Estimates of the abundance of Breeding Stock D humpback whales (Megaptera novaeangliae) are key to the conservation and management of what is thought to be one of the largest populations of the species. Five years (2000, 2001, 2006, 2007 and 2008) of aerial surveys carried out over an eight-year period at North West Cape (Western Australia) using line transect methodology allowed trends in whale numbers to be investigated, and provided a base for comparison with estimates made approximately 400km south at Shark Bay (Western Australia). A total of 3,127 whale detections were made during 74 surveys of the 7,043km 2 study area west of NWC. Pod abundance for each flight was computed using a HorvitzThompson like estimator and converted to an absolute measure of abundance after corrections were made for estimated mean cluster size, unsurveyed time, swimming speed and animal availability. Resulting estimates from the migration model of best fit with the most credible assumptions were 7,276 (CI = 4,993–10,167) for 2000, 12,280 (CI = 6,830–49,434) for 2001, 18,692 (CI = 12,980–24,477) for 2006, 20,044 (CI = 13,815–31,646) for 2007, and 26,100 (CI = 20,152–33,272) for 2008. Based on these data, the trend model with the greatest r 2 was exponential with an annual increase rate of 13% (CI = 5.6%–18.1%). While this value is above the species’ estimated maximum plausible growth rate of 11.8%, it is reasonably close to previous reports of between 10–12%. The coefficient of variation, however, was too large for a reliable trend estimate. Perception bias was also not accounted for in these calculations. Based on a crude appraisal which yielded an estimated p(0) of 0.783 (from independent observer effort, CV = 0.973), the 2008 humpback population size may be as large as 33,300. In conclusion, the work here provides evidence of an increasing Breeding Stock D population, but further surveys are necessary to confirm whether the population is indeed increasing at its maximum rate.

The pooling robustness property of distance sampling results in unbiased abundance estimation even when sources of variation in detection probability are not modeled. However, this property cannot be relied upon to produce unbiased subpopulation abundance estimates when using a single pooled detection function that ignores subpopulations. We investigate by simulation the effect of differences in subpopulation detectability upon bias in subpopulation abundance estimates. We contrast subpopulation abundance estimates using a pooled detection function with estimates derived using a detection function model employing a subpopulation covariate. Using point transect survey data from a multispecies songbird study, species-specific abundance estimates are compared using pooled detection functions with and without a small number of adjustment terms, and a detection function with species as a covariate. With simulation, we demonstrate the bias of subpopulation abundance estimates when a pooled detection function is employed. The magnitude of the bias is positively related to the magnitude of disparity between the subpopulation detection functions. However, the abundance estimate for the entire population remains unbiased except when there is extreme heterogeneity in detection functions. Inclusion of a detection function model with a subpopulation covariate essentially removes the bias of the subpopulation abundance estimates. The analysis of the songbird point count surveys shows some bias in species-specific abundance estimates when a pooled detection function is used. Pooling robustness is a unique property of distance sampling, producing unbiased abundance estimates at the level of the study area even in the presence of large differences in detectability between subpopulations. In situations where subpopulation abundance estimates are required for data-poor subpopulations and where the subpopulations can be identified, we recommend the use of subpopulation as a covariate to reduce bias induced in subpopulation abundance estimates.

Estimating the abundance and spatial distribution of animal and plant populations is essential for conservation and management. We introduce the R package Distance that implements distance sampling methods to estimate abundance. We describe how users can obtain estimates of abundance (and density) using the package as well as documenting the links it provides with other more specialized R packages. We also demonstrate how Distance provides a migration pathway from previous software, thereby allowing us to deliver cutting-edge methods to the users more quickly.

AbstractDistance sampling is a widely used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods areline‐transect samplingandpoint‐transect sampling. In both cases, observer(s) perform a standardized survey along a series of randomly located lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all objects will be detected, but a fundamental assumption of the basic methods is that all objects that are actually on the line or point are detected. The key to distance sampling analyses is to use the observed distances to fit adetection functionthat describes how detectability decreases with increasing distance from the transect. The fitted function is used to estimate the average probability of detecting an object; from here, one can readily obtain point and interval estimates for the density and abundance of objects in the survey area. Various extensions to the basic methods allow assumptions to be relaxed, and include methods that integrate capture‐recapture and distance sampling, as well as methods to model spatial variation in density.

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/eap.1553 This article is protected by copyright. All rights reserved. DR. TIAGO ANDRÉ MARQUES (Orcid ID : 0000-0002-2581-1972)

In a recent paper, Hutto (2016a) challenges the need to account for detectability when interpreting data from point counts. A number of issues with model-based approaches to deal with detectability are presented, and an alternative suggested: surveying an area around each point over which detectability is assumed certain. The article contains a number of false claims and errors of logic, and we address these here. We provide suggestions about appropriate uses of distance sampling and occupancy modeling, arising from an intersection of design- and model-based inference. We begin with three points about which we agree with Hutto (2016a): (1) "The most important thing a researcher can do is use some common sense…"; (2) "There will always be a trade-off between breadth and depth in sampling…"; and (3) "Fixed-radius surveys should not be universally condemned and should certainly not be the basis for rejecting a study outright." If these principles are followed for any given study, we argue that common sense would require that detectability be considered within the context of the study objectives. It may be possible to assume constant detectability over time, allowing estimation of trend without estimating detectability, or to assume all individuals on a plot are detected, in which case probability of detection is unity by assumption, but such assumptions must be reviewed and validated. A precautionary approach, and common sense, imply that the onus is on the researchers to show that the effects of not accounting for detectability are negligible for their goals. To do so, detectability cannot be ignored, and one must therefore collect relevant data about the detection process. This is certainly the case for Hutto's suggested method: the data are used to choose a distance up to which detection is certain. In the absence of a formal model, this choice is subjective. It risks being too small, discarding most of the data and compromising precision, or too large, resulting in downward bias in density estimates and unknown bias in trends and/or spatial variation in density (depending on whether detectability changes over time and/or space). Further, this distance will depend on many factors including species, habitat, and observer. In short, the suggestion implies an unspecified ad hoc "model" for incomplete detection as a function of distance. Hutto's paper focused on criticisms of distance sampling and, to a lesser degree, occupancy modeling. While both approaches incorporate detection probability, they do so at quite different scales, and the types of inferences they are useful for differ. We, therefore, deal with them separately. Distance sampling is arguably the most commonly used method to estimate densities of wild animal populations (Buckland et al. 2015). Like Hutto (2016a), we focus on point transect surveys, where surveying takes place at a set of point locations. Whether the method is useful in a particular study depends on the study goals, the ability to meet assumptions, and the consequences of violating those assumptions. Assumptions of conventional distance sampling are (1) sampling locations are random within the area of inference, (2) animals at the point are detected with certainty, (3) the survey is a snapshot in time, (4) measurements are made without error, and (5) detections are independent events. The first is a design assumption, and thus guaranteed to hold, given proper design. It ensures animals are located independently of point locations, and so the distribution of animals with respect to the points is known (a triangular distribution), allowing derivation of the conventional estimators of density and abundance. The certain detection at a point is key, with downward bias if violated. Additional data can be collected if it is not possible to meet the assumption (e.g., Burt et al. 2014). If animals move during the recording period, the snapshot assumption is violated. Time spent at the point is thus a compromise: time must be sufficient to ensure that all animals at or near the point are detected, but not so much that substantial movement occurs. Buckland (2006) proposed a method in which animal positions are recorded at a snapshot moment, with time before the snapshot used to locate animals, and time after used to confirm locations. If movement is likely to cause strong bias, cue-based methods might be used, in which cues (e.g., song bursts) are the objects of interest (Buckland 2006). Large measurement error can induce substantial bias, and extensions incorporating measurement error models are available (e.g., Borchers et al. 2010). However, as suggested by Hutto (2016a), it is preferable to use design and field methods to minimize errors, rather than to rely on analytical methods to address the problem after data collection. Finally, methods are extremely robust to failure of the independence assumption; see Buckland (2006) for an example. Some key statements in Hutto (2016a) ignore well-established features of distance sampling. The first reads "A decrease in number of bird detections with increasing distance from an observer is often a result of habitat heterogeneity rather than distance per se. As an extreme example, consider the nature of data that might be collected along a narrow riparian strip; the more riparian dependent species are never detected beyond 20 m, but one would be wrong to conclude from their detectability profile that they are not very detectable." This would only occur if the first assumption listed above is ignored. Points must be placed at random with respect to the population they are supposed to be sampling. If that does not happen, as in the example when sampling along rivers, density gradients with respect to the points are to be expected. Alternatives exist for such cases at the expense of a need for additional data and increased analytical complexity (e.g., Marques et al. 2010), but again we prefer a more sensible design that avoids the issues rather than such a model-based solution. A second invalid argument reads "Uniform vegetation conditions must lie within the range of detectability of a given species for its detectability profile to be meaningful." The property of pooling robustness (Buckland et al. 2004: 389–392) implies that estimation is largely unaffected by heterogeneity in probabilities of detection among individuals, habitats, etc. The estimated detection function represents the pooled detection function required to correct for detectability. Pooling robustness will not apply, for example, to habitat-specific density estimates, but it does apply to average densities through the study region, and to estimated total abundance. Nonetheless, sample size allowing, detectability can be modeled as a function of covariates, such as habitat, using multiple covariate distance sampling (see, e.g., Marques et al. 2007). A randomized sampling design of points should result in robust estimates of detection probability and density across a heterogeneous landscape, but proper stratification would also be required to provide habitat-specific density estimates. Conventional distance sampling may not be appropriate for small or narrow habitat patches relative to the typical survey radius. One of Hutto's (2016a) main propositions is that, rather than estimating detectability and abundance using a model-based approach, one should use raw count data from fixed-radius point transects. Based on his own data (Hutto 2016b), an example of the Black-backed Woodpecker (Picoides arcticus) is presented. It is suggested one could simply truncate data within 80 m to obtain a "reasonable presence–absence index of bird abundance" and get the same patterns of occurrence with respect to fire severity as with distance sampling. We tested this claim by fitting a hazard-rate detection function model to this data set (Fig. 1). Code required to produce Fig. 1, including a short description of the data and some additional comments, are provided as online Appendix S1. Contrasting with Hutto's claims, the analysis of the woodpecker data clearly shows that at 80 m detection is far from being certain, and even at 20 m detectability is no longer certain (cf. Fig. 1). Hutto (2016a) claims that "Data drawn from within a smaller 20 m radius become sparse…, so sample size artefacts are likely to have affected the resulting distribution pattern." Fig. 1 clearly shows that, if a fixed radius >20 m is chosen, for example 40 or 60 m, many birds will be missed, and density estimation assuming certain detection will be strongly biased. Given such clear influence of distance on detectability (cf. Fig. 1), one might ask why Hutto's analysis of proportions of woodpecker detections across habitat classes, using raw detection data truncated at 50 and 100 m, yield similar conclusions to densities estimated for each class based on distance sampling (Hutto 2016a: Fig. 3). Similarities arise because the woodpecker data do not exhibit differences in detectability by habitat class (Appendix S1: Fig. S6). Under strong habitat detectability differences, these patterns would not have coincided, and the use of uncorrected indices would likely have led to erroneous conclusions. A misguided criticism in Hutto (2016a) is that different analysts will arrive at different density estimates when modeling detectability. While, as in any modeling, subjective choices might be necessary, model selection and goodness-of-fit tools can help to reduce arbitrariness, and, more importantly, model uncertainty can be incorporated into inference. Further, one should aim for data that lead to robust inferences independent of particular analysis choices, in general achievable with good field methods and survey design. By contrast, under Hutto's suggested ad hoc fixed-radius approach, different analysts may select different fixed radii, potentially generating markedly different conclusions. Without a formal modeling framework there is no way of selecting an appropriate radius, evaluating the impact of different choices and propagating forward the variance induced by such process. Hutto (2016a) criticizes "uncovering bird–habitat relationships" using model-based detectability methods. Again, this criticism is misguided. The problem is not distance sampling or any other detectability-based approach. Problems arise if researchers collect data at a given detectability scale (say kilometers) and then wish to make inferences at a completely different scale (say meters). Naturally, if one ignores such a mismatch, existing relationships might be obscured and spurious relationships found. However, ignoring detectability does not solve the issue. The solution is common sense: collect data at spatial (and temporal) scales for which inferences are needed. Having clearly defined questions is fundamental. If patches of habitat are, on average, 5 m across, collecting point transect data up to 30 m will be equally problematic, whether or not one accounts for detectability within that distance. The general intent of occupancy models is to examine patterns of species presence across a landscape during a certain period of time, often referred to as a season. Depending upon the study objective, presence may be interpreted as "species is always at a location during the season" (i.e., locations are closed to changes in occupancy) or "species is present at some stage during the season" (i.e., location is used by the species; MacKenzie et al. 2006). Thus, irrespective of detection issues, the concept of species presence comprises both a spatial and a temporal aspect that should be well articulated as part of the study objective. Field work should then be consistent with the study objective. The dismissive nature of Hutto's comments about occupancy modeling is presumptuous about why a practitioner may be conducting point count surveys in the first place (e.g., to identify where a species is present within a very short timeframe, or to identify the level of use by a species of a range of habitats over a longer period of time). We agree that occupancy modeling will not be an appropriate tool for inferences under some types of objectives. However, there will be plenty of other situations where occupancy is a useful state variable and occupancy models are useful for separating out the biological and sampling processes using point-count data. Hutto (2016a) states that "One of the most powerful measures of habitat suitability is actually reflected well in naïve detectability because it is probably safe to assume that a point where a bird is frequently detected is a much better place to be than a point where a bird is rarely detected." Many examples where this claim would be utterly wrong are possible, and any biologist involved in data collection would have his or her own counter examples to share. For example, animals might be more conspicuous in areas they tend to avoid; they may prefer to be in areas where they are less easily detected by predators or prey, and hence by surveyors. The assumption that "naïve detectability" must reflect habitat preferences in general is both dangerous and unjustified. Occupancy models are typically described in terms of requiring repeat surveys, but that does not necessitate repeat visits to a location (e.g., Guillera-Arroita 2017). There are ways in which the repeat survey information can be collected in a single visit (e.g., multiple survey methods, multiple observers, incorporating time of detection or spatial subsampling), provided such a design is appropriate given the study objective and system of interest. In particular, there are extensions that consider the information about when/where detections take place in continuous time and that are well suited to a single-visit protocol (e.g., Garrard et al. 2008). Thus, collecting appropriate data that allow detection issues to be addressed during an analysis does not necessarily imply going to fewer locations due to the need for multiple visits per site. Given uncertainty about the outcome of the survey with respect to the biological quantity of interest (e.g., a probability of a false absence > 0.15), going to as many places as possible at the expense of multiple visits to some sites just results in a lot of data of questionable accuracy. Essentially, imperfect detection leads to a measurement error problem in the quantity of interest (species presence/absence; Guillera-Arroita et al. 2014). MacKenzie and Royle (2005) demonstrated that, under imperfect detection, going to fewer places with a greater number of repeat surveys can lead to more precise estimates than going to more places with fewer repeat surveys. This is because the probability of a false absence (i.e., the measurement error) contributes to the standard error of the occupancy estimate. In some situations, the most effective use of additional field effort is to reduce uncertainty due to the measurement error rather than going to a greater number of places (i.e., quality data before quantity of data). Finally, we note that while Hutto (2016a) implies that the assumptions of the basic occupancy model of MacKenzie et al. (2002) will often be violated with point-count data, there is a broad suite of "occupancy models" developed to extend and relax these assumptions (for details see Bailey et al. 2014 and Guillera-Arroita 2017). We conclude by challenging Hutto's concluding remark: "Common sense and biological insight really ought to prevail over what has become a frighteningly blind application of model-based solutions to the potential detectability problem." We naturally agree that common sense and biological insight are fundamental in ecological research. However, model-based solutions do not preclude these, but complement them. Model-based approaches, be it in wildlife abundance and occupancy estimation, or in other sub-fields of ecology, have become widespread, and their utility in ecological research is well appreciated (see, e.g., the recent editorial by Honrado et al. 2016). Model-based approaches, informed by biological insight and tamed by common sense, are well established and fruitful in applied ecology. In many respects, model-based approaches evolved with the realization that standardizing survey protocols to minimize detection failure across complex and variable field conditions is usually unrealistic (Ellingson and Lukacs 2003). The relative ease of access to advanced methods via dedicated software does mean that practitioners can implement sophisticated model-based approaches without understanding their requirements and assumptions. That is not a problem of the methods, but of the way they are used. We argue that the advantages of open access software still outweigh the disadvantages, especially if common sense is used and appropriate training undertaken. Therefore, it is unjustified to state that model-based approaches in general, and those that account for detectability in particular, should be avoided altogether. We agree with Hutto that there is a trade-off between breadth and depth in sampling, but point out that the correct way of resolving this trade-off is with a clearly defined a priori objective, underpinning the determination of an optimal study design (e.g., MacKenzie and Royle 2005). This brings us to what is the crux of many arguments about the performance of particular analytical methods: poorly defined study objectives. These should include a clear statement about the specific biological quantity of interest. Clearly defined objectives are vitally important for both short-term studies and long-term monitoring programs (Yoccoz et al. 2001). Without these, debates about appropriate field methods and analyses cannot be resolved because there is no common benchmark against which the pros and cons of alternative approaches can be assessed. We advocate an approach to wildlife science that integrates design- and model-based inference, allowing assumptions to be verified by data (Nichols et al. 2009). The approach advocated by Hutto (2016a) may be useful for generating hypotheses to be further investigated using more rigorous approaches. However, to support the effective conservation and management of wildlife populations, ecologists generally require reliable estimates of population size, along with associated measures of uncertainty (Martin et al. 2007). Even when assessing trends over time, ignoring detectability might obscure existing patterns or lead to perceived trends where no real trends exist (e.g., Norvell et al. 2003). A model is a representation of reality, not to be confused with reality itself. Model-based approaches to deal with detectability can be extremely useful, and will undoubtedly continue to be of widespread use in ecology and applied fields such as wildlife management. Under a given setting, it might be possible to disregard detectability, having considered its effects and concluded that, for the objectives at hand, these can be accommodated via a carefully chosen design. However, the burden of proof must not be inverted (see, e.g., MacKenzie and Kendall 2002). It is the responsibility of researchers wishing to ignore detectability to justify their choice and to present evidence that it will not have a misleading impact on the outcome of their study. T. A. Marques thanks support by CEAUL (funded by FCT, Portugal, through the project UID/MAT/00006/2013). G. Guillera-Arroita holds a Discovery Early Career Research Award from the Australian Research Council (project DE160100904). Any use of trade names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government. We thank Russell Norvell, John Sauer, Jim Nichols, and an anonymous reviewer for their constructive comments. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Vertebrate populations throughout the circumpolar north often exhibit cyclic dynamics, and predation is generally considered to be a primary driver of these cycles in a variety of herbivore species. However, weather and climate play a role in entraining cycles over broad landscapes and may alter cyclic dynamics, although the mechanism by which these processes operate is uncertain. Experimental and observational work has suggested that weather influences primary productivity over multi-year time periods, suggesting a pathway through which weather and climate may influence cyclic herbivore dynamics. Using long-term monitoring data, we investigated the relationships among multi-year weather conditions, measures of primary productivity, and the abundance of two cyclic herbivore species: snowshoe hare and northern red-backed vole. We found that precipitation (rain and snow) and growing season temperatures were strongly associated with variation in primary productivity over multi-year time horizons. In turn, fourfold variation in the amplitude of both the hare and vole cycles observed in our study area corresponded to long-term changes in primary productivity. The congruence of our results for these two species suggests a general mechanism by which weather and climate might influence cyclic herbivore population dynamics. Our findings also suggested that the association between climate warming and the disappearance of cycles might be initiated by changes in primary productivity. This work provides an explanation for observed influences of weather and climate on primary productivity and population cycles and will help our collective understanding of how future climate warming may influence these ecological phenomena in the future.

Small cetaceans (dolphins and porpoises) face serious anthropogenic threats in coastal habitats. These include bycatch in fisheries; exposure to noise, plastic and chemical pollution; disturbance from boaters; and climate change. Generating reliable abundance estimates is essential to assess sustainability of bycatch in fishing gear or any other form of anthropogenic removals and to design conservation and recovery plans for endangered species. Cetacean abundance estimates are lacking from many coastal waters of many developing countries. Lack of funding and training opportunities makes it difficult to fill in data gaps. Even if international funding were found for surveys in developing countries, building local capacity would be necessary to sustain efforts over time to detect trends and monitor biodiversity loss. Large-scale, shipboard surveys can cost tens of thousands of US dollars each day. We focus on methods to generate preliminary abundance estimates from low-cost, small-boat surveys that embrace a 'training-while-doing' approach to fill in data gaps while simultaneously building regional capacity for data collection. Our toolkit offers practical guidance on simple design and field data collection protocols that work with small boats and small budgets, but expect analysis to involve collaboration with a quantitative ecologist or statistician. Our audience includes independent scientists, government conservation agencies, NGOs and indigenous coastal communities, with a primary focus on fisheries bycatch. We apply our Animal Counting Toolkit to a small-boat survey in Canada's Pacific coastal waters to illustrate the key steps in collecting line transect survey data used to estimate and monitor marine mammal abundance.

Summary Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/plant distribution. Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods. We review recent developments in the field and consider the likely directions of future research before focussing on a popular approach based on generalized additive models. In particular, we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two‐stage model and smoothing in areas with complex boundaries. The methods discussed are available in an R package developed by the authors ( dsm ) and are largely implemented in the popular Windows software Distance.

As methods developers and data analysts, we often see data long after they have been collected. In some instances, no amount of clever analysis can retrieve a study that has fallen foul of poor field methods. In this chapter, we define what is meant by 'poor field methods', and by contrast, what constitutes 'good' field methods; and we describe means by which 'poor' field methods can be diagnosed and remedied before data collection is completed.

In this chapter, we consider some of the problems that may be encountered for specific taxa. We make no attempt to be comprehensive; rather, we have selected a few taxa to illustrate the types of issue that arise in some circumstances. The absence of a taxon from this chapter does not indicate that distance sampling methods are inappropriate for that taxon.

Conventional distance sampling may be thought of as an extension of plot sampling (Sect. 1.4), to allow imperfect detection of animals on the plots. Standard plot sampling is purely design-based: we use a randomized design to ensure that the density on plots, which we observe, can be taken as an estimate of density in the wider study area. By contrast, conventional distance sampling is a mix of design-based and model-based methods. Models are proposed for the detection function, and are fitted to the distance data using maximum likelihood methods. This component of estimation is therefore model-based. However, the likelihood maximized is not the full likelihood, but a conditional likelihood: the likelihood of the distances y from the line or point, conditional on the number n of animals detected.

One of the most common and pervasive questions in applied ecology relates to the size of a given population. How many animals are there? The question is intrinsically interesting, but perhaps even more important from an applied perspective, the actual answer has implications for most ecological processes affecting that population. The effective management of a population is not possible without knowing at least approximately how many individuals it includes. As an example, for a small population, a given mortality rate due to a newly introduced human disturbance might be important and a matter of concern, quickly leading the population to local extinction, but essentially not ecologically relevant for an abundant population. Therefore, knowledge about abundance is required to adequately interpret a wide variety of ecological processes affecting a given population.

Survey design is covered in greater detail by Buckland et al. (2001, pp. 230–248) and by Strindberg et al. (2004). We concentrate here on basic designs, and on issues that commonly need to be addressed. Some of those issues relate to analysis; in such cases, we provide forward references to where they are addressed. Although through most of the book, we describe line transect methods before point transect methods (for which analysis has greater complexity), for survey design, we consider point transect sampling first. This is because point transect surveys are simpler to design than are line transect surveys.

There are several variants of distance sampling, developed to extend the applicability of the methods to a wider range of populations. Several involve the use of traps or lures. The first use of traps in a distance sampling context was for trapping webs (Anderson et al. 1983), in which a series of points across the study region is selected, and a 'web' of perhaps 80 or more traps established around each point. In the case of trapping line transects, traps are distributed within a strip about the centreline (Lukacs et al. 2004). Both methods are very labour-intensive, and, while they depend on animal movement if animals are to encounter traps, that same movement generates bias.

Distance sampling has been referred to as a hybrid approach because it typically includes a design-based component, which deals with encounter rate, and a model-based component, which deals with detection probability. For populations occurring in clusters or groups, the group size estimation can be based on design (in the absence of size bias) or on a model (e.g. size-bias regression or multiple-covariate distance sampling with group size as a covariate). A model can be thought of as a set of assumptions. If we do not have all the data that we would like, we may need to make additional assumptions. As an example, if we are estimating the abundance of chimpanzees using an indirect method via nest surveys, in the absence of a local estimate of nest decay rate, we might assume that the decay rate for another place and time is valid for the current survey. This trade-off between data and assumptions can also be used in the opposite direction. If we have additional data above and beyond that required for conventional distance sampling (CDS) methods, this might allow us to relax our assumptions.