Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between  ∼ 2 and  ∼ 15&thinsp;cm horizontal resolution and accuracies of ±10&thinsp;cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along  ∼ 50&thinsp;m transects, ranged from 1.58 to 10.56&thinsp;cm for weekly SD and from 2.54 to 8.68&thinsp;cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71&thinsp;cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.</p

Use of Species Distribution Modeling in the Deep Sea

In the last two decades the use of species distribution modeling (SDM) for the study and management of marine species has increased dramatically. The availability of predictor variables on a global scale and the ease of use of SDM techniques have resulted in a proliferation of research on the topic of species distribution in the deep sea. Translation of research projects into management tools that can be used to make decisions in the face of changing climate and increasing exploitation of deep-sea resources has been less rapid but necessary. The goal of this workshop was to discuss methods and variables for modeling species distributions in deep-sea habitats and produce standards that can be used to judge SDMs that may be useful to meet management and conservation goals. During the workshop, approaches to modeling and environmental data were discussed and guidelines developed including the desire that 1) environmental variables should be chosen for ecological significance a priori; 2) the scale and accuracy of environmental data should be considered in choosing a modeling method; 3) when possible proxy variables such as depth should be avoided if causal variables are available; 4) models with statistically robust and rigorous outputs are preferred, but not always possible; and 5) model validation is important. Although general guidelines for SDMs were developed, in most cases management issues and objectives should be considered when designing a modeling project. In particular, the trade-off between model complexity and researcher’s ability to communicate input data, modeling method, results and uncertainty is an important consideration for the target audience

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value

Models of marine fish biodiversity : assessing predictors from three habitat classification schemes

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modeling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modeling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability

Global challenges for seagrass conservation

Seagrasses, flowering marine plants that form underwater meadows, play a significant global role in supporting food security, mitigating climate change and supporting biodiversity. Although progress is being made to conserve seagrass meadows in select areas, most meadows remain under significant pressure resulting in a decline in meadow condition and loss of function. Effective management strategies need to be implemented to reverse seagrass loss and enhance their fundamental role in coastal ocean habitats. Here we propose that seagrass meadows globally face a series of significant common challenges that must be addressed from a multifaceted and interdisciplinary perspective in order to achieve global conservation of seagrass meadows. The six main global challenges to seagrass conservation are (1) a lack of awareness of what seagrasses are and a limited societal recognition of the importance of seagrasses in coastal systems; (2) the status of many seagrass meadows are unknown, and up-to-date information on status and condition is essential; (3) understanding threatening activities at local scales is required to target management actions accordingly; (4) expanding our understanding of interactions between the socio-economic and ecological elements of seagrass systems is essential to balance the needs of people and the planet; (5) seagrass research should be expanded to generate scientific inquiries that support conservation actions; (6) increased understanding of the linkages between seagrass and climate change is required to adapt conservation accordingly. We also explicitly outline a series of proposed policy actions that will enable the scientific and conservation community to rise to these challenges. We urge the seagrass conservation community to engage stakeholders from local resource users to international policy-makers to address the challenges outlined here, in order to secure the future of the world’s seagrass ecosystems and maintain the vital services which they supply

Reviewing the use of resilience concepts in forest sciences

Purpose of the review Resilience is a key concept to deal with an uncertain future in forestry. In recent years, it has received increasing attention from both research and practice. However, a common understanding of what resilience means in a forestry context, and how to operationalise it is lacking. Here, we conducted a systematic review of the recent forest science literature on resilience in the forestry context, synthesising how resilience is defined and assessed. Recent findings Based on a detailed review of 255 studies, we analysed how the concepts of engineering resilience, ecological resilience, and social-ecological resilience are used in forest sciences. A clear majority of the studies applied the concept of engineering resilience, quantifying resilience as the recovery time after a disturbance. The two most used indicators for engineering resilience were basal area increment and vegetation cover, whereas ecological resilience studies frequently focus on vegetation cover and tree density. In contrast, important social-ecological resilience indicators used in the literature are socio-economic diversity and stock of natural resources. In the context of global change, we expected an increase in studies adopting the more holistic social-ecological resilience concept, but this was not the observed trend. Summary Our analysis points to the nestedness of these three resilience concepts, suggesting that they are complementary rather than contradictory. It also means that the variety of resilience approaches does not need to be an obstacle for operationalisation of the concept. We provide guidance for choosing the most suitable resilience concept and indicators based on the management, disturbance and application context

Unveiling Opportunities for Local Circular Bioeconomy Systems Using an Open Innovation Approach

Background, motivation, and scope. There is increasing incentive from governments for companies to transition to a circular bioeconomy (CBE), especially in Europe. A Bioeconomy is rooted in renewable resources. In a CBE, biomass is used to make products of added value, seeking to keep the value at its maximum at all times. Nonetheless, such a transition does not happen overnight, and until we are able to satisfy societal needs with sustainably sourced biomass, we can make the case for a circular bioeconomy proving its value by recovering value from waste streams. This research takes an exploratory stance, proposing a project approach to answer the following question: how can we build local circular bioeconomy systems by upcycling biomass waste? This study is scoped down to explore municipal biowaste, such as beach wrack (i.e., seaweed and eelgrass), straw, and grass clippings, in the capital region of Denmark and Southern Sweden, as part of the project Power Bio (https://www.gate21.dk/powerbio/). Methods. This exploratory study dwells on the possible methods to be used to build local circular bioeconomy systems to make use of biomass waste. The methods to be used are rooted in investigative research (Step 1), innovation sprints (Step 2), and stakeholder dialogue (Step 3), as described hereafter. Step 1: Investigate the biomasses. The biomass wastes should be collected and analysed to get to know their content, such as chemical and physical composition. This allows one to start identifying for what purposes parts of the biomass, or the whole of it, can be used. Step 2. Run innovation sprints (e.g., Hackathons). They can be run with two purposes, (i) finding potential products, for instance by posing an open question such as “what can be made from grass clippings?”, or (ii) finding answers to specific challenges, such as “how do we collect and sort beach wrack in order to avoid undesired materials coming along (e.g., plastic waste left on the beach)?”. The results of Step 1 should be made available to the participants in the innovation sprints. These can be run in two phases, where in the first phase challenges are made available, participants (companies, university students, citizens) register and submit a summary of a potential solution, the best ranked solutions are invited for a second phase, where they have closer dialogue with the problem owners, have the chance to further develop their solution, produce a prototype, and pitch their idea at a final event, at the end of the innovation sprint. The best solutions win the right to test their solution at a technological institute (technical advice, knowledge, facilities, time, personnel, are provided for the tests). Step 3. Establish a dialogue between the private initiative and the local government and other stakeholders, aiming to identify gaps that need to be bridged to build local circular bioeconomy system. After the testing phase, the best solutions can be pointed out. To put them into practice, a dialogue between the companies that can implement those solutions and the local government should be initiated, in order to reach agreements on how the market, the supply chain, and the regulations and incentives for such solutions to come to life can take place. Expected Results. A series of results can be expected at the end of this process, including: (1) a list of potential products that can be made from the biomass wastes. Even if not feasible, they can serve as inspiration for other potential products that might result from the use of the biomasses; (2) a list of the industries that can benefit from the final products. One can identify what industries are more likely to benefit from the products that can be made from the biomass wastes. This can be an input to policy initiatives; (3) insights into the most valuable components. One can hope to identify whether only a few components of the biomass are being valued, or all of it, and why. One can also draw on strategies to valorize the less valuable ones; (4) identify what supply chain links are needed to establish local circular bioeconomy systems. One can hope to identify what is needed to build supply chains in order for the products to be brought to the market. This translates into what incentives need to be created, and refers back to Step 3, stakeholder dialogue. Further Research. At the end of this process, one will still be left with the challenges of investigating (a) potential sustainability impacts of the new products (from waste), including financial, social, and environmental sustainability, and (b) potential impacts of substitution (what old products they replace, and which option is sustainably better)