Ecological Informatics 14 (2013) 64–70

Contents lists available at SciVerse ScienceDirect

Ecological Informatics

j ourna l homepage: www.e lsev ie r .com/ locate /eco l in f

A systematic re-sampling approach to assess the probability of detecting otters Lutralutra using spraint surveys on small lowland rivers

G.S. Parry a,⁎, O. Bodger b, R.A. McDonald c, D.W. Forman a,1

a Swansea Ecology Research Team, Department of Biosciences, College of Science Swansea University, Singleton Park, Swansea, SA2 8PP, UKb Institute of Life Sciences, Swansea University, Singleton Park, Swansea, SA2 8PP, UKc Environment and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn TR10 9EZ, Cornwall, UK

⁎ Corresponding author at: Natural Environment teamAbbey Foregate, Shrewsbury, Shropshire SY2 6ND, UK.

E-mail addresses: parrylutra@gmail.com (G.S. Parry)(O. Bodger), r.mcdonald@exeter.ac.uk (R.A. McDonald),(D.W. Forman).

1 Tel.: +44 1792 295445.

1574-9541/$ – see front matter © 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.ecoinf.2012.11.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 6 March 2012Accepted 30 November 2012Available online 8 December 2012

Keywords:Field sign surveyDetection probabilityFalse negativeEurasian otterCarnivoreSpecies distribution

Assessing and monitoring populations of elusive species frequently rely on the identification of indirect signssuch as faeces. The absence of signs does not necessarily denote the absence of a species, thus, the ability todetermine the presence/absence is susceptible to false negative results. The probability of detection is centralto the interpretation and utility of data from field sign surveys. A low probability of detection may introduceconsiderable error into distribution patterns, resulting in inaccurate ecological conclusions.We used a systematic resampling approach, based on sequential spatial replication of spraint surveys, to inves-tigate the probability of detecting Eurasian otters (Lutra lutra L.) with different survey designs. This included thestandard otter transect survey methodology, which is widely used in conservation and scientific studies. In par-ticular, we focus on the impact of applying broad scale population assessment techniques at smaller spatialscales. Fortnightly catchment-level otter surveys were undertaken on four lowland rivers in South Wales, overa period of two years. GIS was used to construct binary vectors for each survey, denoting the presence (1) or ab-sence (0) of otters at each 50 m section of river. Vectors from all study rivers were pooled and resampled to testthe different survey designs. The mean probability of detecting otters based on the standard protocol of a single600 m transect surveywas very low (0.26±0.01 SE). The bestway of obtaining a detection probability of 0.8wasto undertake three repeat surveys at two separate sites, using a transect of 800–1000 m.We demonstrate how sequentially collected spatial data can be analysed to determine the reliability of field signsurveys. Increasing the number of visits and study siteswas amore efficientmeans of improving detection powerthan increasing transect length alone. The study emphasises the importance of determining detection probabil-ities and designing field sign surveys according to study scale and objectives. Our findings question the value ofsurvey designs that aim to provide an instantaneous assessment of species presence/absence.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

1.1. Population monitoring programmes

Effective techniques for population assessment and monitoring arecritical for conservationprogrammes and ecological research. Distributionand abundance data form the baseline of speciesmonitoring programmesand can provide an assessment of status, against which the influence ofother factors can be investigated. The method by which population dataare collected has considerable influence on their use and interpretation(Hirzel and Guisan, 2002).When interpreting data generated from popu-lation monitoring schemes the accuracy, precision, sample size and

, Shropshire Council, Shirehall,Tel.: +44 1743 252543., o.bodger@swansea.ac.ukd.w.forman@swansea.ac.uk

rights reserved.

statistical power associated with the survey methodology must be takeninto account (Gese, 2004; Macdonald et al., 1998). Yet many monitoringprogrammes do not ascertain these parameters (Marsh and Trenham,2008), or utilise survey methodologies that are unsuitable for the objec-tives of the programme (Mattfeldt et al., 2009). Without acknowledgingsurvey design and limitations there is a substantial risk of inaccurately de-scribing species distributions (Kéry et al., 2010). This can introduce errorand/or bias into assessments of conservation status, models of wildlife–habitat relationships (Gu and Swihart, 2004; Tyre et al., 2003) or otherbiotic interactions (Mackenzie et al., 2004), which may lead to inappro-priate management (Loiselle et al., 2003).

1.2. Carnivore population surveys

Accurately determining distribution is particularly problematic forhighlymobile and elusive species. These traits are typical ofmany carni-vores (Gittleman et al., 2001), thus, carnivore surveys and monitoringprogrammes are frequently based on the identification of indirectfield signs (Long et al., 2008). Faeces are a key field sign used to classify

65G.S. Parry et al. / Ecological Informatics 14 (2013) 64–70

positive sites, but they are susceptible to being overlooked and to dete-rioration. Furthermore, in many species faeces function as a scent mark(Hutchings and White, 2000; Lewis, 2006), so the density and locationof faecal marks can be influenced by their ecological function and, inturn, by population status. As a consequence, carnivore field sign sur-veys are liable to imperfect detection and may lead to false inference,usually arising from a failure to detect and wrongly inferring absencefrom an area (false negative).

False negative results affect the ability to determine population sta-tus, detect colonisation and may exaggerate decline or local extinction(Mackenzie, 2005). The error introduced by false negatives can beaccounted for by determining the probability of detection (Mackenzieet al., 2004); however, the detection probability of field surveys hasbeen determined in relatively few carnivores. Recent studies indicatedthat detection probability may frequently be under 0.5 (Jeffress et al.,2011; Smith et al., 2007; Thorn et al., 2011), rarely achieving the 0.8detection probability recommended for protected species monitoringprogrammes (Kendall et al., 1992a, 1992b). This reinforces the need toinvestigate the power of carnivore field survey designs, in order to es-tablish whether current monitoring programmes are fit for purpose.

1.3. Evaluating survey design

Site occupancy estimates the proportion of sites, within a study area,which are likely to be occupied by a species (Mackenzie et al., 2002) andthis approach is increasingly being used tomeasure species distribution(Guillera-Arroita et al., 2010; Hines et al., 2010; Kéry et al., 2010). Thesemodels incorporate detection probabilities, typically derived from tem-poral replication. It is also possible to calculate detection probabilitiesfrom spatial replication (Hines et al., 2010), provided there is temporalreplacement (Kendall andWhite, 2009). Determination of site occupan-cy is an advantageous approach to improving the accuracy of broadscale distribution data. It is also necessary sometimes to accurately de-termine the presence/absence at a specific site, as ecological patternsand life history traits often vary with scale (Bowyer and Kie, 2006)and patterns at different scales may have unique causes and conse-quences (Levins, 1992).

Carnivore field surveymethodologies designed for large scale popu-lation monitoring programmes are often utilised at smaller spatialscales (see Section 1.4), for example, at sites outlined for developmentor within designated conservation areas. It is therefore, important tounderstand if detection probabilities are affected by reducing spatialand temporal scale. Balestrieri et al. (2011) pointed out that this canonly be achieved through field surveys, but few studies have used sur-vey data to investigate the detection probabilities associated with spa-tially or temporally localised assessments (e.g. Balestrieri et al., 2011;Kéry, 2002; Kery et al. 2010).

1.4. Standard otter surveys

Many of the issues associated with monitoring carnivore populationsare typified in the assessment of Eurasian otter (Lutra lutra L.— hereafterreferred to as otter) populations. The standard otter survey protocol usedin the UK, Ireland and elsewherewas designed in the late 1970s, with theobjective of determining thebroad scale distribution of otters (Mason andMacdonald, 1986). It has since been used to monitor otter populationsacross the UK and mainland Europe (e.g. Jones and Jones, 2004; Prigioniet al., 2007; Crawford, 2010), and to survey other species of otter (e.g.González and Utrera, 2001; Nel and Somers, 2009). The standard ottersurvey methodology requires that field sign searches are conductedalong a 600 m transect of river bank, lake shore or coast (Mason andMacdonald, 1986). The most important field sign, and often the onlyone used, is faeces (spraint). Importantly, the absence of spraints doesnot necessarily imply an absence of otters (Kruuk et al., 1986).

The reliability of the standard 600 m transect size has frequently beenquestioned (e.g. Kruuk et al., 1986; Ruiz-Olmo et al., 2001). It has been

argued that the level of error associatedwith the standard surveymethodis acceptable, as it was designed to identify widespread distribution, rath-er than locate every otter population (Mason and MacDonald, 1986).However, the standard survey design is frequently used to detect thepresence/absence of otters at small spatial scales; for locally implementedconservation programmes (UK Biodiversity Steering Group, 1995), eco-logical studies (Bonesi et al., 2004; McDonald et al., 2007; Pedroso andSantos-Reis, 2006; Prenda and Grando-Lorencio, 1996) and in impact as-sessments in areas designated for development (e.g. Dudley, 2008;Spedding, 2009). No informationhas been obtained regarding surveyper-formance on short watercourses, which are a common and importantotter habitat in many areas (Brzeziński et al., 1993; Lanszki et al., 2009;Prenda and Grando-Lorencio, 1996).

Our study systematically re-sampled field survey sites on foursmall rivers over two years, and used these repeated samples toinvestigate the probability of detecting otters using spraint surveys.The analysis investigates the influence of season and evaluatesdifferent survey designs; including the 600 m standard transect sur-vey. We investigate the optimal approach to otter surveys, varyingtransect length and repetition, to identify the most efficient methodof obtaining a 0.8 probability of detection. Determining the reliabilityof the standard otter survey methodology provides context to datacurrently being used to make local conservation and development de-cisions. It also indicates the reliability of data collected through na-tional monitoring programmes. Both local level studies of carnivoredistribution and ecology, and broad-scale reviews of species status,will benefit from an improved understanding of how detectability isaffected by survey approach, scale and timing.

2. Materials and methods

2.1. Study area

The Gower peninsula (latitude 51°59′64″N, longitude 4°14′47″W) islocated in South West Wales (Fig. 1). The Peninsula is approximately20 km long and 12 kmwide covering an area of 188 km2. It has a tem-perate climate and contains a diversity of habitats including; rockyshore, sandy shore, mud flats, heath land, salt marsh, agricultural grass-land, coniferous and deciduous woodlands. There are numerous smallstreams and rivers on Gower. Four study river systems, known to con-tain otters, were included in this study; Burry Pill (latitude 51°37′21″N, longitude 4°14′31″W), Bishopston Pill (latitude 51°33′56″N, longi-tude 4°03′25″W), River Clyne (latitude 51°35′57″N, longitude 3°59′48″W) and Pennard Pill (latitude 51°34′26″N, longitude 4°06′44″W).The four rivers can all be classified as subtype BVc,which are small, low-land, impoverished sand/clay rivers, mainly flowing over limestone andsandstone (Holmes et al., 1999). The plant communities are dominatedby liverworts, ferns and filamentous algae (Holmes et al., 1999).

2.2. Spraint surveys

The four study rivers were surveyed every twoweeks for a period oftwo years between July 2005 and June 2007. Pilot surveys were carriedout at all of the sites in the last week of June 2005, during which all lo-cated spraints were removed. The surveys followed standard routesalong the main channel, covering as much of its length as possible.The length of the rivers and the proportion surveyed were determinedusing MapInfo Professional©. At all of the study sites, surveys began atthe rivermouth andmoved upstream. Both banks andmid-channel fea-tures, such as rocks and tree roots, were searched for spraints. A spraintsite was defined as a single feature (rock, root, stump, grass moundetc.). At each spraint site a GPS reading was taken using a 12 channeleTrex© device (Garmin Europe Ltd, Southampton, UK).Where possible,surveyswere not carried out during or after a period of heavy rainfall, asthis can reduce the number of spraints available (Brzeziński andRomanowski, 2006). Due to prolonged periods of rainfall this was not

Fig. 1. Location of the four rivers on the Gower Peninsular in southWales (Produced using MapInfo Professional© Pitney Bowes Software Inc., New York, USA) using an OS base mapobtained from EDINA©.

66 G.S. Parry et al. / Ecological Informatics 14 (2013) 64–70

always possible, so itwas notedwhether it rained on the survey day or onthe day preceding the survey. It could not be assumed that all of the avail-able spraints were located. However, all surveys were undertaken by thesame experienced surveyor, thereby standardising and minimising ob-server error over the entire duration of field sampling.

2.3. Mapping spraint data

Geo-referenced spraint position data were mapped using MapInfoProfessional ©. Weather conditions affected the accuracy of the GPSreadings, so a small number of the GPS points were not placed on theriver channel by the mapping software. During the fieldwork, notes de-scribing the location of sprainting sites were taken when the accuracyreading, provided by the GPS device, was less than 15 m. AnomalousGPS points were re-mapped to their correct locations using thesenotes; however, this was not possible for a small number of pointsand the number of affected points is detailed in the results. Such pointswere moved in a straight line to the closest section of river, and it is ac-knowledged that this introduces a small amount of error. However, nopoint would bemore than 15 m from its actual position, and in contextand scale of the study aims this level of error is trivial.

2.4. Assessing survey detection probability though systematic re-sampling

As otters were regularly recorded on all of the study rivers,throughout the two year study period, the rivers were considered tobe genuinely positive for otters. Therefore, surveys which failed to de-tect otter presence on the rivers were considered to have recorded afalse negative result. A Geographic Information System (GIS) MapInfoProfessional© was used to split the rivers into 50 m sections and thento determine the detection or otherwise (presence=1, absence=0)of spraints on each 50 m section during each survey period. To reducethe influence of individual river characteristics on the probability ofdetecting otters, the survey vectors from all four rivers were pooledand the resampling approach applied to this combined data set.

GISwas used to classify each river section at each time period as 0/1.A resampling approach was then developed to assess various candidatesurvey protocols, which varied in transect length, transect number and

the number of repeat visits. The resampling approach treated each 50 minterval as a separate survey start point and each candidate survey tech-nique was systematically applied to all starting points. In total, therewere 388 starting points, each representing one replication in theresampling procedure. For each candidate survey protocol, the preva-lence of positive 50 m sections was calculated at every starting point,using p=nP/(nP+nN), where p=prevalence of positive 50 m sec-tions, nN=number of starting points which were negative for otters,and nP=number of starting points which were positive for otters.The probability of detecting otters through a single survey, using tran-sects of varying length, was determined by calculating the mean preva-lence of positive 50 m sections at all starting points.

When the number of survey sites and/or survey visits was increased,the candidate survey technique was again systematically applied toeach starting point. The binomial distribution of the probability of re-cording a positive result was calculated for each starting point. Themean of this value, over all starting points, was used to determine theprobability of detecting otters. The probability of detecting otters witheach survey design was based on the equation Ψ=(1−q), whereq=probability of recording a false negative, which is an adaption ofthe equation q=(1−p), used to calculate the occurrence of false nega-tives by Brewer et al. (2002), outlined in detail by Strachan (2007). Theprobability of detecting otters was stratified by season, in order to in-vestigate seasonal variation in the performance of different surveydesigns.

2.5. Influence of spraint removal and degradation

During the surveys spraints were collected for subsequent dietaryanalysis. Spraint removal could potentially increase the probability ofrecording false negatives in future surveys. A pilot study undertakenon the River Clyne, found that most spraints disappeared within twoweeks (Hill et al., unpub. data). A second study found that the riverswere remarked within three days of removal (Parry et al., in prep). Itis acknowledged that spraint removal affected the independence ofthe surveys. However, the impact of removal on the occurrence offalse negativeswas likely to be negligible, due to the high rate of spraintdisappearance and frequent remarking behaviour.

Fig. 2. Seasonal variation in the mean probability of detecting otters on the study riv-ers, by conducting a single spraint survey along a continuous transect of varying length(m). The red horizontal line marks the 0.8 probability of detection and the vertical bluedashed line marks the detection probability associated with a standard 600 m transectsurvey.

67G.S. Parry et al. / Ecological Informatics 14 (2013) 64–70

3. Results

3.1. Spraint distribution

Otter presence was confirmed on all four study rivers over the studyperiod. The proportion of positive surveys varied between rivers(Table 1). Out of 2561 spraint locations mapped 18 GPS locationswere anomalous. Eight of these were correctly re-positioned usingfield notes. The remainder were moved in a straight line to the nearestsection of the river. A Kolmogorov–Smirnov (KS) test indicated that thefrequency distribution of spraint abundance along standard transectsdiffered significantly from a Poisson distribution on all of the study riv-ers (pb0.01), indicating that the distribution of spraints was spatiallyclustered.

3.2. Detection probability of the standard 600 m otter transect survey

The systematic re-sampling approach demonstrated that each 50 minterval was within 1062 m (±433 SE) of a spraint site used during thestudy period. Otter activity was also recorded in all seasons on all studyrivers (see A.1), demonstrating that otters were regularly active alongthe rivers. Despite this, the resampling analysis confirmed that the stan-dard otter survey design may frequently fail to confirm otter presence(Fig. 2). The mean probability of detection otters with the standard600 m transect survey was 0.26 (±0.01 SE). Increasing transect lengthdid increase the probability of detecting otters (Fig. 2), but a single visittransect survey at one site could not provide an 80% probability ofdetecting otters, even with a transect length of 4 km.

3.3. Effect of increasing the number of sites and undertaking repeat visits

Conducting repeat surveys, separated by a period of two weeks,improved the probability of detecting otters. When undertaking onerepeat visit at a single site, an 80% probability of detection could beachieved with a 2650 m (±95 SE) transect survey (Fig. 3). Adding athird visit meant an 80% probability of detection was achieved with a2050 m (±140 SE) transect survey. Due to the clustered distributionof spraint sites, some sections returned negative results throughoutthe study, despite positive results both upstream and downstream. Onthese sections, increasing the number of survey visits did not improvethe detection probability.

The resampling approach was used to determine if increasing thenumber of survey sites improved the probability of detection, by re-ducing the influence of spraint clustering. A site separation distanceof 500 m was selected because; although the mean distance betweenspraint sites was 307 m (±14 SE) the standard error was quite large,due to the clustering of spraints. Increasing the number of surveysites produced a marked improvement in the probability of detection,up to a point (Fig. 4). However, on 68 occasions surveying an entireriver returned a false negative result (Table 1). Therefore, withoutconducting repeat visits it was not possible to achieve a detectionprobability of 80%, regardless of the number of survey sites. Interest-ingly, applying the standard 600 m otter transect survey to 10 differ-ent sites provided only a 70% probability of detection.

Increasing both the number of visits and the number of sites madeit possible to achieve an 80% probability of detecting otters, but with a

Table 1The proportion of full river surveys that returned a positive result for otter on the over-all river between July 2005 and June 2007. Parentheses denote the number of positivesurveys/total number of surveys.

Study River Proportion of positive surveys % River length (km)

River Clyne 81.3 (39/48) 7.4Burry Pill 85.4 (41/48) 8.6Pennard Pill 64.6 (31/48) 5.9Bishopston Pill 27.1 (13/48) 4.2

shorter transect compared to increasing the number of survey visitsalone. Undertaking one repeat visit of two survey sites detected otterswith 80% probability if an 1100 m (±73 SE) transect was surveyed ateither site. Based on three visits at two different sites, a transectsurvey of 813 m (±66 SE) would provide an 80% probability of detec-tion. It was possible to achieve an 80% probability of detecting ottersusing the standard 600 m transect survey design, if six repeat surveyswere conducted at two separate sites (Probability=0.82±0.01 SE).

3.4. Temporal variation in detection probability

Of the 192 full river surveys undertaken during this study, 35%recorded no evidence of otters (Table 1), despite their presence beingconfirmed by previous and subsequent surveys. There were seasonalfluctuations in the transect length required to achieve an 80% probabil-ity of detecting otters (Table 2), but this variation was not significant.Based on a survey approach that undertook three repeat surveys attwo separate sites, there was no relationship between the transectlength required to achieve 80% detection probability and variation inrainfall, temperature and wind speed (data supplied by theMet Office).Thus, the incidence of false negatives from surveys of an entire rivercould not be attributed to seasonal ormeteorological factors. Other pos-sible factors influencing this are discussed in Section 4.4.

Fig. 3. The mean probability of detecting otters by repeatedly surveying one site using acontinuous transect of varying length (m). The red horizontal linemarks the 0.8 probabil-ity of detection and the vertical blue dashed linemarks the detection probability associat-ed with a standard 600 m transect survey.

Fig. 4. The mean probability of detecting otters on the study rivers by repeatedly survey-ing two sites, separated by at least 500 m of river bank, using transects of varying length(m). The red horizontal line marks the 0.8 probability of detection and the vertical bluedashed line marks the detection probability associated with a standard 600 m transectsurvey.

68 G.S. Parry et al. / Ecological Informatics 14 (2013) 64–70

4. Discussion and conclusions

4.1. Systematic re-sampling field survey data with spatial replication

It is important to evaluate the performance of field surveys in orderto validate the information they generate, particularly in the case ofprotected species. Over the past decade, it has been demonstrated thatfield sign surveys used to monitor carnivore populations often have alow probability of detection (Jeffress et al., 2011; Karanth et al., 2011;Thorn et al., 2011). Our approach estimated detection probability by sys-tematically resampling field survey data, collected during an intensivetwo year study of four small lowland rivers. Different points along thesurvey transects were used as spatial replicates, which were resampledto assess the effectiveness of different survey designs. Using spatial rep-licates to estimate detection probability risks a loss of independence, butthis approach has been shown to be acceptable for sequentially collecteddata from highly mobile species (Kendall and White, 2009).

In highly mobile species, spatial replication produces more reliableestimates of detection probability than temporal replication, particular-ly at small spatial scales. This is because the presence or absence at aspecific site may vary temporally, thereby influencing estimates of de-tection probability based on temporal replication. Using sequential spa-tial replication at known positive site overcomes this source of error.This approach was used to determine the probability of detecting amobile and elusive carnivore. Otters have large ranges and regularly

Table 2Seasonal fluctuations in the transect length required to achieve an 80% probability ofdetecting otters on small lowland rivers, based on three repeat surveys at two separatesites. Dashed lines indicate the standard 600 m transect survey and the transect lengthrequired to achieve a 0.8 probability of detection is shown in bold.

Transect length (m) Probability of detecting otters

Summer Autumn Winter Spring

100 0.31 0.25 0.32 0.35200 0.47 0.37 0.46 0.51300 0.55 0.45 0.58 0.60400 0.62 0.51 0.65 0.68500 0.67 0.57 0.70 0.72600 0.71 0.63 0.75 0.76700 0.75 0.68 0.79 0.80800 0.80 0.72 0.83 0.83900 0.83 0.77 0.86 0.861000 0.86 0.80 0.89 0.89

covering many km per day (Green et al., 1984; Ruiz-Olmo et al.,2001). Our analysis determined the probability of detecting otterswhen they had recently been active in the study area. This is particularlyuseful when trying to establish the presence or absence of otters atsmall spatial and temporal scales.

4.2. Evaluation of the standard otter transect survey

The standard 600 m transect survey was designed to monitor otterdistribution at broad scales. We demonstrate that using the standardsurvey design at a smaller spatial scale only provided a 26% probabilityof detecting otters, considerably lower than the recognised minimumlevel of 0.8 (Kendall et al., 1992a, 1992b). This suggests that fieldsurveys designed for broad scale applications may not be suitable forsmaller scale applications, such as making instantaneous assessmentsof the presence/absence at specific sites. The routine application of thestandard national otter survey design for this purpose is a cause for con-cern. National Otter Surveys often assess just one 600 m transect onsmall lowland rivers, such as those on Gower (Jones and Jones, 2004;Strachan, 2007; Crawford, 2010). Therefore, misusing these data forlocal assessments of otter occurrence risks overlooking positive sites,due to the low probability of detection.

In the context of broad scale monitoring programmes, a low proba-bility of detection is mitigated by the large number of survey sites persample unit, typically a catchment, 10 km or 50 km square. For exam-ple, the mean number of survey sites per hydrological catchment inthe National Otter survey of Wales is 67.9 (±6.3 SE) in Wales (Jonesand Jones, 2004). Our analysis did not investigate probability ofobtaining false negative results at this broad scale, but it demonstratedthat variation in the number of survey sites can affect the probability ofdetecting otters. Further work is required to determine whether this ispertinent for broad scale studies, where there are differences in thenumber of sampling sites per sampling unit.

Our findings support existing concerns regarding the hazards ofadopting sampling protocols that have not been explicitly designedfor the monitoring objectives (Mackenzie and Royle, 2005; Mattfeldtet al., 2009). Previous work highlighted the importance of detectionprobabilities when comparing species distributions between differentareas and habitats (Gu and Swihart, 2004; Mackenzie et al., 2002;Tyre et al., 2003). Based on our local assessment we advocate a broaderscale, robust assessment of otter survey protocols for a range of moni-toring objectives.

4.3. Approaches to improving the probability of detection

Field surveys should be designed to optimise their accuracy with re-spect to effort. Particular emphasis should be placed on avoiding falsenegatives, which limit the ability to effectively study and conserve spe-cies (Moilanen et al., 2006). The current study concurs with previouswork (Balestrieri et al., 2011; Ruiz-Olmo et al., 2001) that one-off600 m transect surveys cannot reliably detect otters, even in areaswhere they are well established. Building on this localised assessment,there are three basic ways to improve detection probability 1) increasetransect length, 2) increase the number of survey sites and 3) undertakerepeat surveys. Here we have shown that increasing the number of sur-vey sites produced the greatest improvement in survey performance. It islikely that this counteracts the clustered distribution of spraints, whichled to some sections being consistently negative. However, in order toachieve a detection probability of 80% it was also necessary to undertakerepeat surveys. Increasing transect lengthwas the least effectivemethodof improving detection probability, although, all designs that achieved an80% probability of detection required transect in excess of 600 m.

The optimal survey approach will be a trade-off between conductingrepeat visits, adding survey sites and increasing transect length, whichwill depend upon the study objectives, the biology of the target organ-ism, and what is logistically practical (Mackenzie and Royle, 2005).

69G.S. Parry et al. / Ecological Informatics 14 (2013) 64–70

Surveying a large number of sites may be less efficient than surveyingfewer sites more often, particularly when detection power is low (Tyreet al., 2003). The current study supports this view, suggesting that themost efficient survey design to determine the presence/absence of otterson small lowland rivers is three repeat surveys of two sites, using a tran-sect of 800–1000 m.

4.4. Implications for assessing otter populations

Our analysis indicates that the standard otter survey design producesdata with low resolution. If this is reflected in other habitats, the distribu-tion of this species may be underestimated and changes in distributionpoorly resolved, affecting the ability to determine population status or de-tect colonisation and extinction events (Mackenzie, 2005). It subsequent-ly becomes difficult to identify factors that contribute to local extinctionsor the failure to re-colonise areas, which are poorly understood. Further-more, patterns in detectability could erroneously be identified as patternsin distribution (Kéry et al., 2010), leading to inaccurate interpretations ofhabitat use (Gu and Swihart, 2004) and interactions with other species(Mackenzie et al., 2004). Our findings have direct implications for otterconservation, as local level development planning and conservation deci-sions are often based on data obtained by applying the standard 600 mtransect survey to small spatial scales (see Section 1.4). Additional surveyeffort is likely to be required, particularly in areas believed to have small,threatened or declining populations.

This study raises a wider issue of making instantaneous assessmentsof populations from short term surveys. Assessments based on a singlevisit of one site are likely to be volatile in highly mobile species, due tothe dynamic nature of their ecology. Logistical constraints often lead tosimplified designs with known biases (Albert et al., 2010), collectingdata of relatively low value. Developing effective methods of assessingpopulations requires consideration of all the factors that could influencethe probability of detection. This could include changes in the size,density, demographic or social organisation of a population; changes inresource availability or differences in surveyor ability. Detection proba-bility may also vary with habitat structure (Boulinier et al., 1998; Kéry,2002). Investigating the influence of these factors would improve ourability to reliably assess and monitor populations of elusive, mobilespecies.

4.5. Conclusions

We demonstrate that re-sampling spatially replicated field datacan estimate detection probabilities and improve field survey design.Emphasis is placed on considering the objectives of the study and thevalue of repeat visits at multiple sites. Undertaking similar studies in arange of species and habitats would improve our ability to detectchanges in the distribution and size of carnivore populations.

Traditional field sign surveys are restricted by their inability toestimate the number, identity, sex or status of individuals. Moleculartools make it possible to ascertain this information from faeces,significantly increasing the value of field sign surveys. A molecularapproach would also address issues of reliability concerning identifi-cation (Birks et al., 2005; Harrington et al., 2010). The use of suchtools is largely restricted to scientific studies; we should considerhow to make them a realistic option for conservation programmesat all levels.

Acknowledgements

Wewould like to thank the RoryWilson andMikeGravenor for theircomments and advice concerning this study. We are also grateful toWaylie Mitchell, who regularly assisted GP in the field.

References

Albert, C.H., Yoccoz, N.G., Edwards, T.C., Graham, C.H., Zimmermann, N.E., Thuiller, W.,2010. Sampling in ecology and evolution — bridging the gap between theory andpractice. Ecography 33, 1028–1037.

Balestrieri, A., Remonti, L., Prigioni, C., 2011. Detectability of the Eurasian otter by stan-dard surveys: an approach using marking intensity to estimate false negative rates.Naturwissenschaften 98, 23–31.

Birks, J.D.S., Messenger, J.E., Braithwaite, A.C., Davison, A., Brookes, R.C., Strachan, C.,2005. Are scat surveys a reliable method for assessing distribution and populationstatus of pine martens? In: Harrison, D.J., Fuller, A.K., Proulx, G. (Eds.), Martens andFishers (Martes) in Human Altered Environments: An International Perspective.Springer Science, New York, pp. 235–252.

Bonesi, L., Chanin, P., MacDonald, D.W., 2004. Competition between Eurasian otter Lutralutra and American minkMustela vison probed by niche shift. Oikos 106, 19–26.

Boulinier, T., Nichols, J.D., Sauer, J.R., Hines, J.E., Pollock, K.H., 1998. Estimating species rich-ness: the importance of heterogeneity in species detectability. Ecology 79, 1018–1028.

Bowyer, R.T., Kie, J.G., 2006. Effect of scale on interpreting life-history characteristics ofungulates and carnivores. Diversity and Distributions 12, 244–257.

Brewer, M.J., Elston, D.A., Green, R., 2002. Scoping study to consider the options for acosteffective, statistically-robust otter surveillance programme in Scotland —2003/2004. Unpublished report to Scottish Natural Heritage.

Brzeziński, M., Romanowski, J., 2006. Experiments on spraint activity of otters (Lutralutra) in the Bieszczady mountains, southeastern Poland. Mammalia 70, 58–63.

Brzeziński, M., Jędrzejewski, W., Jędrzejewska, B., 1993. Diet of otters (Lutra lutra)inhabiting small rivers in the Bialowieza National Park, eastern Poland. Journal ofZoology London 230, 495–501.

Crawford, A., 2010. Fifth Otter Survey of England 2009–2010. The Environment Agency,Bristol.

Dudley, A., 2008. Otter andWater Vole Survey: Proposed Development at BlueHavenMa-rine Rugby, Report No.MMME-103441-01. Middlemarch Environmental Ltd, Rugby.

Gese, E.M., 2004. Survey and census techniques for canids. In: Sillero-Zubiri, C.,Hoffman, M., Macdonald, D.W. (Eds.), Canids: Foxes, Wolves, Jackals, and Dogs.IUCN/SSC Canid Specialist Group, Gland and Cambridge, pp. 273–279.

Gittleman, J.L., Funk, S.W., Macdonald, D.W., Wayne, R.K., 2001. Why carnivore conserva-tion? In: Gittleman, J.L., Funk, S.W., Macdonald, D.W., Wayne, R.K. (Eds.), CarnivoreConservation Conservation Biology, 5. Cambridge University Press, Cambridge, pp. 1–8.

González, I., Utrera, A., 2001. The distribution of the Neotropical otter Lontra longicaudisin the Venezuelan Andes: habitat and status of the population. IUCN Otter Special-ist Group Bulletin 18, 27–33.

Green, J., Green, R., Jefferies, D.J., 1984. A radio-tracking survey of otters Lutra lutra on aPerthshire river system. Lutra 27, 85–145.

Gu, W., Swihart, R.K., 2004. Absent or undetected? Effects of non-detection of speciesoccurrence on wildlife habitat models. Biological Conservation 116, 195–203.

Guillera-Arroita, G., Morgan, B., Ridout, M., Linkie, M., 2010. Species occupancy model-ing for detection data collected along a transect. Journal of Agricultural, Biological,and Environmental Statistics 16, 301–317.

Harrington, L.A., Harrington, A.L., Hughes, J., Stirling, D., Macdonald, D.W., 2010. The ac-curacy of scat identification in distribution surveys: American mink, Neovisonvison, in the northern highlands of Scotland. European Journal of Wildlife Research56, 377–384.

Hines, J.E., Nichols, J.D., Royle, J.A., MacKenzie, D.I., Gopalaswamy, A.M., Kumar, N.S.,Karanth, K.U., 2010. Tigers on trails: occupancy modeling for cluster sampling. Eco-logical Applications 20, 1456–1466.

Hirzel, A.H., Guisan, A., 2002. Which is the optimal sampling strategy for habitat suit-ability modelling. Ecological Modelling 157, 331–341.

Holmes, N., Boon, P., Rowell, T., 1999. Vegetation Communities of British Rivers a RevisedClassification. Joint Nature Conservation Committee, Peterborough, pp. 23–24.

Hutchings, M.R., White, P.C.L., 2000. Mustelid scent-marking in managed ecosystems:implications for population management. Mammal Review 30, 157–169.

Jeffress, M.R., Paukert, C.P., Sandercock, B.K., Gipson, P.S., 2011. Factors affecting detect-ability of river otters during sign surveys. Journal of Wildlife Management 75,144–150.

Jones, T., Jones, D., 2004. Otter Survey of Wales 2002. Environment Agency, Bristol.Karanth, K.U., Gopalaswamy, A.M., Kumar, N.S., Vaidyanathan, S., Nichols, J.D., MacKenzie,

D.I., 2011. Monitoring carnivore populations at the landscape scale: occupancymodelling of tigers from sign surveys. Journal of Applied Ecology 48, 1048–1056.

Kendall, W.L., White, G.C., 2009. A cautionary note on substituting spatial subunits forrepeated temporal sampling in studies of site occupancy. Journal of Applied Ecolo-gy 46, 1182–1188.

Kendall, K.C., Metzgar, L.H., Patterson, D.A., Steele, B.M., 1992a. Power of sign surveys tomonitor population trends. Ecological Applications 2, 422–430.

Kendall, K.C., Metzgar, L.H., Patterson, D.A., Steele, B.M., 1992b. Power of sign surveys tomonitor population trends. Ecological Applications 2, 422–430.

Kéry, M., 2002. Inferring the absence of a species: a case study of snakes. The Journal ofWildlife Management 66, 330–338.

Kéry, M., Gardner, B., Monnerat, C., 2010. Predicting species distributions from check-list data using site-occupancy models. Journal of Biogeography 37, 1851–1862.

Kruuk, H., Conroy, J.W.H., Glimmerveen, U., Ouwererk, E.J., 1986. The use of spraints tosurvey populations of otters Lutra lutra. Biological Conservation 35, 187–194.

Lanszki, J., Szeles, L.G., Yozon, G., 2009. Diet composition of otter (Lutra lutra L.) livingon small watercourses in Southwestern Hungary. Acta Zoologica 55, 293–306.

Levin, S.A., 1992. The problem of pattern and scale in ecology: the Robert H. MacArthurAward lecture. Ecology 73, 1943–1967.

Lewis, R.J., 2006. Scent marking in Sifaka: no one function explains it all. American Journalof Primatology 68, 622–636.

70 G.S. Parry et al. / Ecological Informatics 14 (2013) 64–70

Loiselle, B.A., Howell, C.A., Graham, C.H., Goerck, J.M., Brooks, T., Smith, K.G., Williams,P.H., 2003. Avoiding pitfalls of using species distribution models in conservationplanning. Conservation Biology 17, 1591–1600.

Long, R.A., Mackay, P., Ray, J., Zielinski, W.J., 2008. Noninvasive Survey Methods forCarnivores. Island Press, Washington, DC.

Macdonald, S.M., Mason, C.M., 1987. Seasonal marking in an otter population. ActaTheriologica 32, 37–38.

Macdonald, D.W., Mace, G., Rushton, S., 1998. Proposals for Future Monitoring of BritishMammals. Department of Environment, Transport and the Regions, London.

Mackenzie, D.I., 2005. Was it there? Dealing with imperfect detection for species pres-ence/absence data. Australian & New Zealand Journal of Statistics 47, 65–74.

Mackenzie, D.I., Royle, J.A., 2005. Designing occupancy studies: general advice and allo-cating survey effort. Journal of Applied Ecology 42, 1105–1114.

MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, A.J., Langtimm, C.A.,2002. Estimating site occupancy rates when detection probabilities are less thanone. Ecology 83, 2248–2255.

MacKenzie, D.I., Bailey, L.L., Nichols, J.D., 2004. Investigating species co-occurrence pat-terns when species are detected imperfectly. Journal of Animal Ecology 73, 546–555.

Marsh, D.M., Trenham, P.C., 2008. Current trends in plant and animal population. Con-servation Biology 22, 647–655.

Mason, C.F., Macdonald, S., 1986. Otters: Ecology and Conservation. Cambridge Univer-sity Press, Cambridge.

Mattfeldt, S.D., Bailey, L.L., Grant, E.H.C., 2009. Monitoring multiple species: estimatingstate variables and exploring the efficacy of a monitoring program. Biological Con-servation 142, 720–737.

McDonald, R.A., O'Hara, K., Morrish, D.J., 2007. Decline of invasive alien mink (Mustelavison) is concurrent with recovery of native otters (Lutra lutra). Diversity and Dis-tributions 13, 92–98.

Moilanen, A., Wintle, B.A., Elith, J., Burgman, M., 2006. Uncertainty analysis for region-al‐scale reserve selection. Conservation Biology 20, 1688–1697.

Nel, J.A.J., Somers, M.J., 2009. Distribution and habitat choice of Cape clawless otters, inSouth Africa. South African Journal of Wildlife Research 37, 61–70.

Pedroso, N.M., Santos-Reis, M., 2006. Summer diet of Eurasian otters in large dams ofsouth Portugal. Hystrix 17, 117–128.

Prenda, J., Grando-Lorencio, C., 1996. The relative influence of riparian structure andfish availability on otter Lutra lutra L. sprainting activity in a small Mediterraneancatchment. Biological Conservation 76, 9–15.

Prigioni, C., Balestrieri, A., Remonti, L., 2007. Decline and recovery in otter Lutra lutrapopulations in Italy. Mammal Review 37, 71–79.

Ruiz-Olmo, J., Saavedra, D., Jiménez, J., 2001. Testing the surveys and visual and trackcensuses of Eurasian otters (Lutra lutra). Journal of Zoology, London 253, 359–369.

Smith, J.B., Jenks, J.A., Klaver, R.W., 2007. Evaluating detection probabilities for Americanmar-ten in the Black Hills, South Dakota. Journal of Wildlife Management 71, 2412–2416.

Spedding, E., 2009. Midpoint 18 Development, Middlewich, Cheshire, Otter and WaterVole Survey, Report No. FSE 96920C 3.1. Parsons Brinckerhoff, Bristol.

Strachan, R., 2007. National survey of otter Lutra lutra distribution in Scotland 2003–04.Scottish Natural Heritage Commissioned Report No. 211, Inverness.

Thorn, M., Green, M., Bateman, P.W.,Waite, S., Scott, D.M., 2011. Brown hyaenas on roads:estimating carnivore occupancy and abundance using spatially auto-correlated signsurvey replicates. Biological Conservation 144, 1799–1807.

Tyre, A.J., Tenhumberg, B., Field, S.A., Niejalke, D., Parris, K., Possingham, H.P., 2003.Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecological Applications 13, 1790–1801.

UK Biodiversity Steering Group, 1995. Biodiversity: The UK Steering Group Report,volumes 1 and 2. HMSO.