- Top of page
- Materials and methods
- Supporting Information
The importance of ecological monitoring for conservation has often been acknowledged (Stem et al. 2005; Nichols & Williams 2006). Among its main objectives are to inform management decisions, measure success against stated objectives and learn about the system (Yoccoz, Nichols & Boulinier 2001). Monitoring is, however, often inadequate. Insufficient statistical power, lack of goal and hypothesis formulation, faulty survey design and data quality are common problems affecting monitoring schemes world-wide (Legg & Nagy 2006). The implications of these problematic issues are multiple; they not only affect monitoring effectiveness but also reduce resource availability for other potentially useful conservation interventions (McDonald-Madden et al. 2010). Resources for conservation are generally scarce (Bottrill et al. 2008), especially in developing countries (Danielsen et al. 2003). Planning for conservation success thus requires identifying effective and efficient monitoring strategies (Reynolds, Thompson & Russell 2011).
Monitoring is affected by multiple uncertainties (Harwood & Stokes 2003). Process uncertainty due to variation in the system itself (e.g. wildlife spatial distribution) interacts with observation uncertainty, which is a consequence of sampling effort and survey design as well as the process of observation. Observation uncertainty has multiple drivers and consequences. For example, estimates obtained from aerial surveys may be affected by a number of factors, such as animal detectability, observer performance, variation in aircraft height and deviations from the transect (Norton-Griffiths 1978; Jachmann 2002). Having imperfect knowledge of the true status of natural resources plays a central role in management decisions. For instance, Sethi et al. (2005) incorporated multiple types of uncertainty into a bioeconomic model of fisheries and found that observation uncertainty has the largest impact on policy, profits and extinction risk. The direction and magnitude of the effects of these processes on final abundance estimates have to be considered to establish error minimization priorities and maximize monitoring efficiency.
Optimization of sampling effort to achieve monitoring goals is demonstrably an essential consideration (Field, Tyre & Possingham 2005; Sims et al. 2006), but considerably less attention has been given to the effects and, particularly, the drivers of observation error. The effects of undercounting or the misidentification of the sex or age of an individual have received limited attention (Elphick 2008), most likely because multiple processes may occur simultaneously and discerning their impacts from monitoring data may be difficult. Knowing which types of errors are most important and should be tackled first is particularly challenging. Experimentation is often difficult, due to terrain, lack of capacity and the financial and time costs involved. For convenience and model simplicity, observation uncertainty is often considered as an overarching composite process when using simulations, modelled through log-normally distributed errors (e.g. Hilborn & Mangel 1997; Shea & Mangel 2001).
Modelling is a particularly useful tool because it allows experimentation through simulation. Previous studies have used modelling, for example, to investigate how to improve survey effort and design but without taking specific errors in the observation process into consideration (Sims et al. 2006; Blanchard, Maxwell & Jennings 2008), correct observation bias based on herd size detectability (McConville et al. 2009), assess the effects of data quality on harvest strategies and income (Milner-Gulland, Coulson & Clutton-Brock 2004), and estimate the risk of failing to detect a trend and wasting resources (Katzner, Milner-Gulland & Bragin 2007). Using a modelling approach, it is possible to explicitly simulate ‘true’ scenarios of wildlife abundance and distribution. Each step of the observation procedure can then be replicated to investigate how the quality of the data collected (‘observed state’) may be improved and particularly how researchers' actions and assumptions affect precision (uncertainty or variability in the estimates which is used to produce confidence intervals around them) and accuracy (difference between the set of estimates and the truth they represent).
The Serengeti ecosystem is one of the most intensively studied systems in Africa. Long-term research in the Serengeti includes monitoring of a range of species, with wildlife censuses having been conducted since the 1950s (Sinclair et al. 2007). Monitoring resources are, however, very limited, especially given that this ecosystem covers more than 25 000 km2. Monitoring must therefore be adjusted according to available budgets, while still being able to provide accurate and precise abundance estimates. Using monitoring of two contrasting ungulate species in the Serengeti ecosystem as a case study, we employed simulation modelling to investigate how abundance estimates are affected by multiple types of uncertainty, with a focus on observation error. Specifically, we investigated which factors should be prioritized to increase survey accuracy and precision and explored the potential effects of different budgetary scenarios on the robustness of the population estimates obtained for species of different ecological characteristics. This enables us to provide insights into the likely effect of different types of observation and process error on population estimates for savanna ungulates and more generally to present a framework for evaluating monitoring programmes in a virtual environment.
- Top of page
- Materials and methods
- Supporting Information
In this study, we have considered the multiple sources and effects of uncertainty in monitoring data obtained through wildlife surveys, focusing on the interactions between observation error and the spatial distribution of wildlife populations. Our results suggest that, under the simulated conditions, the relative importance of each process affecting precision and accuracy varies according to the survey technique and biological characteristics of the species. While survey precision was mainly affected by population characteristics and sampling effort, the accuracy of the survey was greatly affected by observer effects, such as juvenile and herd detectability. The adequacy of SRFs, that is, whether these surveys led to a minimum number of sightings, was mainly affected by population size, mean herd size, herd size estimability, maximum individual detectability and sampling effort. Our results also illustrate how budget size affects survey precision and accuracy, particularly for SRFs.
We extend previous work on causes of survey bias and imprecision (e.g. Norton-Griffiths 1978; Norton-Griffiths & McConville 2007) by developing a ‘virtual ecologist’ framework (Zurell et al. 2010) within which to carry out simulated tests of different monitoring strategies for different types of species. Elphick (2008) highlights the need for improved understanding of the effects of multiple sources of uncertainty on survey bias and precision, particularly errors due to observation uncertainty and its interaction with biological characteristics. However, compared to other aspects of monitoring such as sampling design, observation uncertainty is still the ‘Cinderella’ of monitoring, with little attention to the multiple potential sources of error involved. By decomposing observation uncertainty into components, which may vary in magnitude and direction, we can make practical recommendations to managers concerning the priority issues that require attention. This would allow them to improve precision or accuracy of their counts, depending on the biology of the species concerned and budgetary constraints (Table 4).
Table 4. Summary of the main issues considered in this study and our main recommendations for different types of species according to their spatial distribution, listed in priority order
| ||Type of species according to spatial distribution|
|Highly aggregated (e.g. wildebeest)||Random or slightly aggregated (e.g. impala)|
|Aerial survey technique analysed||Aerial Point Sampling||Systematic Reconnaissance Flights|
|Main issues considered||Sampling effort||Sampling effort|
|Flight characteristics (variation in altitude and speed)||Flight characteristics (variation in altitude)|
|Spatial distribution (aggregation and spatial autocorrelation)||Spatial distribution (herd size and home range)|
|Population size and structure (proportion of juveniles)||Population size|
|Observer effects (juvenile detectability and counting error of adult animals)||Observer effects (counting error, herd detectability according to size, individual detectability within herd and distance effects)|
|Prioritized recommendations||Focus on survey precision||Focus on survey bias|
|Obtain preliminary estimates of aggregation and spatial autocorrelation, and define sampling effort accordingly||Maximize, and obtain estimates of, herd size estimability|
|Minimize, and obtain estimates of, counting errors of juvenile animals or obtain juvenile estimates from ground transects||Maximize, and obtain estimates of, herd detectability|
| ||Apply bias correction factor according to mean herd size|
The spatial distribution of a species is a major driver of variation in survey precision and accuracy (Table 4). Our findings chime with those of, for example, Blanchard, Maxwell & Jennings (2008) and Borkowski, Palmer & Borowski (2011), who also show the importance of aggregation (due to biological/social characteristics) and spatial autocorrelation (due to environmental/spatial characteristics) in determining survey precision. Counterbalanced variation due to changes in sampling effort, aggregation and spatial autocorrelation (for more aggregated species), and population size and mean herd size (for less aggregated species) suggests that sampling effort should be defined according to the spatial distribution to account for differences in precision. For monitoring highly aggregated species, such as wildebeest, we recommend that particular attention should be given to survey precision and that sampling effort should be defined according to previous estimates of aggregation in the monitored population. For example, in the Serengeti, sampling effort varies between years according to rough visual estimations of aggregation. This assessment could be formally considered in the monitoring protocol. The survey precision is most sensitive to spatial autocorrelation, which should be explicitly considered in abundance estimation procedures (e.g. confidence levels adjusted for ‘effective sample size’ lower than actual sample size).
Similarly to other studies comparing estimates obtained through aerial surveys to known or presumed accurate population sizes (Goddard 1967; Jachmann 2002), our simulated surveys produced underestimates of considerable magnitude. Survey accuracy was greatly affected by multiple observer effects, particularly juvenile detectability when counting from photographs, and herd size estimability and detectability when conducting direct counts during transects. Although the effects of distance and counting variability have been often mentioned as sources of inaccuracy (Buckland 2001), our results show that these commonly discussed types of observer error were comparatively less important in driving survey accuracy for these species in the range of conditions that occur in the Serengeti. This demonstrates the need for error minimization priority-setting based on comparative analyses. For example, McConville et al. (2009) explore the effect of herd detectability on accuracy, but we show that aerial survey accuracy is very much affected by detectability of individual animals within a herd. For random or slightly aggregated species monitored through aerial surveys, such as impala, we recommend that minimising potential bias should be a major consideration. As accuracy is most sensitive to observer effects, monitors should be provided with appropriate training and their reliability evaluated before the actual survey to calibrate the final abundance estimates. For example, observers’ estimates could be compared with photographs of herds, obtaining correction factors. Other studies have shown that ground counts can provide more accurate estimates than aerial surveys, which are greatly affected by wildlife visibility for this type of species, but are generally more time-consuming and expensive, particularly for large survey areas (Jachmann 2002). When feasible, ground counts, or other better-performing techniques, should be conducted instead of or in addition to aerial surveys.
We also highlight the importance of considering which demographic group is subject to biases. In the case of wildebeest, juvenile detectability was a key driver of survey accuracy, while the effect of miscounting adults was negligible. Variation in juvenile survival can be used to make inferences about population trends (Gaillard, Festa-Bianchet & Yoccoz 1998), which further illustrates the importance of correctly counting juveniles. For highly aggregated populations, juvenile abundance could be obtained from other sources, such as ground transects, to avoid reducing accuracy of total population estimates. In other species, there may be different population components for which accurate and precise abundance estimates are crucial to management. For example, Katzner, Milner-Gulland & Bragin (2007) demonstrated the importance of collecting data on adult survival of Imperial eagles Aquila heliaca instead of territory occupancy to detect population trends.
This study took a static, spatially explicit approach to analysing monitoring uncertainties in the range of conditions that occur in the Serengeti, but there are also issues related to changes over time. For example, observer performance may improve or herd aggregation coefficients may change (cf McConville et al. 2009). Chee & Wintle (2010) have developed a dynamic cull control rule for overabundant wildlife where iterative culling can be used to update population parameters through Bayesian methods. Similarly, a dynamic monitoring strategy could update according to knowledge gained from the observation process.
Monitoring efficiency is of the utmost importance for conservation especially in the context of limited budgets and other priorities (Danielsen et al. 2003; Bottrill et al. 2008). Relating data quality to budgetary constraints for different survey techniques and prioritizing approaches to error minimization are thus essential to investigate trade-offs and make informed decisions under uncertainty (Caughlan & Oakley 2001; Gaidet-Drapier et al. 2006) but these are rarely considered.
Monitoring and management decisions should be incorporated into conceptual and methodological frameworks, which explicitly consider uncertainty, such as the management strategy evaluation (MSE; Bunnefeld, Hoshino & Milner-Gulland 2011) and adaptive management (AM; Keith et al. 2011). MSE uses monitoring data to estimate trends and population size and then simulates decisions taking the degree of observation uncertainty into account, while AM implements strategies that incorporate uncertainty by testing multiple plausible hypotheses. Using a ‘virtual ecologist’ approach (Zurell et al. 2010), we provided insights into how to improve monitoring data and implement informed management actions that take monitoring uncertainty into consideration. This approach could easily be integrated into an MSE or AM framework. Explicit analyses of multiple types and sources of uncertainty are required, ensuring that conservation trade-offs are evaluated in a comprehensive, robust and transparent manner (Chee & Wintle 2010).