Balancing making a difference with making a living in the conservation sector

Goals play important roles in people's lives by focusing attention, mobilizing effort, and sustaining motivation. Understanding conservationists’ satisfaction with goal progress may provide insights into real-world environmental trends and flag risks to their well-being and motivation. We asked 2694 conservationists working globally how satisfied they were with progress towards goals important to them. We then explored how this satisfaction varied between groups. Finally, we looked at respondents' experiences associated with goal progress satisfaction. Many (94.0%) said “making a meaningful contribution to conservation” was an important goal for them, with over half being satisfied or very satisfied in this area (52.5%). However, respondents were generally dissatisfied with progress to collective conservation goals, such as stopping species loss, echoing formal assessments. Some groups were more likely to report dissatisfaction than others. For instance, those in conservation for longer tended to be less satisfied with collective goal progress (log-odds -0.21, 95% credibility interval (CI) -0.32 to -0.10), but practitioners reported greater satisfaction (log-odds 0.38, 95% CI 0.15-0.60). Likewise, those who are more optimistic in life (log-odds 0.24, 95% CI 0.17-0.32), male (log-odds 0.25, 95% CI 0.10-0.41), and working in conservation practice (log-odds 0.25, 95% CI 0.08-0.43) reported greater satisfaction with individual goal progress. Free-text responses suggested widespread dissatisfaction around livelihood goals, particularly related to job security and adequate compensation. While contributing to conservation appeared to be a source of satisfaction, slow goal progress in other areas – particularly around making a living – looked to be a source of distress and demotivation. Employers, funders, professional societies, and others should consider ways to help those in the sector make a difference whilst making a living, including by prioritizing conservationists' well-being when allocating funding. This support could include avoiding exploitative practices, fostering supportive work environments, and celebrating positive outcomes

Personal traits predict conservationists’ optimism about outcomes for nature

In the face of unprecedented biodiversity loss, the belief that conservation goals can be met could play an important role in ensuring they are fulfilled. We asked conservationists how optimistic they felt about key biodiversity outcomes over the next 10 years; 2341 people familiar with conservation in 144 countries responded. Respondents expressed optimism that enabling conditions for conservation would improve but felt pressures would continue, and the state of biodiversity was unlikely to get better. Respondents with greater general optimism about life, at early-career stages, and working in practice and policy (compared to academia) reported higher conservation optimism. But most of our biodiversity and conservation status indicators were not associated with conservation optimism. Unbounded optimism without appropriate action would be misguided in the face of growing threats to biodiversity. However, supporting those struggling to see the light at the end of the tunnel could help sustain efforts to overcome these threats

Residents transitioning between hospital and care homes: protocol for codesigning a systems-level response to safety issues (SafeST study)

Introduction: The aim of this study is to develop a better understanding of incident reporting in relation to transitions in care between hospital and care home, and to codesign a systems- level response to safety issues for patients transitioning between hospital and care home. Methods and analysis: Two workstreams (W) will run in parallel. W1 will aim to develop a taxonomy of incident reporting in care homes, underpinned by structured interviews (N=150) with care home representatives, scoping review of care home incident reporting systems, and a review of incident reporting policy related to care homes. The taxonomy will be developed using a standardised approach to taxonomy development. W2 will be structured in three phases (P). P1a will consist of ≤40 interviews with care home staff to develop a better understanding of their specific internal systems for reporting incidents, and P1b will include ≤30 interviews with others involved in transitions between hospital and care home. P1a and P1b will also examine the impact of the SARS- CoV- 2 pandemic on safe transitions. P2 will consist of a retrospective documentary analysis of care home data relating to resident transitions, with data size and sampling determined based on data sources identified in P1a. A validated data extraction form will be adapted before use. P3 will consist of four validation and codesign workshops to develop a service specification using National Health Service Improvement’s service specification framework, which will then be mapped against existing systems and recommendations produced. Framework analysis informed by the heuristic of systemic risk factors will be the primary mode of analysis, with content analysis used for analysing incident reports. Ethics and dissemination: The study has received university ethical approval and Health Research Authority approval. Findings will be disseminated to commissioners, providers and regulators who will be able to use the codesigned service specification to improve integrated care

Ethical considerations when conservation research involves people

Social science is becoming increasingly important in conservation, with more studies involving methodologies that collect data from and about people. Conservation science is a normative and applied discipline designed to support and inform management and practice. Poor research practice risks harming participants, researchers, and can leave negative legacies. Often, those at the forefront of field‐based research are early‐career researchers, many of whom enter their first research experience ill‐prepared for the ethical conundrums they may face. Here, we draw on our own experiences as early‐career researchers to illuminate how ethical challenges arise during conservation research that involves human participants. Specifically, we discuss ethical review procedures, conflicts of values, and power relations, and provide broad recommendations on how to navigate ethical challenges when they arise during research. We encourage greater engagement with ethical review processes and highlight the pressing need to develop ethical guidelines for conservation research that involves human participants.Output Status: Forthcoming/Available Onlin

Challenges and improvements associated with transitions between hospitals and care homes during the COVID-19 pandemic: a qualitative study with care home and healthcare staff in England

Background: Care home residents transitioning from hospital are at risk of receiving poor-quality care with their safety being challenged by the SARS-CoV-2 virus (COVID-19) pandemic. Little is known about how care home staff worked with hospital staff and other healthcare professionals to address these challenges and make improvements to increase patient safety. Objective: To gain insight into how the COVID-19 pandemic influenced the safety of transitions between hospital and care home. Method: Semi-structured interviews were conducted with care home staff and healthcare professionals involved in hospital to care home transitions including doctors, nurses, paramedics, pharmacists, social workers, and occupational therapists. Commonalities and patterns in the data were identified using thematic analysis. Results: Seventy participants were interviewed. Three themes were developed, first, ‘new challenges’, described care homes were pressurised to receive hospital patients amidst issues with COVID-19 testing, changes to working practices and contentious media attention, which all impacted staff negatively. Second, ‘dehumanisation’ described how care home residents were treated, being isolated from others amounted to feelings of being imprisoned, caused fear and engendered negative reactions from families. Third, ‘better ways of working’ described how health and social care workers developed relationships that improved integration and confidence and benefited care provision. Conclusion: The COVID-19 pandemic contributed to and compounded high-risk hospital-to-care home discharges. Government policy failed to support care homes. Rapid discharge objectives exposed a myriad of infection control issues causing inhumane conditions for care home residents. However, staff involved in transitions continued to provide and improve upon care provision

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. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to 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. All reference materials required to build the system are provided in open-source repositories. Results show the system can operate for a minimum of 3 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 min, though some outliers showed delays of 5-days or more when the system was incorrectly positioned and unable to connect to the Iridium network. We anticipate significant developments in this field 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 including real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

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.We modified an off‐the‐shelf camera trap (Bushnell™) and customised existing open‐source hardware to 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. All reference materials required to build the system are provided in open‐source repositories.Results show the system can operate for a minimum of 3 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 min, though some outliers showed delays of 5‐days or more when the system was incorrectly positioned and unable to connect to the Iridium network.We anticipate significant developments in this field 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 including real‐time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas

Robust ecological analysis of camera trap data labelled by a machine learning model

1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time‐consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. 2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case‐study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. 3. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. 4. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.Additional co-authors: Cisquet Kiebou Opepa, Ross T. Pitman, Hugh S. Robinso

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

Health, education, and social care provision after diagnosis of childhood visual disability

Aim: To investigate the health, education, and social care provision for children newly diagnosed with visual disability.Method: This was a national prospective study, the British Childhood Visual Impairment and Blindness Study 2 (BCVIS2), ascertaining new diagnoses of visual impairment or severe visual impairment and blindness (SVIBL), or equivalent vi-sion. Data collection was performed by managing clinicians up to 1-year follow-up, and included health and developmental needs, and health, education, and social care provision.Results: BCVIS2 identified 784 children newly diagnosed with visual impairment/SVIBL (313 with visual impairment, 471 with SVIBL). Most children had associated systemic disorders (559 [71%], 167 [54%] with visual impairment, and 392 [84%] with SVIBL). Care from multidisciplinary teams was provided for 549 children (70%). Two-thirds (515) had not received an Education, Health, and Care Plan (EHCP). Fewer children with visual impairment had seen a specialist teacher (SVIBL 35%, visual impairment 28%, χ2p < 0.001), or had an EHCP (11% vs 7%, χ2p < 0 . 01).Interpretation: Families need additional support from managing clinicians to access recommended complex interventions such as the use of multidisciplinary teams and educational support. This need is pressing, as the population of children with visual impairment/SVIBL is expected to grow in size and complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited