36%, while FCM exhibited an R2 of 0.55, RMSE of 8.89, and MAPE of 13.18%. An SEM–based analysis revealed that over the long term, human activities exerted a more substantial impact on eutrophication in Daihai Lake, while climatic factors played an accelerating and reinforcing role. These results are consistent with prior research in the Daihai area, indicating a state of mild eutrophication and the potential of the fuzzy classification method and comprehensive trophic status index method in eutrophication assessment.

Factors Influencing the Prevalence of Hyperpigmented Melanistic Lesions in Smallmouth Bass Micropterus dolomieu in the Susquehanna River Basin, Pennsylvania.

Hyperpigmented melanistic lesions (HPMLs) are a visual anomaly documented on the skin of smallmouth bass Micropterus dolomieu in the Susquehanna River Basin, Pennsylvania and in numerous other geographical locations. Currently, there is a lack of information on environmental and fish characteristics that may influence the prevalence of HPMLs associated with a recently described Adomavirus. The goal of this study was to understand potential drivers associated with HPMLs in socioeconomically and ecologically important riverine smallmouth bass populations. A total of 16,220 smallmouth bass were collected and examined for HPMLs between 2012 and 2022 in the Susquehanna River Basin. Overall, HPMLs were documented on 2.9% of fish collected. The interaction between temperature and fish size suggested differing relationships between shorter and longer fish with respect to temperature. Predicted probability of HPML prevalence ranged from 1.1% (95% CI = 0.3, 3.2) at 4°C to 0.01% (CI = 0.00, 0.04) at 26°C for an age-0 (125 mm) fish. In contrast, predicted probability of HPML prevalence ranged from 10.5% (95% CI = 5.8, 18.9) at 4°C to 0.8% (CI = 0.4, 1.5) at 26°C for an adult (322 mm) fish. Overall, HPMLs were more common in longer fish during cooler temperature periods which also corresponds to key life history periods for smallmouth bass (e.g., pre-spawn and overwintering) and could represent different exposure histories for juvenile and adult fish.

Navigating the definition of urban flooding: a conceptual and systematic review of the literature

ABSTRACT Urban flooding is a significant global challenge, posing risks to urban planners, policymakers, and communities. This paper reviews the literature to analyze how urban flooding is defined across scientific disciplines. Our objectives are to uncover the elements used to define urban flooding and evaluate how these elements can impact future research and practice. A key difficulty is the lack of a consistent, comprehensive definition that captures both physical and social dimensions of urban flooding. Current definitions often focus solely on physical aspects (e.g., rainfall, infrastructure) or social impacts, rarely integrating both. This fragmentation hinders effective flood risk management and interdisciplinary collaboration. Our contribution is a multifaceted definition incorporating spatial and social concerns, including water origins, built environment characteristics, and local community aspects. We introduce the "Urban Water Transect" concept to illustrate the continuum of flood risk across urban zones, addressing a gap in the literature. The analysis reveals that many papers discuss flooding causes without providing an explicit definition. Urban flooding is predominantly defined based on water source, imperviousness, and drainage infrastructure. Future research should adopt an interdisciplinary perspective considering both physical and social aspects, potentially transforming urban flood risk management.

Performance support team effectiveness in elite sport: a narrative review

ABSTRACT In pursuit of competitive advantage, elite sport organizations are increasingly relying on the support of diverse sport medicine and sport science staff, who are collectively referred to as the performance support team. Whilst it has been suggested that the accumulative input from diverse multiteam systems has the potential to contribute to a resultant whole that is greater than the sum of its parts, team effectiveness is reliant on more than the mere aggregate of diverse experts. The aim of this narrative review was to appraise, summarize, and apply pertinent performance support team literature to a conceptual framework for teamwork and team effectiveness in sport. It specifically explores team effectiveness, with reference to its inputs (i.e. characteristics of individual, team, and environment) and mediators (i.e. team processes and emergent states). This review provides an insight into the individual (i.e. disciplinary knowledge, technical competency, and interpersonal qualities), team (i.e. team composition and leadership), and external (i.e. hierarchical arrangement and environmental factors) inputs that are necessary for team effectiveness, as well as the mediators (i.e. behavioral processes and emergent states) that translate such inputs into desired outcomes.

Maritimacies and Nature-Culture Collectives as Inputs for a Sustainable Blue Economy on the East Coast of Uruguay

The research for this article examines the characteristics of the marine-coastal environment from the perspectives of a range of social actors. Knowledge of maritimacies can serve as an input for management of marine-coastal environments that takes into account the diverse types of humanity found there, by emphasizing that these processes are not just physical and ecological, but also social, economic, cultural, and historical. An ethnographic methodology allows for mapping the perspectives of social actors, their points of view, and different ways of life. The result was a systemization of maritimacies, which can contribute to thinking about and building a sustainable Blue Economy that recognizes and involves those who inhabit the coast and the sea in Uruguay by considering the heterogeneity and complexity of their social networks.

Tailoring the Wettability of Bacterial Cellulose Magnetobots via the Assembly of In Situ Synthesized and Surfactant-Coated Magnetic Nanoparticles.

This study showcases the possibility of tailoring the wettability of magnetic bacterial cellulose (m-BC) composites by the combined effect of in situ synthesized magnetic nanoparticles (MNPs) distribution and simultaneous oleic acid (OA) coating within the BC matrix. This combined effect of MNPs and OA resulted in m-BC composites exhibiting solvent-dependent and time-dependent surface-wetting behavior, which was not observed in either individual cases of BC that have been modified with OA or BC that has MNPs adsorbed on its fibers. This tailored wettability in m-BCs was achieved via varying the concentrations of iron precursors, which governed the arrangement and morphology of MNPs (uniformly or clustered) on the BC membrane, although the same fraction of MNPs was observed in both the m-BCs. Finally, we have achieved delayed water absorption in m-BC_x (synthesized in a comparatively lower precursor concentration) and no absorption of water in m-BC_4x (synthesized in a 4-times-higher precursor concentration). The m-BC_4x composite maintained its hydrophobic characteristics in diverse environments, ranging from highly acidic conditions (pH 1.2) to physiological environments at pH 4, 5.5, and 7.4. The MNP agglomerates on the BC nanofibers in the m-BC_4x composite were found to be instrumental in attaining a stable cyclic absorption performance with structural integrity. Additionally, the magnetic-inducing heat generation efficiency of the m-BCs can be extended for evaporating low-boiling-point solvents. The present study expands the frontiers of BC-based magnetic composites by emphasizing the assembly of surfactant-coated magnetic nanostructures in their responsiveness to polar/nonpolar liquids with stable performance even in complex scenarios.

The offline roots of online hostility: Adult and childhood administrative records correlate with individual-level hostility on Twitter

Reducing hostility in social media interactions is a key public concern. Most extant research emphasizes how online contextual factors breed hostility. Here, we take a different perspective and focus on the offline roots of hostility, that is, offline experiences and stable individual-level dispositions. Using a unique dataset of Danish Twitter users (N [Formula: see text] 4,931), we merge data from administrative government registries with a behavioral measure of online hostility. We demonstrate that individuals with more aggressive dispositions (as proxied by having many more criminal verdicts) are more hostile in social media conversations. We also find evidence that features of childhood environments predict online hostility. Time spent in foster care is a strong correlate, while other indicators of childhood instability (e.g., the number of moves and divorced parents) are not. Furthermore, people from more resourceful childhood environments-those with better grades in primary school and higher parental socioeconomic status-are more hostile on average, as such people are more politically engaged. These results offer an important reminder that much online hostility is rooted in offline experiences and stable dispositions. They also provide anuanced view of the core group of online aggressors. While these individuals display general antisocial personality tendencies by having many more criminal verdicts, they also come from resourceful backgrounds more often than not.

Annotation-free prediction of microbial dioxygen utilization.

Aerobes require dioxygen (O2) to grow; anaerobes do not. However, nearly all microbes-aerobes, anaerobes, and facultative organisms alike-express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O2-utilizing enzymes, for example. These effects permit high-quality prediction of O2 utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content-e.g., triplets of amino acids-perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O2 gradient in the Black Sea, we found quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or "sense," pivotal features of the chemical environment using DNA sequencing data.IMPORTANCEWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology-an organism's capacity to engage metabolically with its environment-may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O2 levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.

Using eDNA Sampling to Identify Correlates of Species Occupancy Across Broad Spatial Scales

ABSTRACTAimSpecies presence–absence data can be time‐consuming and logistically difficult to obtain across large spatial extents. Yet these data are important for ensuring changes in species distributions are accurately monitored and are vital for ensuring appropriate conservation actions are undertaken. Here, we demonstrate how environmental DNA (eDNA) sampling can be used to systematically collect species occupancy data rapidly and efficiently across vast spatial domains to improve understanding of factors influencing species distributions.LocationSouth‐eastern Australia.MethodsWe use a widely distributed, but near‐threatened species, the platypus (Ornithorhynchus anatinus), as a test case and undertake an environmentally stratified systematic survey to assess the presence–absence of platypus eDNA at 504 sites across 584,292 km2 of south‐eastern Australia, representing ~37% of the species' extensive distribution. Site occupancy‐detection models were used to analyse how landscape‐ and site‐level factors affect platypus occupancy, enabling us to incorporate uncertainty at the different levels inherent in eDNA sampling (site, water sample replicate and qPCR replicate).ResultsPlatypus eDNA was detected at 272 sites (~54%) with platypuses more likely to occupy sites in catchments with increased runoff and less zero‐flow days, and sites with access to banks suitable for burrowing. Platypuses were less likely to occupy sites in catchments with a high proportion of shrubs and grasslands, or agricultural land use.Main ConclusionsThese data provide an important large‐scale validation of the landscape‐ and site‐level factors influencing platypus occupancy that can be used to inform future conservation efforts. Our case study shows that systematically designed, stratified eDNA surveys provide an efficient means to understand how environmental characteristics affect species occupancy across broad environmental gradients. The methods employed here can be applied to aquatic and semi‐aquatic species globally, providing unprecedented opportunities to understand biodiversity status and change and provide insights for current and future conservation actions.