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Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti



  1. Planning for conservation success requires identifying effective and efficient monitoring strategies but multiple types of uncertainty affect the accuracy and precision of wildlife abundance estimates. Observation uncertainty, a consequence of sampling effort and design as well as the process of observation, is still understudied, with little attention given to the multiple potential sources of error involved. To establish error minimization priorities and maximize monitoring efficiency, the direction and magnitude of multiple sources of uncertainty must be considered.
  2. Using monitoring of two contrasting ungulate species in the Serengeti ecosystem as a case study, we developed a ‘virtual ecologist’ framework within which we carried out simulated tests of different monitoring strategies for different types of species. We investigated which components of monitoring should be prioritized to increase survey accuracy and precision and explored the robustness of population estimates under different budgetary scenarios.
  3. The relative importance of each process affecting precision and accuracy varied 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.
  4. Synthesis and applications. Monitoring efficiency is of the utmost importance for conservation, especially in the context of limited budgets and other priorities. We provide insights into the likely effect of different types of observation and process error on population estimates for savanna ungulates, and more generally present a framework for evaluating monitoring programmes in a virtual environment. In highly aggregated species, the main focus should be on survey precision; sampling effort should be defined according to wildlife spatial distribution. For random or slightly aggregated species, accuracy is the key factor; this is most sensitive to observer effects which should be minimized by training and calibration by observer.


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.

Materials and methods

Study area and species

We chose two species to investigate the contrasting issues involved in monitoring ungulate species in savanna ecosystems. The migratory wildebeest Connochaetes taurinus population is monitored throughout the Serengeti ecosystem using aerial surveys to take photographs within sampling blocks (Fig. S1, Supporting Information). By contrast, a resident population of impala Aepyceros melampus is monitored using systematic flights along transects in a Game Reserve adjoining the Serengeti National Park (Grumeti–Ikorongo GR). Surveys are conducted approximately every 3–5 years in February/March to assess populations of resident and migratory ungulates (Campbell & Borner 1995; TAWIRI 2010).

The wildebeest population is highly gregarious and composed of bachelor herds and large nursery herds, with territorial males at certain times of year (Estes 1992). Wildebeest use the Serengeti plains in large herds during the wet season (mid-October through April), when the monitoring is conducted, moving west and north at the beginning of the dry season (May to mid-October). They give birth synchronously in February (Thirgood et al. 2004). Impala occur in the Serengeti woodlands and their populations are composed of large groups of females with a single dominant male (Jarman & Jarman 1973). These sedentary ungulates move up to 3 km in the dry season and 0·95 km in the wet season (Estes 1992). Currently, there are around 1·3 million wildebeest in the Serengeti and 10 000 impala in the Game Reserve (Grumeti Fund 2010; Hilborn & Sinclair 2010).

Methodological framework

We simulated the monitoring process for the two ungulate populations to investigate monitoring precision and accuracy. The methodological framework was divided into four main components (Fig. 1): (a) a spatial distribution model, which provided the ‘true scenario’ against which simulated monitoring data were compared; (b) an ‘observation model’, which simulated monitoring of these populations; (c) a data analysis component, which estimated wildlife abundance from simulated monitoring data, the ‘assessment model’; and (d) an assessment of survey accuracy and precision, in which discrepancies between ‘true’ and ‘observed’ population sizes and their drivers were investigated.

Figure 1.

Conceptual description of the study's methodological approach.

Modelling the distribution of wildlife


A virtual wildebeest population was distributed in a 90 × 35 km grid with a total area of 3150 km2 (2887 km2 was surveyed in 2009; Hilborn & Sinclair 2010). Grid units were sized to be equivalent to a ‘potential photograph’ capturing around 0·05 km2. Numbers of wildebeest per grid unit were simulated using a negative binomial distribution (NBD) with two defining parameters: the mean (μ) and the aggregation (k), with lower values of k representing more aggregated populations. Pieters et al. (1977) compare the efficiencies of several methods of estimation of the parameter k; the method of moments estimate relates it to the empirical mean μ and variance σ2 by:

display math(eqn 1)

A NBD allows us to account for, and investigate the effects of, differing degrees of animal aggregation on survey counts (Matthiopoulos 2011). To check its suitability to describe wildebeest counts, the goodness-of-fit was assessed through comparison of fitted and actual counts from the 2006 census (χ2 = 338, d.f. = 887, > 0·99; J. G. C. Hopcraft, unpublished data).

The number of wildebeest in each cell is likely to be affected by abundance in neighbouring cells, so we adopted a geostatistical approach to incorporate spatial autocorrelation using the r package geoR (Ribeiro & Diggle 2001, version1·7-4). First, we defined the spatial autocorrelation structure by using a Gaussian process with variance–covariance matrix C related to an exponential correlation function between unit locations:

display math(eqn 2)

where dij is the distance between grid units i and j, math formula is the threshold variance known as the sill (which we kept at a constant value) and phi is the range parameter that represents a fraction of the distance beyond which there is little or no autocorrelation (Diggle, Tawn & Moyeed 1998). The strength of spatial autocorrelation was controlled by varying the range parameter; the larger the range, the stronger the autocorrelation because it persists over longer distances. To generate spatially autocorrelated survey counts, we then conditioned the outcome of the NBD on these spatially correlated random fields by affecting the actual realization of the distribution for each cell via the exponential link and the mean and aggregation parameters.

At the time of the counts, juvenile wildebeest are found within large nursery herds with their mothers, while older males remain in separate aggregations (Estes 1992). In the 2006 census in the Serengeti, juvenile wildebeest were more likely to be present in photographs with higher total numbers of animals (Fig. 2; GLM with a binomial error structure: z = 6·560, d.f. = 340, P < 0·001). Empirical juvenile counts did not differ significantly from a NBD (χ2 = 84, d.f. = 340, > 0·99), and 46% of the photographs with wildebeest present had juveniles and adults, while the remaining only had adults (J.G.C. Hopcraft, unpublished data).

Figure 2.

Estimated probability of juvenile presence according to total number of animals per photograph. Original data on the presence of juveniles are superimposed as grey circles, with diameter proportional to the total number of animals. The trend line represents effect taken from model outputs (GLM with binomial errors, N = 343 photographs) and the dashed lines indicate 95% confidence intervals.

The distribution of juveniles was modelled for a range of juvenile proportions in the total population. Juvenile counts followed a NBD drawn separately from the previous one which simulated the total count of wildebeest per cell, incorporating both adults and juveniles. We assumed that juveniles occurred in half of the photographs where wildebeest were found, with the probability of presence associated with higher total numbers of animals. Juveniles were thus redistributed according to total wildebeest counts per cell. To simulate the number of juveniles per cell, the desired proportion of cells without juveniles [i.e. zeros in the NBD; p′(0)] and mean number of juveniles per cell μjuv were used to estimate the aggregation kjuv of juveniles (Perry & Taylor 1986):

display math(eqn 3)


A range of ‘true’ population sizes of impala was distributed in a virtual landscape with shape and area similar to the real survey area (around 1500 km2; Fig. S1, Supporting Information).

Impala form herds of two to hundreds of animals and are generally dispersed in a random or slightly aggregated pattern (Jarman & Jarman 1973; Stein & Georgiadis 2008). We were interested in modelling individual spatial locations while taking into account the abundance patterns related to herd distribution and size. Impala distribution was thus modelled using a 3-step clustering process: (1) a number of clusters (‘herds’) was situated randomly in the landscape (assuming a homogeneous landscape) according to a Poisson process, defining a ‘parent point’ per herd; (2) herd size followed a log-normal distribution; and (3) animals within each herd were independently and uniformly scattered inside a circular herd home range centered on the parent point. Herd home range was obtained by multiplying herd size by the assumed ‘individual space’ requirements, up to a specified maximum value (‘maximum herd home range’). The log-normal distribution was parameterized with the mean and coefficient of variation (CV) of the observed scale, where the standard deviation of the log-normal variable (SDln) is given by:

display math(eqn 4)

and the mean of the log-normal variable (meanln) is given by:

display math(eqn 5)

The log-normal distribution is commonly applied to describe multiplicative processes when mean values are low, variances large, and values cannot be negative, such as wildlife abundance (Matthiopoulos 2011). Impala monitoring does not provide counts of juveniles so population sizes were assumed to refer to adults only.

Wildlife monitoring

The observation procedure was modelled according to descriptions of monitoring in the Serengeti by Sinclair (1973), Norton-Griffiths (1973, 1978), Sinclair & Norton-Griffiths (1982), and Hilborn & Sinclair (2010). Therefore, we simulated monitoring of wildebeest in the Serengeti plains during the wet season when animals are aggregated in large numbers in a relatively small area. This timing increases the likelihood of good visibility and leads to a clear separation between migratory and resident wildebeest (only migratory animals are surveyed in this monitoring design). Migratory wildebeest are monitored through aerial point sampling (APS; Norton-Griffiths 1978, 1988), which was first developed to characterize the land use of a region by interpreting a sample of aerial photographs. We simulated monitoring of impala using Systematic Reconnaissance Flight (SRF) surveys, in which abundance data are collected within each subunit along several flight lines (i.e. transects; Norton-Griffiths 1978).


The sampling steps simulated the process of taking aerial photographs of wildebeest or conducting direct impala counts through selection of grid cells. We simulated a wide range of levels of sampling effort (measured by distances between transects and spacing between photographs) to investigate the effects of sampling error on survey accuracy and precision. Detailed information about each specific step in this model component is provided in the online supporting information (Appendix S1, Supporting information).

Observational procedures: flight and observer effects

These steps simulated flight and counting characteristics, namely variation in flight altitude and speed, and spatially autocorrelated miscounting of animals from photographs or direct counts because of bad weather conditions, habitat or fatigue. For impala, observer effects also included herd and individual detectability and distance. This component allowed us to investigate the effects of observation error on survey accuracy and precision. Detailed information is provided in the online supporting information (Appendix S2, Supporting information).

A summary of the modelled variables and range of values explored for each species is presented in Table 1.

Table 1. Description of variables and range of values explored for monitoring of (A) wildebeest and (B) impala. The subscripts ‘wild’ and ‘imp’ refer to parameters regarding wildebeest and impala, respectively
  1. CV, coefficient of variation.

(A) Wildebeest
Population characteristics
Population size N wild 200 000–2 000 000Hilborn & Sinclair (2010)
Proportion of juveniles (%) α 5–35Estimated
Aggregation k 0·01–2Assumed
Spatial autocorrelation rangephi0·1–0·5Assumed
Spatial threshold variance (sill) math formula Fixed (1)Assumed
Sampling characteristics
Distance between transects (km) γ wild 0·5–24Hilborn & Sinclair (2010)
Time between photographs (s) δ 1–120Hilborn & Sinclair (2010)
Flight characteristics
Mean flight altitude (feet) ε wild Fixed (1200)Hilborn & Sinclair 2010;
CV error altitude ζ wild 0–0·2Estimated
Mean flight speed (km s−1) θ wild Fixed (0·06)Hilborn & Sinclair 2010;
CV error speed ι wild 0–0·3Assumed
Observer effects
Minimum error counting juveniles (%) Λ 0–0·2Sinclair (1973)
Juvenile detectability (number of animals in a photograph for which 50% juveniles are likely to be missed) ϊ 20–50Assumed
CV error counting adults ϋ 0–0·5Assumed
Counting error autocorrelation rangephiwild0–1Assumed
(B) Impala
Population characteristics
Population size N imp 1000–15 000Grumeti Fund (2010)
Median herd size ξ 5–50Jarman & Jarman (1973)
CV herd size ο 0–0·5Stein & Georgiadis (2008)
Maximum herd home range (km2) π 0·5–3Jarman & Sinclair (1979)
Individual space (km2) ς 0·05–0·2Jarman & Sinclair (1979)
Sampling characteristics
Distance between transects (km) γ imp 0·5–7TAWIRI (2010)
Flight characteristics
Mean flight altitude (feet) ε imp Fixed (300)TAWIRI (2010)
CV error altitude ζ imp 0–0·2Assumed
Mean flight speed (km s−1) θ imp Fixed (0·06)TAWIRI (2010)
Observer effects
Minimum herd detectability (%) σ 0·05–0·5Assumed
Herd size nondetectability (herd size for which  there is a 50% chance of missing it) τ 10–50Assumed
Individual detectability at distance 0 (%) υ 0·7–0·99Assumed
Detectability by distance (distance for which there is a 50% chance of seeing animals; km) φ 0·125–0·250Assumed
Maximum individual detectability (%) χ 0·7–0·99Assumed
Herd size estimability (number of animals in a herd for which 50% are likely to be missed) ψ 10–50Assumed
CV counting error ω 0–0·5Assumed
CV counting error autocorrelation rangephiimp0–1Assumed

Abundance estimation

The simulated survey data were used to estimate wildlife abundance, following procedures currently adopted in the study area.


Simulated aerial photographs were treated as simple random samples from which juvenile and adult wildebeest were counted (Hilborn & Sinclair 2010). The estimated density per photograph is the number of animals in a certain photograph divided by the photograph area. The final estimate of the wildebeest population size is the area included in the survey (90 × 35 km2) multiplied by the average density:

display math(eqn 6)


Data from a simulated SRF were converted to estimates of animal density by dividing the total number of animals seen by both observers by the length of the subunit multiplied by their summed strip widths (Norton-Griffiths 1978). Subunits were then combined within each transect and population estimates were calculated using transects as units of random sampling (Campbell & Borner 1995).

Analysis of sources of observation uncertainty

Analysis involved varying all model parameters simultaneously within the range considered (Table 1) and testing their effects on survey accuracy and precision. Survey precision was measured by the CV and the normalized variance (CV is squared to the coefficient of variance, CV2, which represents the total observed variance) but only CV results are presented (CV2 results in Supporting information). The CV was based on the simulated survey data, rather than statistically derived from each survey estimate, and expressed as:

display math(eqn 7)

where SD is the sample standard deviation of the population estimates from 50 simulations and math formula the mean estimate of population size. Accuracy was defined as the percentage discrepancy between the mean estimated population size and the known population sizes for juveniles only or all age classes together (for impala, only adult counts were conducted).

One thousand sets of parameter values were generated independently from uniform distributions for each species, and 50 simulations were carried out for each parameter set, from which mean values were obtained. All explanatory and dependent variables were scaled to have a standard deviation of unity, resulting in unit-less measures that can be used to infer the relative importance of parameters. Generalized linear models with Gamma (log-link) and Gaussian error distributions were fitted to the simulation results to evaluate the sensitivity of survey precision and accuracy to parameters, respectively. A generalized linear model with quasi-binomial error distribution (to account for overdispersion) and a logit link was fitted for juvenile wildebeest. Relevant two-way interactions were also considered. The linearity of the relationship between the parameters and the dependent variables and model residuals was examined graphically.

We also explored under which conditions (population characteristics and observation error) SRFs were adequate for impala monitoring. We assumed that at least one herd or five animals would have to be seen in order for the method to be considered adequate and used a generalized linear model with binomial error distribution to evaluate the effects of potential drivers on survey adequacy, treated as a binary variable.

Finally, to illustrate the potential effects of different budget allocations on survey precision and accuracy, we ran 50 replicates for 1000 parameter sets under high- and low-budget scenarios. High- or low-budget scenarios assume parameters at their best or worst values, respectively (values presented in Table S1, Supporting information). For example, the low-budget scenario assumes that only a few transects are conducted and that there is high counting variability (perhaps due to inexperienced or untrained observers). We obtained current unitary costs from itemized monitoring expenses in the study area (J.G.C. Hopcraft, unpublished data) and then multiplied them by the simulated parameter values to estimate approximate budget costs for both scenarios.


Effects of survey characteristics on precision and accuracy


As wildebeest became more aggregated (i.e. lower k values) and more spatially autocorrelated (i.e. higher similarity between nearby cells), the surveys became less precise (higher CV). Higher sampling effort (i.e. smaller distance between transects or spacing between photographs) increased precision in the wildebeest surveys but this effect was significantly weaker when spatial autocorrelation increased (Table 2).

Table 2. Results of a sensitivity analysis in which generalized linear models were fitted to precision (coefficient of variation) and inaccuracy (percentage discrepancy between the mean estimated population size and the known population size) for wildebeest monitoring
ParameterRelative importance (standardized regression coefficients; β)
Coefficient of variation (CV)InaccuracyInaccuracy (juveniles only)
  1. All dependent and explanatory variables were scaled to have a standard deviation of unity for comparative purposes. The table shows the coefficients of all parameters and interactions from the full model. All β > 0·10 are given in bold. Significance is coded as ***P < 0·001, **P < 0·01, *P < 0·05.

Population size−0·03 0·40 *** 0·70 ***
Proportion of juveniles−0·02 0·71 *** 0·05***
Aggregation (k)0·17***−0·08***0·12***
Spatial autocorrelation 0·35 *** −0·04*0·01
Distance between transects (km) 0·13 *** −0·01−0·09**
Time between photographs (s) 0·14 *** −0·01−0·08**
CV error altitude0·020·04−0·02
CV error speed−0·010·01−0·01
Minimum error counting juveniles (%)0·020·05***−0·01
Juvenile detectability−0·020·16***0·29***
CV of error counting adults0·03**0·010·02
Spatially autocorrelated errors0·01−0·03−0·01
Spatially autocorrelated error*CV of error counting adults−0·020·010·01
Spatially autocorrelated error*Juvenile detectability−0·030·02−0·01
Spatially autocorrelated error*minimum error counting juveniles0·01−0·010·01
Spatial autocorrelation*distance between photographs−0·07**0·02−0·01
Spatial autocorrelation*distance between transects−0·09**0·01−0·01
Aggregation*Spatial autocorrelation0·05−0·02−0·01

The comparison between the population estimates from the surveys and known population sizes suggested that accuracy was lower for higher population sizes, when juveniles constituted a higher proportion of the total population, and for lower levels of juvenile detectability when counting from photographs (Table 2). Accuracy of juvenile estimates was mostly affected by population size, juvenile detectability and aggregation.


For impala monitoring through SRFs, the surveys became less precise as distance between transects increased (i.e. lower sampling effort), for lower population sizes, higher mean herd sizes and lower herd size estimability. Accuracy in SRFs decreased when detectability at minimum distance and herd size estimability decreased and mean herd size and herd size nondetectability increased (Table 3).

Table 3. Results of a sensitivity analysis in which generalized linear models were fitted to precision (coefficient of variation), inaccuracy (percentage discrepancy between the mean estimated population size and the known population size) and survey adequacy (able to detect at least one herd or five animals) for impala monitoring
ParameterRelative importance (standardized regression coefficients; β)
Coefficient of variation (CV)InaccuracyAdequacy
  1. All dependent and explanatory variables were scaled to have a standard deviation of unity for comparative purposes. The table shows the coefficients of all parameters and interactions from the full model. All β > 0·10 are given in bold. Significance is coded as ***P < 0·001, **P < 0·01, *P < 0·05.

Population size0·32***0·05* 0·36 ***
Mean herd size 0·15 *** 0·25 *** 0·31***
CV herd size0·020·10***0·01
Maximum herd home range (km2)−0·040·030·04
Individual space (km2)−0·03−0·01−0·01
Distance between transects (km) 0·45 *** 0·030·53***
CV error altitude−0·03−0·02−0·04
Minimum herd detectability (%)−0·05−0·07***0·04
Herd size nondetectability0·08** 0·39 *** −0·03
Detectability at distance 0 (%)−0·020·20***0·07
Detectability by distance−0·03−0·04*0·02
Maximum individual detectability (%)−0·01−0·040·18**
Herd size estimability0·11***0·62*** 0·54 ***
CV counting error0·02−0·010·01
Spatially autocorrelated errors0·010·01−0·01
Spatially autocorrelated errors*CV counting error0·010·01−0·01

The likelihood of detecting at least one herd or five animals using SRFs decreased for lower population sizes, lower sampling effort (measured as distance between transects), higher mean herd size, lower maximum individual detectability and lower herd size estimability (Table 3).

Budgetary scenarios

High budgets produced more precise estimates for wildebeest and impala (Fig. 3b, d) although both monitoring techniques were likely to underestimate wildlife abundance (Fig. 3a, c). Mean underestimation for wildebeest monitoring from APS was around 15% and, although low- and high-budget scenarios produced similar values of mean underestimation, survey accuracy was much more variable for low budgets, producing estimates from 60% below the known population size up to 30% above (Fig. 3a).

Figure 3.

The potential effects of different budget allocations (low- or high-budget scenarios) on (a) survey accuracy for wildebeest monitoring; (b) survey precision for wildebeest monitoring; (c) survey accuracy for impala monitoring; and (d) survey precision for impala monitoring. High- or low-budget scenarios assume parameters at their best or worst values, respectively (see Table S1, Supporting information). For example, the low-budget scenario assumes conducting only a few transects and high counting variability.

For impala monitoring, the SRFs produced estimates generally 80% below the true value. Higher budget sizes produced a mean underestimation of around 65% and lower budget sizes produced a mean underestimation of around 90%, ranging from 80% to 100%. Higher budgets produced more variable bias, unlike for the wildebeest; this is due to variability being constrained by reaching zero herds detected in the low-budget scenario.

For wildebeest monitoring, the simulated high- and low-budget scenarios were estimated to cost c. US$6410 and US$3780, respectively. For impala, the high budget would require around US$8250 and the lower c. US$4250. The higher-budget scenario considered for wildebeest monitoring, which provides twice more precise estimates, would thus cost c. 70% more to implement. The higher budget for impala monitoring, which increases accuracy by 30%, would increase costs roughly by 95%.


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 analysedAerial Point SamplingSystematic Reconnaissance Flights
Main issues consideredSampling effortSampling 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 recommendationsFocus on survey precisionFocus on survey bias
Obtain preliminary estimates of aggregation and spatial autocorrelation, and define sampling effort accordinglyMaximize, and obtain estimates of, herd size estimability
Minimize, and obtain estimates of, counting errors of juvenile animals or obtain juvenile estimates from ground transectsMaximize, 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).


We thank Tony Sinclair for sharing his detailed understanding and expert knowledge about the Serengeti, Grant Hopcraft for sharing unpublished data and providing feedback on an earlier version of this manuscript and Joe Bull for language revision. AN is supported by the Portuguese Foundation for Science and Technology (FCT; doctoral grant SFRH/BD/43186/2008). NB and EJMG were supported by the European Commission under the HUNT project of the 7th Framework Programme for Research and Technological Development and EJMG by a Royal Society Wolfson Research Merit award. Phil Stephens, Mike Lonergan and an anonymous reviewer gave valuable comments on the manuscript.