Effective wildlife monitoring is a prerequisite for effective wildlife conservation since, without time-series data on species populations and threats, evidence-based adaptive management will be difficult to achieve. Technological advances in remote sensing offer more opportunities for data collection than ever before. However, if we are to enhance data sharing and the use of data by decision-makers, methods must be relevant to local user needs and be integrated into monitoring schemes with appropriate goals and indicators.

In recent years, conservation project managers have increasingly turned to technological innovations to enhance wildlife monitoring, and remote-sensing devices deployed in space, in the air and on the ground are more realistic and affordable options than ever before. Satellite-based remote sensing of wildlife habitats and (sometimes) wildlife populations (see Pettorelli et al. Reference Pettorelli, Laurance, O’Brien, Wegmann, Nagendra and Turner2014) has been complemented by the newest generation of Earth-based sensors, including camera traps (Rovero & Zimmermann Reference Rovero and Zimmermann2016, Murphy et al. Reference Murphy, Goodman, Farris, Karpanty, Andrianjakarivelo and Kelly2017), acoustic recording devices (Alvarez-Berríos et al. Reference Alvarez-Berríos, Campos-Cerqueira, Hernández-Serna, Delgado, Román-Dañobeytia and Aide2016, Deichmann et al. Reference Deichmann, Hernández-Serna, Campos-Cerqueira and Aide2017) and unmanned aerial vehicles or drones (Christie et al. Reference Christie, Gilbert, Brown, Hatfield and Hanson2016, Thapa et al. Reference Thapa, Thapa, Thapa, Jnawali, Wich, Poudyal and Karki2018). These sensors, as well as emerging methods such as environmental DNA monitoring for tracking community composition (Biggs et al. Reference Biggs, Ewald, Valentini, Gaboriaud, Dejean and Griffiths2015, Valentini et al. Reference Valentini, Taberlet, Miaud, Civade, Herder and Thomsen2016) and genetic monitoring for identifying individuals within populations (e.g., Gray et al. Reference Gray, Roy, Vigilant, Fawcett, Basabose and Cranfield2013), provide new opportunities for enhancing the quality and volume of wildlife monitoring data and reducing the time people need to spend on the ground to collect it. If used in systematic ways (e.g., Beaudrot et al. Reference Beaudrot, Ahumada, O’Brien, Alvarez-Loayza, Boekee and Campos-Arceiz2016), remote sensing can also help fill the data gaps that exist in high-biodiversity tropical countries (McRae et al. Reference McRae, Deinet and Freeman2017) and help build time-series data of higher temporal and spatial resolution.

However, there is a risk that excitement over the technologies, encouraged by donors keen to show their support for innovation, may lead to practitioners deciding on which tools to use before they have decided on what they want to measure. Among the numerous blockages to the collection and use of biodiversity data for management, weak monitoring plans and tools that are poorly adapted to local conditions are cited regularly as problems (Stephenson et al. Reference Stephenson, Bowles-Newark, Regan, Stanwell-Smith, Diagana and Hoft2017a). Remote sensing therefore needs to be applied only when appropriate to the local situation and when it can be used to answer specific monitoring questions. The decision to use technology should also be based on project objectives and the availability of appropriate budgets and technical skills (Schmeller et al. Reference Schmeller, Böhm, Arvanitidis, Barber-Meyer, Brummitt and Chandler2017).

Guidance abounds on how to develop monitoring plans (e.g., BirdLife International 2006, CMP 2013) but, essentially, an appropriate monitoring system for a biodiversity project can be developed by answering the following five questions: (1) What are we trying to achieve (i.e., which species or habitats are we targeting and what do we want to see happen to them as a result of our actions)? (2) What does success look like (i.e., what quantitative changes do we expect to bring about in biodiversity and the pressures that threaten it)? (3) What do we need to measure to demonstrate if we have achieved success (i.e., what indicators do we select)? (4) How do we collect data to measure success (i.e., what monitoring methods, tools and protocols will we use? Are remote sensing devices relevant and feasible)? (5) How will we use the data for adaptive management (i.e., how should data be analysed and in what format should they be presented? What decisions need to be taken to respond to the trends identified)?

Many conservation agencies use the pressure–state–benefit–response model (an interlinked indicator framework that measures how well actions reduce threats and improve biodiversity and human livelihoods) to gain a better understanding of the complexities of conservation action (Sparks et al. Reference Sparks, Butchart, Balmford, Bennun, Stanwell-Smith and Walpole2011, Stephenson et al. Reference Stephenson, Burgess, Jungmann, Loh, O’Connor and Oldfield2015a). In this context, animal and plant population trends are the ultimate state indicator, confirming how target species are faring. Therefore, wildlife monitoring should be a necessary and key management practice for any stakeholder trying to conserve or manage populations, whether a government, non-governmental organization, local community, donor or business. However, to be effective and to learn from recent research, wildlife monitoring schemes (especially those using remote sensing) should be developed and implemented while taking into account key issues around monitoring design, indicator selection, data collection methods and protocols and data sharing (Table 1). Furthermore, it is essential that more effort is made by conservation agencies and donors to support the development of capacity for monitoring where it is most needed: in high-biodiversity countries (Schmeller et al. Reference Schmeller, Böhm, Arvanitidis, Barber-Meyer, Brummitt and Chandler2017, Stephenson et al. Reference Stephenson, Brooks, Butchart, Fegraus, Geller and Hoft2017b). It is also important to document and share examples of wildlife monitoring, highlighting what works well and what works less well (Stephenson et al. Reference Stephenson, O’Connor, Reidhead and Loh2015b). This is especially important with remote sensing, as practitioners need help with understanding the relative advantages and limitations of different tools.

Table 1.

Key issues to consider in developing and implementing a wildlife monitoring scheme.

In conclusion, remote sensing offers many opportunities for wildlife data collection if integrated into well-structured monitoring plans with clear goals and standardized protocols. However, remote-sensing techniques have their limitations (Christie et al. Reference Christie, Gilbert, Brown, Hatfield and Hanson2016, Aebischer et al. Reference Aebischer, Siguindo, Rochat, Arandjelovic, Heilman and Hickisch2017), and if we are to move beyond a focus on large mammals and birds to include less well-known fauna, modern technology should be complemented by traditional field survey methods (Stephenson et al. Reference Stephenson, Brooks, Butchart, Fegraus, Geller and Hoft2017b). Therefore, in many wildlife monitoring schemes, drone-based and satellite-based sensors, camera traps and acoustic recording devices ought to be used alongside people in boots on the ground.