The two most urgent and interlinked environmental challenges humanity faces are climate change and biodiversity loss. We are entering a pivotal decade for both the international biodiversity and climate change agendas with the sharpening of ambitious strategies and targets by the Convention on Biological Diversity and the United Nations Framework Convention on Climate Change. Within their respective Conventions, the biodiversity and climate interlinked challenges have largely been addressed separately. There is evidence that conservation actions that halt, slow or reverse biodiversity loss can simultaneously slow anthropogenic mediated climate change significantly. This review highlights conservation actions which have the largest potential for mitigation of climate change. We note that conservation actions have mainly synergistic benefits and few antagonistic trade‐offs with climate change mitigation. Specifically, we identify direct co‐benefits in 14 out of the 21 action targets of the draft post‐2020 global biodiversity framework of the Convention on Biological Diversity, notwithstanding the many indirect links that can also support both biodiversity conservation and climate change mitigation. These relationships are context and scale‐dependent; therefore, we showcase examples of local biodiversity conservation actions that can be incentivized, guided and prioritized by global objectives and targets. The close interlinkages between biodiversity, climate change mitigation, other nature's contributions to people and good quality of life are seldom as integrated as they should be in management and policy. This review aims to re‐emphasize the vital relationships between biodiversity conservation actions and climate change mitigation in a timely manner, in support to major Conferences of Parties that are about to negotiate strategic frameworks and international goals for the decades to come.

Conservation researchers require low-cost access to acoustic monitoring technology. However, affordable tools are often constrained to short-term studies due to high energy consumption and limited storage. To enable long-term monitoring, energy and space efficiency must be improved on such tools. This paper describes the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring on affordable, open-source hardware. The algorithms aim to detect bat echolocation, to search for evidence of an endangered cicada species, and also to collect evidence of poaching in a protected nature reserve. The algorithms are designed to run on AudioMoth: a low-cost, open-source acoustic monitoring device, developed by the authors and widely adopted by the conservation community. Each algorithm addresses a detection task of increasing complexity, implementing extra analytical steps to account for environmental conditions such as wind, analysing samples multiple times to prevent missed events, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm, we report on real-world deployments carried out with partner organisations and also benchmark the hidden Markov model against a convolutional neural network, a deep-learning technique commonly used for acoustics. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.

Ocean Census is a new Large-Scale Strategic Science Mission aimed at accelerating the discovery and description of marine species. This mission addresses the knowledge gap of the diversity and distribution of marine life whereby of an estimated 1 million to 2 million species of marine life between 75% to 90% remain undescribed to date. Without improved knowledge of marine biodiversity, tackling the decline and eventual extinction of many marine species will not be possible. The marine biota has evolved over 4 billion years and includes many branches of the tree of life that do not exist on land or in freshwater. Understanding what is in the ocean and where it lives is fundamental science, which is required to understand how the ocean works, the direct and indirect benefits it provides to society and how human impacts can be reduced and managed to ensure marine ecosystems remain healthy. We describe a strategy to accelerate the rate of ocean species discovery by: 1) employing consistent standards for digitisation of species data to broaden access to biodiversity knowledge and enabling cybertaxonomy; 2) establishing new working practices and adopting advanced technologies to accelerate taxonomy; 3) building the capacity of stakeholders to undertake taxonomic and biodiversity research and capacity development, especially targeted at low- and middle-income countries (LMICs) so they can better assess and manage life in their waters and contribute to global biodiversity knowledge; and 4) increasing observational coverage on dedicated expeditions. Ocean Census, is conceived as a global open network of scientists anchored by Biodiversity Centres in developed countries and LMICs. Through a collaborative approach, including co-production of science with LMICs, and by working with funding partners, Ocean Census will focus and grow current efforts to discover ocean life globally, and permanently transform our ability to document, describe and safeguard marine species.

Naturally ventilated offices enable users to control their environment through the opening of windows. Whilst this level of control is welcomed by users, it creates risk in terms of energy performance, especially during the heating season. In older office buildings, facilities managers usually obtain energy information at the building level. They are often unaware or unable to respond to non-ideal facade interaction by users often as a result of poor environmental control provision. In the summer months, this may mean poor use of free cooling opportunities, whereas in the winter space heating may be wasteful. This paper describes a low-cost, camera-based system to diagnose automatically the status of each window (open or closed) in a facade. The system is shown to achieve a window status prediction accuracy level of 90–97% across both winter and summer test periods in a case study building. A number of limitations are discussed including winter daylight hours, the impact of rain, and the use of fixed camera locations and how these may be addressed. Options to use this window-opening information to engage with office users are explored.