pyveg: A Python package for analysing the time evolution of patterned vegetation using Google Earth Engine

Periodic vegetation patterns (PVP) arise from the interplay between forces that drive the growth and mortality of plants. Inter-plant competition for resources, in particular water, can lead to the formation of PVP. Arid and semi-arid ecosystems may be under threat due to changing precipitation dynamics driven by macroscopic changes in climate. These regions display some noteable examples of PVP, for example the “tiger bush” patterns found in West Africa. The morphology of the periodic pattern has been suggested to be linked to the resilience of the ecosystem (Mander et al., 2017; Trichon et al., 2018). Using remote sensing techniques, vegetation patterns in these regions can be studied, and an analysis of the resilience of the ecosystem can be performed. The pyveg package implements functionality to download and process data from Google Earth Engine (GEE), and to subsequently perform a resilience analysis on the aquired data. PVP images are quantified using network centrality metrics. The results of the analysis can be used to search for typical early warning signals of an ecological collapse (Dakos et al., 2008). Google Earth Engine Editor scripts are also provided to help researchers discover locations of ecosystems which may be in decline. pyveg is being developed as part of a research project looking for evidence of early warning signals of ecosystem collapse using remote sensing data. pyveg allows such research to be carried out at scale, and hence can be an important tool in understanding changing arid and semi-arid ecosystem dynamics. An evolving list of PVP locations, obtained through both literature and manual searches, is included in the package at pyveg/coordinates.py. The structure of the package is outlined in Figure 1, and is discussed in more detail in the following sections

Habitat-Net: Habitat interpretation using deep neural nets

Biological diversity is decreasing at a rate of 100-1000 times pre-human rates [1] [2], and tropical rainforests are among the most vulnerable ecosystems. To avoid species extinction, we need to understand factors influencing the occurrence of species. Fast, reliable computer-assisted tools can help to describe the habitat and thus to understand species habitat associations. This understanding is of utmost importance for more targeted species conservation efforts. Due to logistical challenges and time-consuming manual processing of field data, months up to years are often needed to progress from data collection to data interpretation. Deep learning can be used to significantly shorten the time while keeping a similar level of accuracy. Here, we propose Habitat-Net: a novel Convolutional Neural Network (CNN) based method to segment habitat images of rainforests. Habitat-Net takes color images as input and after multiple layers of convolution and deconvolution produces a binary segmentation of an image. The primary contribution of Habitat-Net is the translation of medical imaging knowledge (inspired by U-Net [3]) to ecological problems. The entire Habitat-Net pipeline works automatically without any user interaction. Our only assumption is the availability of annotated images, from which Habitat-Net learns the most distinguishing features automatically. In our experiments, we use two habitat datasets: (1) canopy and (2) understory vegetation. We train the model with 800 canopy images and 700 understory images separately. Our testing dataset has 150 canopy and 170 understory images. We use the Dice coefficient and Jaccard Index to quantify the overlap between ground-truthed segmentation images and those obtained by Habitat-Net model. This results in a mean Dice Score (mean Jaccard Index) for the segmentation of canopy and understory images of 0.89 (0.81) and 0.79 (0.69), respectively. Compared to manual segmentation, Habitat-Net prediction is approximately 3K – 150K times faster. For a typical canopy dataset of 335 images, Habitat-Net reduces total processing time to 5 seconds (15 milliseconds/ image) from 4 hours (45 seconds/ image). In this study, we show that it is possible to speed up the data pipeline using deep learning in the ecological domain. In the future, we plan to create a freely available mobile app based on Habitat-Net technology to characterize the habitat directly and automated in the field. In combination with ecological models our tools will help to understand the ecology of some poorly known, but often highly threatened, species and thus contribute to more timely conservation interventions. REFERENCES: 1. Sachs et al. "Biodiversity conservation and the millennium development goals." Science 325.5947 (2009): 1502-1503. 2. Chapin Iii, F. Stuart, et al. "Consequences of changing biodiversity." Nature 405.6783 (2000): 234. 3. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015

Quantitatively monitoring the resilience of patterned vegetation in the Sahel

Patterning of vegetation in drylands is a consequence of localized feedback mechanisms. Such feedbacks also determine ecosystem resilience—i.e. the ability to recover from perturbation. Hence, the patterning of vegetation has been hypothesized to be an indicator of resilience, that is, spots are less resilient than labyrinths. Previous studies have made this qualitative link and used models to quantitatively explore it, but few have quantitatively analysed available data to test the hypothesis. Here we provide methods for quantitatively monitoring the resilience of patterned vegetation, applied to 40 sites in the Sahel (a mix of previously identified and new ones). We show that an existing quantification of vegetation patterns in terms of a feature vector metric can effectively distinguish gaps, labyrinths, spots, and a novel category of spot–labyrinths at their maximum extent, whereas NDVI does not. The feature vector pattern metric correlates with mean precipitation. We then explored two approaches to measuring resilience. First we treated the rainy season as a perturbation and examined the subsequent rate of decay of patterns and NDVI as possible measures of resilience. This showed faster decay rates—conventionally interpreted as greater resilience—associated with wetter, more vegetated sites. Second we detrended the seasonal cycle and examined temporal autocorrelation and variance of the residuals as possible measures of resilience. Autocorrelation and variance of our pattern metric increase with declining mean precipitation, consistent with loss of resilience. Thus, drier sites appear less resilient, but we find no significant correlation between the mean or maximum value of the pattern metric (and associated morphological pattern types) and either of our measures of resilience

Shifting up a gear with iDNA: From mammal detection events to standardised surveys

Invertebrate-derived DNA (iDNA), in combination with high throughput sequencing, has been proposed as a cost-efficient and powerful tool to survey vertebrate species. Previous studies, however, have only provided evidence that vertebrates can be detected using iDNA, but have not taken the next step of placing these detection events within a statistical framework that allows for robust biodiversity assessments. Here, we compare concurrent iDNA and camera-trap surveys. Leeches were repeatedly collected in close vicinity to 64 camera-trap stations in Sabah, Malaysian Borneo. We analyse iDNA-derived mammalian detection events in a modern occupancy model that accounts for imperfect detection and compare the results with those from occupancy models parameterised with camera-trap-derived detection events. We also combine leech-iDNA and camera-trap data in a single occupancy model. We found consistent estimates of occupancy probabilities produced by our camera-trap and leech datasets. This indicates that the metabarcoding of leech-iDNA method provides reasonable estimates of occupancy and may be a suitable method for studying and monitoring mammal species in tropical rainforests. However, we also show that a more extensive collection of leeches would be needed to assess mammal biodiversity with a robustness similar to that of camera traps. As certain taxa were only detected in leeches, we see great potential in complementing camera-trap studies with the iDNA approach, as long as the collection of leeches follows a robust and standardised sampling scheme. Synthesis and applications. Here, we describe an approach to analyse detection records of mammals derived from leech samples using an occupancy framework that accounts for leech-specific factors influencing the detection probability. We further combined camera trap and leech data, which lead to increased confidence in occupancy estimates. Our approach is not restricted to the processing of leech samples, but can be used for the analysis of other invertebrate DNA and environmental DNA data. Our study is the first step to shift the application of invertebrate DNA studies from opportunistic ad-hoc collections to the systematic surveys required for long-term management of wildlife populations

A dynamic microsimulation model for epidemics.

Funder: Aerospace Technology InstituteFunder: UK Research and InnovationFunder: The Alan Turing InstituteA large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations

Stories of hope created together: A pilot, school-based workshop for sharing eco-emotions and creating an actively hopeful vision of the future

The climate and ecological crises challenge all communities across the world, with the greatest impact upon the most vulnerable and the youngest. There are multiple impacts on mental health, including the psychological burdens that arise with increasing awareness of the loss, threat and injustice caused by these crises. Large numbers of young people globally are understandably concerned and distressed about these crises, whilst simultaneously reporting that their concerns are regularly dismissed and ignored, particularly by those in power. This can increase feelings of isolation and distress, particularly if they have no recourse to effect change. This pilot project sought to explore how a schools-based, co-created workshop for school pupils aged 16 to 18 years could use a community-oriented space to explore their eco-emotions, address feelings of isolation and engender a sense of realistic, active hope, using storytelling and images of possible futures. A 3-h workshop for delivery in schools was co-designed with young people, researchers, educators and clinicians, using principles of Youth Participatory Action Research (YPAR). Six school pupils aged 16–18 years consented and four completed the workshop, which involved a range of group-based activities to explore their understanding of the climate and ecological crises, support emotional expression related to these and engage in storytelling about hopeful and realistic futures. A live illustrator in attendance created shared images of the participants’ fears and hopes. The workshop was recorded, transcribed and analysed using Thematic Analysis and sentiment analysis. Feedback was sought from participants at 1 and 4 weeks after completion and analysed using content analysis. Results indicated that participants reported a range of painful and positive emotions about the crises. They highly valued having space to express their experience alongside others. Storytelling and creativity appeared to help them articulate their feelings and hopes for the future, and gave them greater motivation and confidence in talking to others about these topics. This innovative pilot study suggests that a school-based youth participatory group could offer a novel way of helping young people to engage more with the climate and ecological crises in a way that supports their wellbeing. It provides strong support for future, larger-scale projects in this area

Committed global warming risks triggering multiple climate tipping points

Many scenarios for limiting global warming to 1.5°C assume planetary-scale carbon dioxide removal sufficient to exceed anthropogenic emissions, resulting in radiative forcing falling and temperatures stabilizing. However, such removal technology may prove unfeasible for technical, environmental, political, or economic reasons, resulting in continuing greenhouse gas emissions from hard-to-mitigate sectors. This may lead to constant concentration scenarios, where net anthropogenic emissions remain non-zero but small, and are roughly balanced by natural carbon sinks. Such a situation would keep atmospheric radiative forcing roughly constant. Fixed radiative forcing creates an equilibrium “committed” warming, captured in the concept of “equilibrium climate sensitivity.” This scenario is rarely analyzed as a potential extension to transient climate scenarios. Here, we aim to understand the planetary response to such fixed concentration commitments, with an emphasis on assessing the resulting likelihood of exceeding temperature thresholds that trigger climate tipping points. We explore transients followed by respective equilibrium committed warming initiated under low to high emission scenarios. We find that the likelihood of crossing the 1.5°C threshold and the 2.0°C threshold is 83% and 55%, respectively, if today's radiative forcing is maintained until achieving equilibrium global warming. Under the scenario that best matches current national commitments (RCP4.5), we estimate that in the transient stage, two tipping points will be crossed. If radiative forcing is then held fixed after the year 2100, a further six tipping point thresholds are crossed. Achieving a trajectory similar to RCP2.6 requires reaching net-zero emissions rapidly, which would greatly reduce the likelihood of tipping events

Impacts of meeting minimum access on critical earth systems amidst the Great Inequality

The Sustainable Development Goals aim to improve access to resources and services, reduce environmental degradation, eradicate poverty and reduce inequality. However, the magnitude of the environmental burden that would arise from meeting the needs of the poorest is under debate—especially when compared to much larger burdens from the rich. We show that the ‘Great Acceleration’ of human impacts was characterized by a ‘Great Inequality’ in using and damaging the environment. We then operationalize ‘just access’ to minimum energy, water, food and infrastructure. We show that achieving just access in 2018, with existing inequalities, technologies and behaviours, would have produced 2–26% additional impacts on the Earth’s natural systems of climate, water, land and nutrients—thus further crossing planetary boundaries. These hypothetical impacts, caused by about a third of humanity, equalled those caused by the wealthiest 1–4%. Technological and behavioural changes thus far, while important, did not deliver just access within a stable Earth system. Achieving these goals therefore calls for a radical redistribution of resources