Wetland Ecosystem Monitoring Through Water Quality Perspectives

Water quality monitoring is an important tool in determining the safety and suitability of water for various desired and intended uses. The procedures involved in the evaluation of water quality are numerous and multifaceted. Therefore, taking into consideration the specific objectives of water quality monitoring, sampling design is of vital importance. Most of the physical parameters of water quality are determined via in-situ measurements using modern testing equipment/field testing kits. Although there are some good field-based sensors that are being used for evaluation of water quality, the chemical parameters traditionally are mostly analyzed through laboratory-based experiments. This chapter is aimed to offer an inclusive knowledge and insights on the importance and assessment of physico-chemical parameters that are of high priority for monitoring the water quality of wetlands.

Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

Medium-term air quality assessment, benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes. By using daily and monthly averaged data, medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives. By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark (CLAB). The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator. Such a benchmark can provide an overall air quality assessment for areas of interest. A case study is presented for Dallas County (U.S.A.) applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019. Six air pollutants considered are: ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, benzene and particulate matter less than 2.5 micrometres. These pollutants are assessed individually and in terms of CLAB, and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019. Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years. The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs. Conducting CLAB analysis on an ongoing basis, using a relevant nearpast time interval for benchmarking that covers several years, can reveal useful monthly, seasonal and annual trends in overall air quality. This type of medium-term, benchmarked air quality data analysis is well suited for ecosystem monitoring.

Isotopic evidence of increasing water abundance and lake hydrological change in Old Crow Flats, Yukon, Canada

Lake-rich northern permafrost landscapes are sensitive to changing climate conditions, but ability to track real-time and potentially multiple hydrological responses (e.g. lake expansion, drawdown, drainage) is challenging due to absence of long-term, sustainable monitoring programs in these remote locations. Old Crow Flats (OCF), Yukon, is a Ramsar Wetland of International Importance where concerns about low water levels and their consequences for wildlife habitat and traditional ways of life prompted multidisciplinary studies during the International Polar Year (2007–2008) and led to the establishment of an aquatic ecosystem monitoring program. Here, we report water isotope data from 14 representative thermokarst lakes in OCF, the foundation of the monitoring program, and time-series of derived metrics including the isotope composition of input waters and evaporation-to-inflow ratios for a 13 year period (2007–2019). Although the lakes spanned multiple hydrological categories (i.e. rainfall-, snowmelt- and evaporation-dominated) based on initial surveys, well-defined trends from application of generalized additive models and meteorological records reveal that lakes have become increasingly influenced by rainfall, and potentially waters from thawing permafrost. These sources of input have led to more positive lake water balances. Given the documented role of rainfall in causing thermokarst lake drainage events in OCF and elsewhere, we anticipate increased vulnerability of lateral water export from OCF. This study demonstrates the value of long-term isotope-based monitoring programs for identifying hydrological consequences of climate change in lake-rich permafrost landscapes.

Te Ao Hurihuri

<p>At the heart of the thesis is the establishment of a new type of landscape practice based upon leveraging the power and potential of computational tools to serve cultural attitudes to land and land management. The research acknowledges that a new approach to landscape understanding is required, one that extends the current discipline’s mode of notation and representation/visualisation and ‘experience’ within the design process. It questions current forms of mapping and representational media and highlights limitations when communicating ‘non-traditional’ cartographic data, such as cultural and spiritual sites arguing that there are opportunities for a more holistic experiential interaction. By utilising a holistic approach influenced by key Māori kaupapa including kaitiakitanga, manaakitanga, and mauri, the research offers up a novel digital methodology that draws from a range of existing data (demographics, climate etc.) and initiates the creation or capturing of new data. This extended method of ‘bottom up’ data collection combined with virtual 3D modelling and visualisation, enables traditional understandings of landscape to extend to the experiential in the creation of an immersive, interactive and open collaborative 3D environment. This is further investigated through a process consisting of data conversion to mesh production for game engine use, incorporating diverse data sets to create new knowledge landscapes - an information-rich land model which in turn generates interactive 3D landscapes for end users. The process itself uses commonplace photogrammetry techniques as a means to capture selected areas of the cultural landscape recording both mesh and texture/image map. We then employ the software ‘Unreal Engine 4’ (Game development platform). The development of the gamification model allows location specific data to be ‘plugged in’ for landscape ecosystem monitoring also providing the potential for real time resource management. Future speculation of the cultural landscape enables climate events to be simulated and tested, giving an understanding of implications and risks with a view to local response and mitigation. From a design perspective the method/model allows designers to respond effectively with Māori end users and their real needs, potentially collapsing traditional modes of engagement and consultation between designer-client relationships providing a more bottom up collaborative approach.</p>

Te Ao Hurihuri

<p>At the heart of the thesis is the establishment of a new type of landscape practice based upon leveraging the power and potential of computational tools to serve cultural attitudes to land and land management. The research acknowledges that a new approach to landscape understanding is required, one that extends the current discipline’s mode of notation and representation/visualisation and ‘experience’ within the design process. It questions current forms of mapping and representational media and highlights limitations when communicating ‘non-traditional’ cartographic data, such as cultural and spiritual sites arguing that there are opportunities for a more holistic experiential interaction. By utilising a holistic approach influenced by key Māori kaupapa including kaitiakitanga, manaakitanga, and mauri, the research offers up a novel digital methodology that draws from a range of existing data (demographics, climate etc.) and initiates the creation or capturing of new data. This extended method of ‘bottom up’ data collection combined with virtual 3D modelling and visualisation, enables traditional understandings of landscape to extend to the experiential in the creation of an immersive, interactive and open collaborative 3D environment. This is further investigated through a process consisting of data conversion to mesh production for game engine use, incorporating diverse data sets to create new knowledge landscapes - an information-rich land model which in turn generates interactive 3D landscapes for end users. The process itself uses commonplace photogrammetry techniques as a means to capture selected areas of the cultural landscape recording both mesh and texture/image map. We then employ the software ‘Unreal Engine 4’ (Game development platform). The development of the gamification model allows location specific data to be ‘plugged in’ for landscape ecosystem monitoring also providing the potential for real time resource management. Future speculation of the cultural landscape enables climate events to be simulated and tested, giving an understanding of implications and risks with a view to local response and mitigation. From a design perspective the method/model allows designers to respond effectively with Māori end users and their real needs, potentially collapsing traditional modes of engagement and consultation between designer-client relationships providing a more bottom up collaborative approach.</p>

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas.

Diverse integrated ecosystem approach overcomes pandemic-related fisheries monitoring challenges

The COVID-19 pandemic caused unprecedented cancellations of fisheries and ecosystem-assessment surveys, resulting in a recession of observations needed for management and conservation globally. This unavoidable reduction of survey data poses challenges for informing biodiversity and ecosystem functioning, developing future stock assessments of harvested species, and providing strategic advice for ecosystem-based management. We present a diversified framework involving integration of monitoring data with empirical models and simulations to inform ecosystem status within the California Current Large Marine Ecosystem. We augment trawl observations collected from a limited fisheries survey with survey effort reduction simulations, use of seabird diets as indicators of fish abundance, and krill species distribution modeling trained on past observations. This diversified approach allows for evaluation of ecosystem status during data-poor situations, especially during the COVID-19 era. The challenges to ecosystem monitoring imposed by the pandemic may be overcome by preparing for unexpected effort reduction, linking disparate ecosystem indicators, and applying new species modeling techniques.

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

The microbiome emits informative signals of biological organization and environmental pressure that aid ecosystem monitoring and prediction. Are the many signals reducible to a habitat-specific portfolio that characterizes ecosystem health? Does an optimally structured microbiome imply a resilient microbiome? To answer these questions, we applied our novel Eco-Evo Mandala to bacterioplankton data from four habitats within the Great Barrier Reef, to explore how patterns in community structure, function and genetics signal habitat-specific organization and departures from theoretical optimality. The Mandala revealed communities departing from optimality in habitat-specific ways, mostly along structural and functional traits related to bacterioplankton abundance and interaction distributions (reflected by ϵ and λ as power law and exponential distribution parameters), which are not linearly associated with each other. River and reef communities were similar in their relatively low abundance and interaction disorganization (low ϵ and λ) due to their protective structured habitats. On the contrary, lagoon and estuarine inshore reefs appeared the most disorganized due to the ocean temperature and biogeochemical stress. Phylogenetic distances (D) were minimally informative in characterizing bacterioplankton organization. However, dominant populations, such as Proteobacteria, Bacteroidetes, and Cyanobacteria, were largely responsible for community patterns, being generalists with a large functional gene repertoire (high D) that increases resilience. The relative balance of these populations was found to be habitat-specific and likely related to systemic environmental stress. The position on the Mandala along the three fundamental traits, as well as fluctuations in this ecological state, conveys information about the microbiome’s health (and likely ecosystem health considering bacteria-based multitrophic dependencies) as divergence from the expected relative optimality. The Eco-Evo Mandala emphasizes how habitat and the microbiome’s interaction network topology are first- and second-order factors for ecosystem health evaluation over taxonomic species richness. Unhealthy microbiome communities and unbalanced microbes are identified not by macroecological indicators but by mapping their impact on the collective proportion and distribution of interactions, which regulates the microbiome’s ecosystem function.

Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.