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1. Reframing the payments for ecosystem services framework in a coupled human and natural systems context: strengthening the integration between ecological and human dimensions

   https://doi.org/10.1080/20964129.2017.1335931  

   Lewison, Rebecca L. ; An, Li ; Chen, Xiaodong ( May 2017 , Ecosystem Health and Sustainability)

   Introduction:As challenges to biodiversity mount, land-use policies have been implemented to balance human needs and the integrity of ecological systems. One such program, Payments for Ecosystem Services (PES), incentivizes resource users to protect ecosystem services and has been implemented around the world to reduce soil erosion, create or improve wildlife habitats, and improve water quality and other environmental goals. The PES policy, at its core, is a concept that aims to capture the reciprocal relationships between human systems and ecological function and process. As such, PES epistemologically embodies a coupled human and natural systems approach.

   Outcomes:Yet, despite this conceptual alignment, the on-the-ground implementation or evaluation of PES typically does not adopt this coupled approach and PES programs have little integration between socioeconomic, sociocultural, human demographic, and ecological elements. To advance the evolution of PES, we consider what and how socioeconomic and ecological factors have been incorporated into PES program implementation and evaluation. We also present a conceptual model to articulate how PES research can capture the reciprocal relationships among socioeconomics, demography, and ecology and discuss the quantitative modeling approaches that can support this conceptual development, i.e., structural equation and agent-based modeling, and latent trajectory models.

   Conclusions:By strengthening the conceptualmore »framework for PES within a coupled human and natural systems approach and identifyinganalytical approaches that can be used to quantify and characterize these complex cross-disciplinary relationships, we aim to support the evolution and advancement of PES, in service of more meaningful and positive outcomes for human well-being and ecological sustainability.

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2. Grand challenges in biodiversity–ecosystem functioning research in the era of science–policy platforms require explicit consideration of feedbacks

   https://doi.org/10.1098/rspb.2021.0783  

   O'Connor, Mary I. ; Mori, Akira S. ; Gonzalez, Andrew ; Dee, Laura E. ; Loreau, Michel ; Avolio, Meghan ; Byrnes, Jarrett E. ; Cheung, William ; Cowles, Jane ; Clark, Adam T. ; et al ( October 2021 , Proceedings of the Royal Society B: Biological Sciences)

   Feedbacks are an essential feature of resilient socio-economic systems, yet the feedbacks between biodiversity, ecosystem services and human wellbeing are not fully accounted for in global policy efforts that consider future scenarios for human activities and their consequences for nature. Failure to integrate feedbacks in our knowledge frameworks exacerbates uncertainty in future projections and potentially prevents us from realizing the full benefits of actions we can take to enhance sustainability. We identify six scientific research challenges that, if addressed, could allow future policy, conservation and monitoring efforts to quantitatively account for ecosystem and societal consequences of biodiversity change. Placing feedbacks prominently in our frameworks would lead to (i) coordinated observation of biodiversity change, ecosystem functions and human actions, (ii) joint experiment and observation programmes, (iii) more effective use of emerging technologies in biodiversity science and policy, and (iv) a more inclusive and integrated global community of biodiversity observers. To meet these challenges, we outline a five-point action plan for collaboration and connection among scientists and policymakers that emphasizes diversity, inclusion and open access. Efforts to protect biodiversity require the best possible scientific understanding of human activities, biodiversity trends, ecosystem functions and—critically—the feedbacks among them.
3. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less
4. Reviews and syntheses: A framework to observe, understand and project ecosystem response to environmental change in the East Antarctic Southern Ocean

   https://doi.org/10.5194/bg-19-5313-2022  

   Gutt, Julian ; Arndt, Stefanie ; Barnes, David Keith ; Bornemann, Horst ; Brey, Thomas ; Eisen, Olaf ; Flores, Hauke ; Griffiths, Huw ; Haas, Christian ; Hain, Stefan ; et al ( January 2022 , Biogeosciences)

   Abstract. Systematic long-term studies on ecosystem dynamics are largely lacking from the East Antarctic Southern Ocean, although it is well recognized that they are indispensable to identify the ecological impacts and risks of environmental change. Here, we present a framework for establishing a long-term cross-disciplinary study on decadal timescales. We argue that the eastern Weddell Sea and the adjacent sea to the east, off Dronning Maud Land, is a particularly well suited area for such a study, since it is based on findings from previous expeditions to this region. Moreover, since climate and environmental change have so far been comparatively muted in this area, as in the eastern Antarctic in general, a systematic long-term study of its environmental and ecological state can provide a baseline of the current situation, which will be important for an assessment of future changes from their very onset, with consistent and comparable time series data underpinning and testing models and their projections. By establishing an Integrated East Antarctic Marine Research (IEAMaR) observatory, long-term changes in ocean dynamics, geochemistry, biodiversity, and ecosystem functions and services will be systematically explored and mapped through regular autonomous and ship-based synoptic surveys. An associated long-term ecological research (LTER) programme, includingmore »experimental and modelling work, will allow for studying climate-driven ecosystem changes and interactions with impacts arising from other anthropogenic activities. This integrative approach will provide a level of long-term data availability and ecosystem understanding that are imperative to determine, understand, and project the consequences of climate change and support a sound science-informed management of future conservation efforts in the Southern Ocean.« less
5. Designing agricultural landscapes for arthropod-based ecosystem services in North America

   https://doi.org/10.1016/bs.aecr.2021.01.003  

   Haan, Nathan L. ; Iuliano, Benjamin G. ; Gratton, Claudio ; Landis, Douglas A. ( March 2021 , Advances in ecological research)

   Agricultural landscapes in North America have developed through complex interactions of biophysical, socioeconomic and technological forces. While they can be highly productive, these landscapes are increasingly simplified, causing biodiversity loss. As a result, ecosystem services associated with biodiversity are being dismantled. Agricultural landscape structure arises from collective decisions of farmers over long time periods, which are usually not intentionally coordinated beyond the farm scale. Regaining ecosystem services will require active efforts to intentionally redesign landscapes, in part based on ecological evidence about relationships between landscape structure and ecosystem services. Here we focus on services provided by arthropods and how to foster them at landscape scales. We first provide a brief history of how agricultural landscape structure in temperate North America developed and review the landscape-scale ecological drivers underpinning arthropod-based ecosystem services. We then propose ecological and social principles for designing agricultural landscapes, based on the ecological evidence we reviewed and on previous efforts in agricultural landscape design. Finally, we look ahead to discern prospects for putting agricultural landscape design into practice, including ecological, technological and policy opportunities. To reap benefits from arthropod-based services, future agricultural landscapes will need to increase in structural heterogeneity and diversity across multiple dimensions including crop,more »farmer and consumer diversity. A number of knowledge gaps persist, including how to design landscapes at spatial scales that are relevant to service providers, identifying areas of overlap or conflict between design for ecosystem services and for biodiversity conservation more broadly and navigating the social and political processes needed to implement landscape design.« less