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We used remote sensing data, field observations and numerical groundwater modelling to investigate long-term groundwater storage losses in the regional aquifer of the Ganga Basin in India. This comprised trend analysis for groundwater level observations from 2851 monitoring bores, groundwater storage anomaly estimation using GRACE and Global Land Data Assimilation System (GLDAS) data sets and numerical modelling of long-term groundwater storage changes underpinned by over 50,000 groundwater level observations and uncertainty analysis. Three analyses based on different methods consistently informed that groundwater storage in the aquifer is declining at a significant rate. Groundwater level trend indicated storage loss in the range − 1.1 to − 3.3 cm year −1 (median − 2.6 cm year −1 ) while the modelling and GRACE storage anomaly methods indicated the storage loss in the range of − 2.1 to − 4.5 cm year −1 (median − 3.2 cm year −1 ) and − 1.0 to − 4.2 cm year −1 (median − 1.7 cm year −1 ) respectively. Probabilistic modelling analysis also indicated that the average groundwater storage is declining in all the major basin states, the highest declining trend being in the western states of Rajasthan, Haryana and Delhi. While smaller compared to the western states, average groundwater storage in states further towards east—Uttar Pradesh, Bihar and West Bengal within the basin are also declining. Time series of storage anomalies obtained from the three methods showed similar trends. Probabilistic storage analysis using the numerical model vetted by observed trend analysis and GRACE data provides the opportunity for predictive analysis of storage changes for future climate and other scenarios.

Understanding the response of groundwater levels in alluvial and sedimentary basin aquifers to climatic variability and human water-resource developments is a key step in many hydrogeological investigations. This study presents an analysis of groundwater response to climate variability from 2000 to 2012 in the Queensland part of the sedimentary Clarence-Moreton Basin, Australia. It contributes to the baseline hydrogeological understanding by identifying the primary groundwater flow pattern, water-level response to climate extremes, and the resulting dynamics of surface-water/groundwater interaction. Groundwater-level measurements from thousands of bores over several decades were analysed using Kriging and nonparametric trend analysis, together with a newly developed three-dimensional geological model. Groundwater-level contours suggest that groundwater flow in the shallow aquifers shows local variations in the close vicinity of streams, notwithstanding general conformance with topographic relief. The trend analysis reveals that climate variability can be quickly reflected in the shallow aquifers of the Clarence-Moreton Basin although the alluvial aquifers have a quicker rainfall response than the sedimentary bedrock formations. The Lockyer Valley alluvium represents the most sensitively responding alluvium in the area, with the highest declining (−0.7 m/year) and ascending (2.1 m/year) Sen’s slope rates during and after the drought period, respectively. Different surface-water/groundwater interaction characteristics were observed in different catchments by studying groundwater-level fluctuations along hydrogeologic cross-sections. The findings of this study lay a foundation for future water-resource management in the study area.

True setae borne on the abdominal tergites of Ochrogaster lunifer Herrich-Schӓffer caterpillars are the agents of an irritating contact dermatitis, osteomyelitis, ophthalmia, and severe allergic reactions in humans, and are the cause of Equine Amnionitis and Fetal Loss in Australia. The setae are detached and readily dislodge from the integument whereby they disperse throughout the environment. To better understand the true setae of O. lunifer as agents of medical and veterinary concern, we studied their characteristics and distance dispersed. Whereas members of the European Thaumetopoeinae have been widely studied, their southern-hemisphere counterparts such as O. lunifer are not well known despite their harmfulness and known medical and veterinary importance. The caterpillar’s investment in true setae increased with age and size, and two distinct size classes co-occurred in setae fields. A previously undescribed morphological type of true seta was found on the first abdominal segment. All true setae were calculated to travel long distances in the air even under light breeze conditions. Our results show there is a high risk of exposure to airborne urticating setae within 100 m of elevated caterpillar activity, and a likely risk of exposure for some kilometers in the direction of the prevailing breeze. This information should be used to inform management strategies in areas where urticating processionary caterpillars are active, and especially during periods of an outbreak.

Releases of Aedes aegypti carrying Wolbachia bacteria are known to suppress arbovirus transmission and reduce the incidence of vector-borne diseases. In planning for Wolbachia releases in the arid environment of Jeddah, Saudi Arabia, we collected entomological data with ovitraps across a 7-month period in four locations. Herein, we show that mosquito presence in basements does not differ from that of non-basement areas of buildings. In modelling mosquito presence across the study sites, we found the spatial structure to be statistically significant in one of the four sites, while a significant spatial structure was found for egg production data across three of the four sites. The length scales of the spatial covariance functions fitted to the egg production data ranged from 143 m to 574 m, indicating that high productivity regions can be extensive in size. Rank-correlation analyses indicated that mosquito presence tended to persist from the dry to wet season, but that egg production ranks at locations could reverse. The data suggest that, in Jeddah, the quality of the local environment for breeding can vary over time. The data support the feasibility of dry season releases but with release numbers needing to be flexible depending on local rates of invasion.

Time series data from environmental monitoring stations are often analysed with machine learning methods on an individual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time series from the same monitoring network within a ‘global’ model. This approach provides the opportunity for larger training data sets, allows information to be shared across the network, leading to greater generalisability, and can overcome issues encountered in the individual time series, such as small datasets or missing data. We present a case study involving the analysis of 165 time series from groundwater monitoring wells in the Namoi region of Australia. Analyses of the multiple time series using a variety of different aggregations are compared and contrasted (with single time series, subsets, and all of the time series together), using variations of the multilayer perceptron (MLP), self-organizing map (SOM), long short-term memory (LSTM), and a recently developed LSTM extension (DeepAR) that incorporates autoregressive terms and handles multiple time series. The benefits, in terms of prediction performance, of these various approaches are investigated, and challenges such as differing measurement frequencies and variations in temporal patterns between the time series are discussed. We conclude with some discussion regarding recommendations and opportunities associated with using networks of environmental data to help inform future resource-related decision making.

Releasing sterile or incompatible male insects is a proven method of population management in agricultural systems with the potential to revolutionize mosquito control. Through a collaborative venture with the “Debug” Verily Life Sciences team, we assessed the incompatible insect technique (IIT) with the mosquito vector Aedes aegypti in northern Australia in a replicated treatment control field trial. Backcrossing a US strain of Ae. aegypti carrying Wolbachia wAlbB from Aedes albopictus with a local strain, we generated a wAlbB2-F4 strain incompatible with both the wild-type (no Wolbachia) and wMel-Wolbachia Ae. aegypti now extant in North Queensland. The wAlbB2-F4 strain was manually mass reared with males separated from females using Verily sex-sorting technologies to obtain no detectable female contamination in the field. With community consent, we delivered a total of three million IIT males into three isolated landscapes of over 200 houses each, releasing ∼50 males per house three times a week over 20 wk. Detecting initial overflooding ratios of between 5:1 and 10:1, strong population declines well beyond 80% were detected across all treatment landscapes when compared to controls. Monitoring through the following season to observe the ongoing effect saw one treatment landscape devoid of adult Ae. aegypti early in the season. A second landscape showed reduced adults, and the third recovered fully. These encouraging results in suppressing both wild-type and wMel-Ae. aegypti confirms the utility of bidirectional incompatibility in the field setting, show the IIT to be robust, and indicate that the removal of this arbovirus vector from human-occupied landscapes may be achievable.

Understanding and predicting animal movement is fundamental to ecology and conservation management. Models that estimate and then predict animal movement and habitat selection parameters underpin diverse conservation applications, from mitigating invasive species spread to enhancing landscape connectivity. However, many predictive models overlook fine‐scale temporal dynamics within their predictions, despite animals often displaying fine‐scale behavioural variability that might significantly alter their movement, habitat selection and distribution over time. Incorporating fine‐scale temporal dynamics, such as circadian rhythms, within predictive models might reduce the averaging out of such behaviours, thereby enhancing our ability to make predictions in both the short and long term. We tested whether the inclusion of fine‐scale temporal dynamics improved both fine‐scale (hourly) and long‐term (seasonal) spatial predictions for a significant invasive species of northern Australia, the water buffalo Bubalus bubalis. Water buffalo require intensive management actions over vast, remote areas and display distinct circadian rhythms linked to habitat use. To inform management operations we generated hourly and dry season prediction maps by simulating trajectories from static and temporally dynamic step selection functions (SSFs) that were fitted to the GPS data of 13 water buffalo. We found that simulations generated from temporally dynamic models replicated the buffalo crepuscular movement patterns and dynamic habitat selection, resulting in more informative and accurate hourly predictions. Additionally, when the simulations were aggregated into long‐term predictions, the dynamic models were more accurate and better able to highlight areas of concentrated habitat use that might indicate high‐risk areas for environmental damage. Our findings emphasise the importance of incorporating fine‐scale temporal dynamics in predictive models for species with clear dynamic behavioural patterns. By integrating temporally dynamic processes into animal movement trajectories, we demonstrate an approach that can enhance conservation management strategies and deepen our understanding of ecological and behavioural patterns across multiple timescales. Keywords: circadian, fine‐scale dynamics, harmonics, landscape‐scale distributions, simulated trajectories, temporal dynamics

Predictions of animal movement are vital for understanding and managing wild populations. However, the fine‐scale, complex decision‐making of animals can pose challenges for the accurate prediction of trajectories. Integrated step selection functions (iSSFs), a common tool for inferring relationships between animal movement and the environment, are also increasingly used to simulate animal trajectories for prediction. Although admitting a lot of flexibility, the iSSF framework is limited to its reliance on pre‐defined functional forms for fitting to data, and iSSFs that involve complex functional forms to model detailed processes can be prohibitively difficult to fit and interpret. Here, we present deepSSF, an approach to fit and predict animal movement data using deep learning. The deepSSF approach replaces the log‐linear model of an iSSF with a neural network architecture that receives multiple environmental layers and scalar values as inputs and outputs a single layer representing the next‐step probability. We demonstrate an example deepSSF model, built in PyTorch , consisting of distinct but interacting habitat selection and movement subnetworks. This allows for explicit representation of both selection and movement processes, thus giving interpretable intermediate outputs. We apply our model to GPS data of introduced water buffalo ( Bubalus bubalis ) in the tropical savannas of Northern Australia. Our deepSSF model was able to learn features that are present in the habitat covariate layers, such as linear features (rivers, forest edges) and the composition of certain habitat areas, without having to specify them pre‐emptively within the model framework. It was able to capture complex interactions between the habitat covariates as well as temporal dynamics across time of day and year. Finally, our deepSSF model generally had better in‐ and out‐of‐sample predictive accuracy than the analogous iSSF model. We expect that the deepSSF approach will generate accurate and informative predictions about animal movement, which can be used for deepening our understanding of animal–environment systems and for the practical management of species. We discuss how the wide range of existing deep learning tools could enable the deepSSF approach to be extended to represent memory and social dynamic processes, with the potential for integrating non‐spatial data sources such as accelerometers and physiological sensors.

In this study, we developed a workflow that applies a complex groundwater model for purpose-driven groundwater monitoring network design and uses linear uncertainty analysis to explore the predictive dependencies and provide insights into the veracity of the monitoring design. A numerical groundwater flow model was used in a probabilistic modelling framework for obtaining the spatial distribution of predicted drawdown for a wide range of plausible combination of uncertain parameters pertaining to the deep sedimentary basin and groundwater flow processes. Reduced rank spatial prediction was used to characterize dominant trends in these spatial drawdown patterns using empirical orthogonal functions (EOF). A differential evolution algorithm was used to identify optimal locations for multi-level piezometers for collecting groundwater pressure data to minimize predictive uncertainty in groundwater drawdown. Data-worth analysis helps to explore the veracity of the design by using only the sensitivities of the observations to predictions independent of the absolute values of predictions. A 10-bore monitoring network that collects drawdown data from multiple depths at each location was designed. The data-worth analysis revealed that the design honours sensitivities of the predictions of interest to parameters. The designed network provided relatively high data-worth for minimizing uncertainty in the drawdown prediction at locations of interest.

True setae borne on the abdominal tergites of Ochrogaster lunifer Herrich-Schaffer caterpillars are the agents of an irritating contact dermatitis, osteomyelitis, ophthalmia, and severe allergic reactions in humans, and are the cause of Equine Amnionitis and Fetal Loss in Australia. The setae are detached and readily dislodge from the integument whereby they disperse throughout the environment. To better understand the true setae of O. lunifer as agents of medical and veterinary concern, we studied their characteristics and distance dispersed. Whereas members of the European Thaumetopoeinae have been widely studied, their southern-hemisphere counterparts such as O. lunifer are not well known despite their harmfulness and known medical and veterinary importance. The caterpillar's investment in true setae increased with age and size, and two distinct size classes co-occurred in setae fields. A previously undescribed morphological type of true seta was found on the first abdominal segment. All true setae were calculated to travel long distances in the air even under light breeze conditions. Our results show there is a high risk of exposure to airborne urticating setae within 100 m of elevated caterpillar activity, and a likely risk of exposure for some kilometers in the direction of the prevailing breeze. This information should be used to inform management strategies in areas where urticating processionary caterpillars are active, and especially during periods of an outbreak.