The Critical Social Processes for Standardising the Ecological Monitoring of Australian Landscapes.

For a long time, ecological monitoring across Australia has utilised a wide variety of different methodologies resulting in data that is difficult to analyse across place or time. In response to these limitations, a new systematic approach to ecological monitoring has been developed in collaboration between the Terrestrial Ecosystem Research Network and the Australian Department of Climate Change, Energy, the Environment and Water - the Ecological Monitoring System Australia (EMSA). A qualitative approach involving focus groups and semi-structured interviews was undertaken to review perceptions of the introduction of the EMSA protocols amongst Natural Resource Management practitioners and other key stakeholders. We found that environmental management stakeholders recognise there will be many advantages from the standardisation of ecological monitoring. However, key concerns emerged regarding the capacity needed to implement the standard protocols, the utility of the resultant data for regional projects, and the scope for adaptive co-management under the EMSA. Stakeholders emphasised the need for autonomy and flexibility, so their participation in protocol development can facilitate regional adoption of the standards. Respondents' concerns about a perceived lack of genuine consultation and acknowledgement of feedback revealed the importance of clear communication at all stages of an environmental management project aiming to standardise practices. Our findings indicate that reflexivity will be vital to address the complexity involved in standardisation of ecological monitoring. Formal processes of social learning will need to be integrated into environmental management approaches to account for the increasing complexity of socio-ecological systems as they are challenged by global change.

Using citizen science data for predicting the timing of ecological phenomena across regions.

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

Variation in ecological scorecards and their potential for wider use.

Ecological monitoring is a vital tool to help us assess habitat condition and understand the mechanism(s) for habitat change. Yet many countries struggle to meet their monitoring requirements in part due to the high assessment workload. Rapid ecological assessment methods may have an important role to play in this regard. Following their success within several European habitats (e.g., semi-natural grasslands), they are now being developed for additional habitats such as heathlands, peatlands, and other agri-associated areas. Whilst some rapid assessments using ecological scorecards have been shown to be accurate compared to traditional ecological monitoring, less is known about the functionality of these scorecards in heterogenous landscapes. In this study, we selected four existing scorecards to test alongside a prototype. We assessed how these different scorecards measured habitat condition on the same heathland sites. We found that the choice of metrics, their score weighting, and the thresholds used for categorical scores cause scorecards to assess the same site with substantial variation (37%). Vegetation metrics were the primary cause of score variation, with vegetation structure and positive indicator species being the leading causes. Our study indicates that whilst current scorecards may be representative of project-specific goals, they may not be suitable for wider monitoring uses in their current form. Ecological scorecards have great potential to drastically increase the extent of monitoring, but caution is needed before adapting existing scorecards beyond the purposes from which they were designed.

Application of amodal segmentation for shape reconstruction and occlusion recovery in occluded tomatoes.

Common object detection and image segmentation methods are unable to accurately estimate the shape of the occluded fruit. Monitoring the growth status of shaded crops in a specific environment is challenging, and certain studies related to crop harvesting and pest detection are constrained by the natural shadow conditions. Amodal segmentation can focus on the occluded part of the fruit and complete the overall shape of the fruit. We proposed a Transformer-based amodal segmentation algorithm to infer the amodal shape of occluded tomatoes. Considering the high cost of amodal annotation, we only needed modal dataset to train the model. The dataset was taken from two greenhouses on the farm and contains rich occlusion information. We introduced boundary estimation in the hourglass structured network to provide a priori information about the completion of the amodal shapes, and reconstructed the occluded objects using a GAN network (with discriminator) and GAN loss. The model in this study showed accuracy, with average pairwise accuracy of 96.07%, mean intersection-over-union (mIoU) of 94.13% and invisible mIoU of 57.79%. We also examined the quality of pseudo-amodal annotations generated by our proposed model using Mask R-CNN. Its average precision (AP) and average precision with intersection over union (IoU) 0.5 (AP50) reached 63.91%,86.91% respectively. This method accurately and rationally achieves the shape of occluded tomatoes, saving the cost of manual annotation, and is able to deal with the boundary information of occlusion while decoupling the relationship of occluded objects from each other. Future work considers how to complete the amodal segmentation task without overly relying on the occlusion order and the quality of the modal mask, thus promising applications to provide technical support for the advancement of ecological monitoring techniques and ecological cultivation.

A Triboelectric-Electromagnetic Hybrid Generator for Harvesting River Energy and Wind Energy for River Ecosystem Monitoring.

Currently, monitoring the ecological conditions of watercourses is overly unitary and inefficient and is burdened by high costs. A cost-effective, efficient, self-powered sensor for incorporating the Internet of Things (IoT) into the surveillance of riverine ecosystems is lacking. This manuscript introduces a device designed for energy harvesting and sensing through a triboelectric-electromagnetic generator (CX-TEHG). The CX-TEHG is composed of a wind-driven electromagnetic generator (F-EMG), a river-driven electromagnetic generator (W-EMG), a triboelectric nanogenerator for measuring flow velocity (W-TENG), and another triboelectric nanogenerator for gauging the speed of floodwater level rise (F-TENG). It employs planetary gears to achieve a 6-fold increase in speed, facilitating efficient multienergy collection from wind and river currents. CX-TEHG achieves a peak power output of 183 mW and a power density of 373.5 W/m3 under environmental conditions featuring a wind speed of 4 m/s and a flow velocity of 0.5 m/s. This study developed a cost-efficient signal acquisition system and a mechanism for information transmission via a 5G module. Alerts are issued on both upper-level computers and mobile devices for river flow velocities exceeding 2.8 m/s and water levels reaching specified locations; thus, an innovative solution for applying the Internet of Things in riverine ecological monitoring is presented.

Radar vegetation indices for monitoring surface vegetation: Developments, challenges, and trends.

Monitoring surface vegetation is essential for environmental protection, disaster prevention, and carbon sequestration in forests. However, optical remote-sensing methods and their derivative technologies typically fail to fully meet this requirement due to constraints such as lighting and weather. Radar vegetation indices (RVIs), developed based on microwave remote-sensing data, describe the dielectric properties and morphological structure of vegetation and have been applied for vegetation monitoring at various scales. This technical review is the first to systematically summarize RVIs; it analyzes and discusses their principles, developments, categories and applications, and provides a comprehensive guide for their use. Additionally, the challenges faced by RVIs, as well as their applicability, were analyzed, and future improvements and development trends were carefully projected. The selection of RVIs must consider the type of data used, the terrain and location of the study area, and the major vegetation types. The effectiveness of RVIs applied to vegetation monitoring can be affected by various factors, including index performance, sensor type, study area, and data type and quality. These factors reduce the reliability and robustness of results, as well as guide the improvement direction of RVIs. The development of technologies, such as artificial intelligence, in remote sensing offers new possibilities for RVIs, enabling the removal of background scattering, improvement in interpretation accuracy, and reduction in application thresholds. Additionally, the development trends in high resolution, multi-polarization, multi-base, multi-dimensional, and networked synthetic aperture radar (SAR) and their satellite platforms offer data support for the next generation of RVIs. The rapid development of RVIs strongly supports the use of surface vegetation monitoring and terrestrial ecosystem research.

A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams.

This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands-on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera-captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture-recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time-consuming compared with physical captures and tagging.

Ornamental Phoenix palm trees as habitat for fauna in the Mediterranean Region - results from a full year monitoring.

In the European Mediterranean Region, palm trees are a common element in cities and semi-urban landscapes and have become important habitat structures for local fauna. This study aimed to monitor the invertebrate and vertebrate fauna occurring on and associated with ornamental palms of the genus Phoenix, over the course of one year. Five study sites were used in southern Spain, with varying levels of management. Several complementary methods were applied monthly in order to assess the vertebrates and invertebrates associated with the full seasonal cycle of palms, including flowering and fruiting. The study resulted in the identification of 216 invertebrate families from seven different classes and 89 vertebrate species, consisting of 62 bird, 20 mammal (including bats), six reptile and one amphibian species associated with Phoenix palms. It thus highlights that Phoenix palms provide a habitat for many species and individuals over the course of one year.

Monitoring jellyfish outbreaks along Israel's Mediterranean coast using digital footprints.

With mounting global concerns about jellyfish outbreaks, monitoring their occurrence remains challenging. Tapping into the wealth of digital data that internet users share online, which includes reports of jellyfish sightings, may provide an alternative or complement to more conventional expert-based or citizen science monitoring. Here, we explore digital footprints as a data source to monitor jellyfish outbreaks along the Israeli Mediterranean coast. We compiled jellyfish sighting data for the period 2011-2022 from multiple platforms, including leading social media platforms, searches in the Google search engine, and Wikipedia page views. Employing time series analysis, cross-correlation, and various evaluation metrics for presence/absence data, we compared weekly data from three sources: digital footprints, citizen science, and traditional expert-based field monitoring. Consistent seasonal patterns emerge across datasets, with notable correlations, particularly in jellyfish abundance. The cross-correlation between digital footprint and citizen science data exceeds >0.7, with Twitter and Instagram showing the highest correlation. Citizen science data often precedes digital footprints by up to one week. Correlation with traditional, expert-based field monitoring is limited as a result of limited data availability. Digital footprints demonstrate substantial agreement with the other data sources regarding jellyfish presence/absence and major outbreaks, especially for data from Wikipedia, Twitter, and Instagram. Overall, we highlight digital footprint data as a reliable, cost-effective tool for passive monitoring of jellyfish outbreaks, which can aid characterization in data-scarce coastal regions, including retrospective assessment. Transferring and scaling up the proposed approach should consider data accessibility as well as platform relative popularity and usage in the regions under investigation.

A gentle introduction to computer vision-based specimen classification in ecological datasets.

Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time-consuming and expensive, limiting how much data a project can afford to process. Computer vision, a form of machine learning, can help overcome these problems by rapidly, automatically and accurately classifying images of specimens. Given the diversity of animal species and contexts in which images are captured, there is no universal classifier for all species and use cases. As such, ecologists often need to train their own models. While numerous software programs exist to support this process, ecologists need a fundamental understanding of how computer vision works to select appropriate model workflows based on their specific use case, data types, computing resources and desired performance capabilities. Ecologists may also face characteristic quirks of ecological datasets, such as long-tail distributions, 'unknown' species, similarity between species and polymorphism within species, which impact the efficacy of computer vision. Despite growing interest in computer vision for ecology, there are few resources available to help ecologists face the challenges they are likely to encounter. Here, we present a gentle introduction for species classification using computer vision. In this manuscript and associated GitHub repository, we demonstrate how to prepare training data, basic model training procedures, and methods for model evaluation and selection. Throughout, we explore specific considerations ecologists should make when training classification models, such as data domains, feature extractors and class imbalances. With these basics, ecologists can adjust their workflows to achieve research goals and/or account for uncertainty in downstream analysis. Our goal is to provide guidance for ecologists for getting started in or improving their use of machine learning for visual classification tasks.

The influence of spatial processes on fish community structure: using a metacommunity framework for freshwater bioassessment.

The use of biological indicators in a bioassessment approach is important for inferences of anthropogenic stress in routine monitoring programs. One of the primary assumptions of bioassessment is that biological indicators observed at specific sampling sites will allow for inferences of local environmental quality; however, this assumption requires a reliable understanding of dispersal processes, which is particularly relevant in river systems due to their dendritic network. Inter-stream dispersal between different points of the river network could bias bioassessment, especially for highly mobile organisms like fish. Here, we examine standard biological metrics used in routine biomonitoring to determine how spatial variables, including dispersal, influence inferences between fish populations and environmental gradients. Using redundancy analysis (RDA) and variation partitioning, we tested the relative influence of both environmental and spatial variables on fish community structure and related community metrics. Fish were collected from 99 sampling sites distributed across 44 rivers and streams of the Great Morava River Basin, Serbia. Electroconductivity, the percentage of agricultural areas, dissolved oxygen, ammonia, and nitrate-nitrogen were found to be significant environmental variables, while ten spatial predictors from broad- to small-scales were found to influence fish community structure and community metrics. Our results suggest that contemporary dispersal among streams influences fish community composition, but that trait-based metrics are less sensitive than basic measures of diversity to spatial processes. Our results highlight the need for spatially independent sampling, as well as the need to consider dispersal-based processes in routine biomonitoring.

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring.

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different frequencies of the landscape acoustic attribute and the relationship of said sounds with ecosystem health metrics and indicators (e.g., species richness, acoustic biodiversity, vectors of structural change, gradients of vegetation cover, landscape connectivity, and temporal and spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use of ARUs and their capacity to record hours of audio for months at a time have created a need for automatic processing methods to reduce time consumption, correlate variables implicit in the recordings, extract features, and characterize sound patterns related to landscape attributes. Consequently, traditional machine learning methods have been commonly used to process data on different characteristics of soundscapes, mainly the presence-absence of species. In addition, it has been employed for call segmentation, species identification, and sound source clustering. However, some authors highlight the importance of the new approaches that use unsupervised deep learning methods to improve the results and diversify the assessed attributes. In this paper, we present a systematic review of machine learning methods used in the field of ecoacoustics for data processing. It includes recent trends, such as semi-supervised and unsupervised deep learning methods. Moreover, it maintains the format found in the reviewed papers. First, we describe the ARUs employed in the papers analyzed, their configuration, and the study sites where the datasets were collected. Then, we provide an ecological justification that relates acoustic monitoring to landscape features. Subsequently, we explain the machine learning methods followed to assess various landscape attributes. The results show a trend towards label-free methods that can process the large volumes of data gathered in recent years. Finally, we discuss the need to adopt methods with a machine learning approach in other biological dimensions of landscapes.

Carabid Beetles (Coleoptera) as Indicators of Sustainability in Agroecosystems: A Systematic Review.

The sustainability of agroecosystems is at risk owing to continuous anthropogenic disturbance. As such, there is a need to evaluate indicator taxa that may be used to monitor the health of agricultural management systems. Carabid beetles are ubiquitous and functionally crucial in agroecosystems while at the same time are sensitive to the changes caused by management practices. Their quick response to anthropogenic disturbances has been proposed as a practical and realistic tool for monitoring the sustainability of agricultural practices. However, there is still disagreement about carabids as possible indicators of agroecosystem sustainability. We conducted a systematic review of the responses of carabid beetles to agricultural systems in different biogeographical areas. We examined whether these beetles could serve as potential indicators of agroecosystem sustainability. The ISI Web of Science, Google Scholar, and Scopus were used to search for papers published from 2000-2019. In total, we included 69 studies indicating the use of carabids to monitor the impact of management practices in agroecosystems. Most studies were conducted in European countries (n = 37), while Southern Africa and East Asia countries were significantly under-represented (n = 10). Carabid beetle response to agroecosystems varied between management practices, with biodiversity indices (n = 41: positive 60%, negative 19%, and neutral 19%) being the most measured response variable, followed by functional diversity (n = 28: positive 67%, negative 25%, and neutral 7%). Overall, our findings highlight the need for more research in underdeveloped countries, to investigate the potential of overlooked carabids and include response variables measuring functional diversity in assessing the sustainability of agricultural management. This will assist policy makers and land managers in making active and informed decisions about agroecological disturbances and management.

Monitoring the ecological restoration effect of land reclamation in open-pit coal mining areas: An exploration of a fusion method based on ZhuHai-1 and Landsat 8 data.

Land reclamation is a long-term, dynamic process; postreclamation monitoring and management are particularly important, and the use of remote sensing technology is a good way to conduct ecological quality monitoring and evaluations. In this study, we fused ZhuHai-1 and Landsat 8 data; selected the best band combinations to calculate ecological quality indicators such as the inverted red-edge chlorophyll index, modified soil moisture monitoring index, normalized difference built-up and soil index and land surface temperature; and constructed the fusion remote sensing ecological index to monitor the ecological restoration effect of the reclaimed area in Pingshuo, China. The results showed that the inverted red-edge chlorophyll index and modified soil moisture monitoring index had positive contributions, the normalized difference built-up and soil index had a low impact on the ecological quality of the study area, and the land surface temperature had a negative effect on ecological quality. The environment of the reclaimed area was better than that of the surrounding areas where these areas were affected by mining. The mean value of the fusion remote sensing ecological index showed a trend of "rising first, then falling" with increasing reclamation time. The ecological quality of the reclaimed area was best in areas with 20-22 years of reclamation time. The ecological condition of the area has been declining for 25 years or more of reclamation, so it is suitable to apply artificial intervention to ensure good ecological quality. The use of remote sensing technology for monitoring the effects of ecological restoration can provide a reference basis for the targeted and accurate implementation of land reclamation management measures.

Interactions of hydrology, geochemistry, and biodiversity in woodland ponds located in riverine floodplains: case study from Scotland.

The Gore Glen pond is located in the forested floodplain of the Gore River, in the Mid-Lothian region near Edinburgh, Scotland. This work has considered in detail the hydrology, hydrochemistry, and biodiversity of the pond together with all their interactions and in addition interactions with the adjacent Gore River. It is important as it is one of few studies considering all these issues for a pond located in a riverine floodplain. This work shows the pond is connected to the Gore River for discharge events in the river larger than a 1 in 5-year return period. For these events, the pond acts as an online storage feature and will attenuate the flow, thus contributing towards the mitigation of downstream flooding. These large events are also a potential source of the large amount of sediments, as well as adsorbed chemicals, accumulated in the pond. The open water surface of the pond is covered by duckweed, Lemna minor, for most of the year, and that has profound implications for the hydrobiological community and biogeochemical cycling. The system is characterised by nutrient release from sediments due to the extensive hypolimnion anoxia in the summer/autumn period caused by the decomposition of organic matter. Algae are mostly represented by epiphytic diatoms and the cyanobacterium Microcystis, but their development appears to be constrained by light penetration. The eutrophic conditions are also conducive for the population of Difflugia (Protozoa, Rhizopoda). This work improves our understanding of the ecological relations of testate amoebae-an important group which has recently been used as biological indicators for the analysis of paleoecological relationships and paleogeographical reconstructions.

Established and Emerging Research Trends in Norway Lobster, Nephrops norvegicus.

The burrowing crustacean decapod Nephrops norvegicus is a significant species in European Atlantic and Mediterranean fisheries. Research over the decades has mainly focused on behavioral and physiological aspects related to the burrowing lifestyle, since animals can only be captured by trawls when engaged in emergence on the seabed. Here, we performed a global bibliographic survey of all the scientific literature retrieved in SCOPUS since 1965, and terminology maps were produced with the VOSviewer software to reveal established and emerging research areas. We produced three term-map plots: term clustering, term citation, and term year. The term clustering network showed three clusters: fishery performance, assessment, and management; biological cycles in growth, reproduction, and behavior; and finally, physiology and ecotoxicology, including food products. The term citation map showed that intense research is developed on ecotoxicology and fishery management. Finally, the term year map showed that the species was first studied in its morphological and physiological aspects and more recently in relation to fishery and as a food resource. Taken together, the results indicate scarce knowledge on how burrowing behavior and its environmental control can alter stock assessment, because of the poor use of current and advanced monitoring technologies.

Ecosystem services in connected catchment to coast ecosystems: Monitoring to detect emerging trends.

There is an increasing need for long-term monitoring of ecosystems and their services to inform on-ground management. The supply of many ecosystem services relies on connections that span multiple ecosystems. Monitoring the underlying condition of interconnected ecosystems is therefore required to track effectiveness of past interventions and identify impending change. Here we test the performance of indicators of ecosystem services with the aim of identifying the time-scales over which indicators of ecosystem services responded to change. We chose a case-study of a catchment in Northern Australia, where water resource development is a threat to the river flows that support vegetation growth and the life-cycle of coastal fishery species. We developed a novel approach to performance testing that drew on state-space modelling to capture ecological dynamics, and structural equation modelling to capture covariation in indicator time series. We first quantified covariation among three ecological indicators that had time-series data: pasture biomass, vegetation greenness and barramundi catch per unit effort. Higher values of all indicators occurred in years with greater river flow. We then predicted the emergence times for each indicator, as the time taken for a trend in an indicator to emerge from the background of natural variation. Emergence times were > 10 years in all cases, quantified at 80 % and higher confidence levels. Past trends and current status of ecosystem service flows are often used by decision makers to directly inform near-term actions, particularly for provisioning services (such as barramundi catch) due to their important contribution to regional economies. We found that ecological indicators could be used to assess historical performance over decadal timespans, but not as short-term indicators of recent change. More generally, we offer an approach to performance testing of indicators. This approach could be useful for quantifying timescales of ecosystem response in systems where cross-ecosystem connections are important.

Effects of different management strategies on long-term trends of Australian threatened and near-threatened mammals.

Monitoring is critical to assess management effectiveness, but broadscale systematic assessments of monitoring to evaluate and improve recovery efforts are lacking. We compiled 1808 time series from 71 threatened and near-threatened terrestrial and volant mammal species and subspecies in Australia (48% of all threatened mammal taxa) to compare relative trends of populations subject to different management strategies. We adapted the Living Planet Index to develop the Threatened Species Index for Australian Mammals and track aggregate trends for all sampled threatened mammal populations and for small (<35 g), medium (35-5500 g), and large mammals (>5500 g) from 2000 to 2017. Unmanaged populations (42 taxa) declined by 63% on average; unmanaged small mammals exhibited the greatest declines (96%). Populations of 17 taxa in havens (islands and fenced areas that excluded or eliminated introduced red foxes [Vulpes vulpes] and domestic cats [Felis catus]) increased by 680%. Outside havens, populations undergoing sustained predator baiting initially declined by 75% but subsequently increased to 47% of their abundance in 2000. At sites where predators were not excluded or baited but other actions (e.g., fire management, introduced herbivore control) occurred, populations of small and medium mammals declined faster, but large mammals declined more slowly, than unmanaged populations. Only 13% of taxa had data for both unmanaged and managed populations; index comparisons for this subset showed that taxa with populations increasing inside havens declined outside havens but taxa with populations subject to predator baiting outside havens declined more slowly than populations with no management and then increased, whereas unmanaged populations continued to decline. More comprehensive and improved monitoring (particularly encompassing poorly represented management actions and taxonomic groups like bats and small mammals) is required to understand whether and where management has worked. Improved implementation of management for threats other than predation is critical to recover Australia's threatened mammals.

Efectos de diferentes estrategias de manejo sobre las tendencias a largo plazo de los mamíferos amenazados y casi amenazados de Australia Resumen El monitoreo es fundamental para evaluar la efectividad del manejo, aunque faltan evaluaciones sistemáticas y a gran escala de este monitoreo para evaluar y mejorar los esfuerzos de recuperación. Compilamos 1,808 series temporales de 71 especies y subespecies de mamíferos terrestres y voladores amenazadas y casi amenazadas en Australia (48% de todos los taxones de mamíferos amenazados) para comparar las tendencias relativas de las poblaciones sujetas a diferentes estrategias de manejo. Adaptamos el Índice Planeta Vivo para desarrollar el Índice de Especies Amenazadas para los Mamíferos Australianos y así rastrear las tendencias agregadas de todas las poblaciones muestreadas de mamíferos amenazados y de los mamíferos pequeños (<35 g), medianos (35-5,500 g) y grandes (>5,500 g) entre 2000 y 2017. Las poblaciones sin manejo (42 taxones) declinaron en un 63% en promedio; los mamíferos pequeños sin manejo exhibieron las declinaciones más marcadas (96%). Las poblaciones de 17 taxones incrementaron 680% en los refugios (islas o áreas encercadas que excluían o eliminaban al zorro rojo [Vulpes vulpes] y al gato doméstico [Felis catus], especies introducidas) Afuera de los refugios, las poblaciones sometidas al cebado constante de los depredadores en un inicio declinaron en un 75% pero después incrementaron al 47% de su abundancia para el 2000. En los sitios en donde los depredadores no fueron excluidos o cebados sino sometidos a otras acciones (manejo del fuego, control de herbívoros introducidos), las poblaciones de los mamíferos pequeños y medianos declinaron más rápido, pero los mamíferos grandes declinaron de manera más lenta que las poblaciones sin manejo. Sólo el 13% de los taxones contaron con datos para sus poblaciones con y sin manejo; las comparaciones entre índices para este subconjunto mostraron que los taxones con poblaciones en incremento dentro de los refugios declinaron afuera de éstos, pero los taxones con poblaciones sujetas al cebado de depredadores afuera de los refugios declinaron más lentamente que las poblaciones sin manejo y después incrementaron, mientras que las poblaciones sin manejo continuaron su declinación. Se requiere un monitoreo más completo y mejorado (particularmente el que engloba las acciones de manejo mal representadas y los grupos taxonómicos como los murciélagos y los mamíferos pequeños) para entender si ha funcionado el manejo y en dónde. La implementación mejorada del manejo para las amenazas distintas a la depredación es fundamental para recuperar a los mamíferos amenazados de Australia.

Temporal dynamics in alpine snowpatch plants along a snowmelt gradient explained by functional traits and strategies.

Alpine snowpatches are characterised by persistent snow cover, short growing seasons and periglacial processes, which has resulted in highly specialised plant communities. Hence, these snowpatch communities are among the most threatened from climate change. However, temporal dynamics in snowpatch microclimate and plant composition are rarely explored, especially in the marginal alpine environments of Australia. Seven snowpatches were categorised into early, mid and late snowmelt zones based on growing season length, with soil temperatures recorded from 2003 to 2020 and plant composition surveyed in 84 1 m2 quadrats in 2007, 2013 and 2020. Microclimate, species diversity, plant cover and composition, along with community-weighted trait means and plant strategies were assessed to understand snowpatch dynamics in response to climate change. We found that growing season length and temperatures have increased in late melt zones, while changes were less consistent in early and mid melt zones. There were few changes in species diversity, but increases in graminoids and declines in snowpatch specialists in mid and late melt zones. Community-weighted plant height, leaf area and leaf weight also increased, particularly in mid and late melt zones, while plant strategies shifted from compositions of ruderal-tolerant to stress-tolerant. Here, we show that snowpatch communities are rapidly changing in response to longer growing seasons and warmer temperatures, with the greatest changes occurring where snow persists the longest. The results highlight the climate-induced loss of defining biotic and abiotic characteristics of snowpatches, as temporal convergence of compositions along snowmelt gradients threatens the distinctiveness of snowpatch plant communities.

Laying it on thick: Ecosystem effects of sediment placement on a microtidal Rhode Island salt marsh.

Heightened recognition of impacts to coastal salt marshes from sea-level rise has led to expanding interest in using thin-layer sediment placement (TLP) as an adaptation tool to enhance future marsh resilience. Building on successes and lessons learned from the Gulf and southeast U.S. coasts, projects are now underway in other regions, including New England where the effects of TLP on marsh ecosystems and processes are less clear. In this study, we report on early responses of a drowning, microtidal Rhode Island marsh (Ninigret Marsh, Charlestown, RI) to the application of a thick (10-48 cm) application of sandy dredged material and complimentary extensive adaptive management to quickly build elevation capital and enhance declining high marsh plant species. Physical changes occurred quickly. Elevation capital, rates of marsh elevation gain, and soil drainage all increased, while surface inundation, die-off areas, and surface ponding were greatly reduced. Much of the marsh revegetated within a few years, exhibiting aspects of classic successional processes leading to new expansive areas of high marsh species, although low marsh Spartina alterniflora recovered more slowly. Faunal communities, including nekton and birds, were largely unaffected by sediment placement. Overall, sediment placement provided Ninigret Marsh with an estimated 67-320 years of ambient elevation gain, increasing its resilience and likely long-term persistence. Project stakeholders intentionally aimed for the upper end of high marsh plant elevation growth ranges to build elevation capital and minimize maintenance costs, which also resulted in new migration corridors, providing pathways for future marsh expansion.