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Basic and Applied Ecology
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Authors: Kristin Wäber* & Paul M. Dolman• Deer abundance estimation
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Deer abundance estimation at landscape-scales in heterogeneous forests
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School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
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*Corresponding author present address:
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Forestry Commission England, East England, Santon Downham, Brandon, IP27 0TJ, UK
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Tel.: +44 300 067 4535; fax: +44 1842 811309.
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Email address: [email protected].
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Abstract
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Reliable estimates of deer abundance support effective management of source-sink
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population dynamics in complex landscapes and improve understanding of the relation between
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deer density and biodiversity impacts. Performance of distance sampling using thermal imaging of
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Reeves’ muntjac Muntiacus reevesi and roe deer Capreolus capreolus was examined across 123 km2
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of conifer forest in Eastern England, sampling 1567 km in total. For muntjac distance sampling was
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compared to estimates from drive counts in 2007. For each of three subsequent winters (2008-
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2010), we compared the magnitude and precision of forest-wide abundance estimated from
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analytical designs that: i) ignored potential habitat-specific detectability, either with uneven or
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balanced sampling effort; ii) controlled for sampling effort and/or density among seven forest blocks
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(mean = 18.8 km2 ± 11.1 SD); iii) accounted for potential movement prior detection; iv) accounted
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for varying detectability among habitat classes (as a covariate), while controlling for differing
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densities among blocks. Detectability was further examined in models that stratified to estimate
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habitat-specific Effective Strip Width (ESW).
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Estimated muntjac densities from distance sampling were of similar magnitude to estimates
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from drive counts. Over 2008-2010, we observed 1926 muntjac and 921 roe groups; allowing robust
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abundance estimation and habitat-specific analysis. ESWs in open habitat were 31% and 27% greater
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than in mature and 45% and 46% greater than in dense habitat, for roe and muntjac respectively.
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Although differences in densities among model designs were not large, ignoring block or habitat
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effects gave higher estimates, while models that accounted for habitat-specific detectability gave
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lower (-8%) and more precise (38% reduction in CV) estimates (n = 3, muntjac: 5.3-7.5% CV; roe
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deer: 8.8-12.6% CV). The similarity of density estimates between ungrouped and grouped data and
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analysis of behaviour of detected deer support the conclusion that distance estimates were not
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biased by avoidance.
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We conclude that distance sampling using thermal imaging is a robust and powerful method
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for estimating deer density. In heterogeneous forest density estimates will be improved by
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accounting for varying detectability among growth stages or habitats.
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Zusammenfassung
Robuste Abundanzwerte für Schalenwildarten sind wichtig, da sie das nachhaltige
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Management von Immigration und Emigartion (Ein- und Auswanderungen) in komplexen
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Landschaften unterstützen und das Verständnis von Schalenwilddichte und deren Einfluss auf die
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Biodiversität erhöhen. In der vorliegenden Studie wurde die Performanz von distance sampling mit
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Hilfe einer Wärmebildkamera am Beispiel von Reeves‘ Muntjak (Muntiacus reevesi) und des Rehes
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(Capreolus capreolus) in einem Nadelwald (123 km2) in Ost-England untersucht. Dabei wurden
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insgesamt 1567 km an Linientransekten zurückgelegt. Im Jahr 2007 wurden für das Muntjak
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erstmalig die Ergebnisse der Abundanzermittlung mit distance sampling mit denen einer
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Treibzählung verglichen. In den folgenden drei Jahren wurden Varianz und Genauigkeit waldweiter
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Abundanzen, die mit verschiedenen analytischen Designs berechnet wurden, verglichen. Dabei
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wurden entweder i) die möglichen habitatspezifischen Entdeckungswahrscheinlichkeiten und die
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Häufigkeit, mit der die Transekte befahren wurden, vernächlässigt, oder ii) die Frequenz der
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Transektbefahrung und/oder die Abundanzunterschiede in 7 Waldrevieren (Mittelwert = 18.8 km2 ±
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11.1 SD) wurden berücksichtigt, oder iii) mögliche Bewegungen von der ursprünglichen Position,
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bevor Tiere entdeckt wurden, wurden berücksichtigt, oder iv) es flossen unterschiedliche
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Entdeckungswahrscheinlichkeiten in verschiedenen Habitattypen (als Kovariate) inklusive der
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Abundanzunterschiede in den Waldrevieren in die Berechnungen ein. Darüber hinaus wurde die
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Entdeckungswahrscheinlichkeit in den Habitattypen auf der Basis der spezifischen effektiven
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Streifenbreite (ESW) untersucht.
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Die Abundanzberechnungen der Treibzählungen und des distance sampling ergaben
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vergleichbare Ergebnisse für das Muntjak. Von 2008 bis 2010 konnten 1926 Muntjak- bzw. 921
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Rehgruppen erfasst werden, was eine robuste Analyse der Daten erlaubte. Die ESW von Rehen und
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Muntjaks war in offenen Habitaten jeweils 31% bzw. 27% grösser als in Altholzbeständen und jeweils
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45% und 46% grösser als in Dickungen.
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Auch wenn sich die Ergebnisse der Abundanzberechnungen nicht wesentlich unterschieden,
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konnten wir doch zeigen, dass die Vernachlässigung von Abundanzunterschieden, Häufigkeit der
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Transektbefahrung in den Waldrevieren oder der habitatspezifischen
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Entdeckungswahrscheinlichkeiten zu höheren Abundanzwerten führte, während Designs welche
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habitatspezifische Entdeckungswahrscheinlichkeiten beachteten, geringere (-8%) und präzisere
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Abundanzen (38% niedriger CV) ergaben (n = 3, muntjac: 5.3-7.5% CV; roe deer: 8.8-12.6% CV). Die
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Ähnlichkeiten der Abundanzberechnung zwischen separaten und in Intervalle unterteilten
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Beobachtungen sowie eine sorgfältige Verhaltensanalyse der beobachteten Tiere unterstützen die
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Schlussfolgerung, dass die Abundanzen nicht durch eine Vermeidung des Beobachters negativ
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beeinflusst wurden.
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Wir schlussfolgern, das distance sampling mit Hilfe einer Wärmebildkamera eine robuste und
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leistungsfähige Methode ist, um Schalenwildabundanzen zu ermitteln. In heterogenen Wäldern
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verbessert die Berücksichtigung von habitatspezifischen Entdeckungswahrscheinlichkeiten die
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Abundanzberechnung.
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Keywords Thermal imaging; Distance sampling; Abundance estimation; Capreolus capreolus;
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Evidence-based management; Muntiacus reevesi; Detection functions
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Introduction
Native deer are an important element of biodiversity. However, in both Europe and North
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America increasing numbers of native and invasive deer are altering the structure of forest
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vegetation (Gill & Beardall 2001; Goldberg & Watson 2011; Joys, Fuller & Dolman 2004) and the
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abundance, species and trait composition of woodland bird (Gill & Fuller 2007; Holt, Fuller & Dolman
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2011; Holt, Fuller & Dolman 2014; McShea & Rappole 2000), small mammal (Buesching, Newman,
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Jones & Macdonald 2011; Flowerdew & Ellwood 2001; McShea 2000) and insect (Côte, Rooney,
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Tremblay, Dussault & Waller 2004; Feber, Brereton, Warren & Oates 2001; Pollard & Cooke 1994)
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assemblages. Local impacts can be mitigated by deer management guided by simple indices of
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relative abundance, such as impact scores (Cooke & Farrell 2001). But there are many situations
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where knowledge of deer numbers and density is advantageous. Understanding numbers and spatial
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variation in density is necessary for effective management, without this understanding source-sink
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dynamics driving regional population expansion may not be controlled (Wäber, Spencer & Dolman
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2013). Impacts vary with density (Gill & Morgan 2010) and the relation between ungulate density
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and vegetation recovery is non-linear (Fuller & Gill 2001; Tremblay, Huot & Potvin 2007), but poorly
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understood. There is, therefore, a need for methodologies to quantify the density of deer in forest
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habitats.
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Knowledge of deer numbers is also an important element in adaptation of silvicultural
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management to changing climate. Synergistic effects of changing weather patterns and distributions
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of forest diseases and pests can necessitate radical change in tree species planting composition
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(Mason, Nicoll & Perks 2009). Adaptive diversification of tree crops may be compromised where
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species are prone to herbivore damage (Broadmeadow, Webber, Ray & Berry 2009), but outcomes
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are uncertain. An adaptive approach to management is therefore required, in which relating
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measures of tree performance to deer density will be key elements.
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Despite the importance of population assessment, estimating deer densities can be
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problematic - especially in forested landscapes. Simple counts or spotlight counts are impractical in
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dense vegetation while capture-mark-resighting or drive counts are labour-intensive and challenging
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(e.g. Focardi, Pelliccioni, Petrucco & Toso 2002b). Accuracy of faecal pellet-group methods is
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reduced by uncertain estimates of defecation and decay rates (Campbell, Swanson & Sales 2004;
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Hemami, Watkinson, Gill & Dolman 2007) and for lowland Britain is compromised by rapid decay
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rates (Hemami & Dolman 2005a). Consequently, nocturnal distance sampling using thermal imaging
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equipment has been recommended as a cost-effective method (Focardi, De Marinis, Rizzotto & Pucci
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2001; Gill, Thomas & Stocker 1997; Hemami et al. 2007; Smart, Ward & White 2004). Distance
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sampling counts have been calibrated against independent estimates from dung counts for roe deer
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Capreolus capreolus (Hemami et al. 2007) and for wild boar Sus scrofa and fallow deer Dama dama
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against capture-mark-resighting (Focardi, Franzetti & Ronchi 2013; Franzetti, Ronchi, Marini, Scacco,
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Calmanti et al. 2012). However, for Reeves’ muntjac Muntiacus reevesi, an invasive introduced
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species now widespread and abundant in the UK (Ward 2005), density estimates from distance
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sampling differed from those obtained by dung counts, likely because the defecation rate for this
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species was poorly known (Hemami et al. 2007). Therefore, we provide some initial calibration of
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thermal imaging density estimates for this species against results obtained from a drive survey.
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Distance sampling analysis is widely used in wildlife biology, but often without considering
how detectability may vary among habitats; this can bias density estimates and reduce their
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precision (Marques & Buckland 2003; Marques, Thomas, Fancy & Buckland 2007). For deer in
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concealing forest habitats, detection probability and effective strip width (ESW) can vary among
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habitats or forest growth stages that differ in vegetation density (Focardi, Isotti, Pelliccioni &
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Iannuzzo 2002a; Gill et al. 1997; Hemami et al. 2007). Applying a single detection rate can
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overestimate detectability and under-estimate numbers within the concealing habitats; magnitude
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and direction of error will be greater if animals select or avoid these concealing habitats. Hemami et
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al. (2007) suggested the precision of density estimates may be increased through stratification by
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habitat, but lacked sufficient observations for species-specific analyses. In a Mediterranean mixed
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oak forest (40 km2) in Italy, densities estimated using distance sampling with habitat-specific
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detection were 10% higher for roe deer and 18% lower for wild boar, than without habitat
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stratification (Focardi et al. 2002a). However, a large sample size of > 60 groups (see Buckland,
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Anderson, Burnham, Laake, Borchers et al. 2001) from each habitat strata is needed to calculate a
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robust detection function. A further limitation to habitat stratification during analysis in Distance
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software is that it precludes simultaneous stratification by an additional factor such as geographical
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unit. This may be required for landscape-scale assessments - particularly where abundance is
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spatially uneven due to varying management pressure (Wäber, Spencer & Dolman 2013). Here we
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examine whether directly incorporating habitat density as a covariate of detectability (Buckland,
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Anderson, Burnham, Laake, Borchers et al. 2004; Marques et al. 2007) improved the precision of
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density estimates in heterogeneous forest landscapes.
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We conducted thermal imaging distance surveys of two deer species (Reeves’ muntjac and roe
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deer) annually for three years, obtaining a large sample size that allowed us to examine whether
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different structures of distance models affected abundance estimates or precision. We examined an
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extensive (123 km2) forested landscape in Eastern England, where forest blocks (mean = 18.8 km2 ±
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11.1 SD, n = 7) differ in representation of growth stages, soil types and thus understorey density, and
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the relative abundance of deer species (Hemami, Watkinson & Dolman 2005b; Wäber et al. 2013).
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We investigated whether controlling for differences in sampling effort among forest blocks, in deer
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density amongst forest blocks, or habitat-specific detectability, affected the magnitude and precision
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of estimated deer numbers. Our aims were to:
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(1) compare muntjac density estimates from distance sampling thermal imaging transects to those
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obtained from independent drive counts,
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(2) quantify behaviour of detected deer groups as an indication of likely movement and
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measurement error,
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(3) use a large sample of driven transects (total 1567 km) and deer observations to compare
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detection functions, ESWs and the precision and magnitude of landscape-scale density estimates
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between distance models that: i) ignore potential differences in habitat-specific detectability with
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uneven sampling effort and ii) with even sampling effort, iii) control for differences in sampling effort
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or density among forest blocks, iv) examine potential differences in detectability among habitats; v)
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incorporate habitat as a covariate in the detection function to account for potential differences in
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detectability among habitats, while also accounting for differences in sampling effort and density
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among forest blocks and vi) account for potential movement prior detection.
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Materials
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Study area
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The study was conducted in Thetford Forest (total area 195 km2), an afforested landscape
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planted in Eastern England (52°30´N, 0°60´W) during the early 20th Century. The forest is divided into
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twelve deer management blocks (mean area = 15.6 km2 ± 10.2 SD, range 4.5 km2 to 34.7 km2)
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separated by roads. Reeves’ muntjac and roe deer were abundant throughout the study area.
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Surveys of muntjac and roe were conducted in late winter, annually over three years (2008-10) in
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seven forest blocks (aggregate area 123 km2) (see Appendix A: Fig. A.1), with four blocks (mean area
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= 26.5 km2 ± 7.9 SD) located in the contiguous core of the forest landscape with farmland abutting
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only a small proportion of the forest perimeter, and three outlying blocks (mean area = 8.7 km2 ± 1.4
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SD, n = 3) each surrounded by farmland. In 2007 a sub-set of four blocks was sampled but not
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considered for comparisons of distance models.
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At the time of the study, Thetford Forest was conifer-dominated with Corsican (Pinus nigra
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var. maritima) and Scots pine (Pinus sylvestris) comprising 67% of the forest area; other conifers 4%;
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deciduous tree species 10% and open areas 19%. Conifer crops are managed by rotational clear
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felling that provided a mosaic of even-aged ‘coupes’ (mean area = 9.0 ha ± 8.6 SD) (Pedley, Franco,
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Pankhurst & Dolman 2013). A network of linear forest trackways that allow management access,
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subdivide coupes into rectangular ‘sub-compartments’ (mean area = 3.2 ha ± 3.3 SD) (Eycott,
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Watkinson & Dolman 2006). Sub-compartments provided the unit of replication for this study. The
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trackway network was closed to public access, is rarely driven and was not used at night by any
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vehicles other than by our fieldwork. Trackways were categorised into three ordinal classes
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comprising: fire-routes (median verge width = 3 m, range = 1-9 m, n = 321), forest trackways (median
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verge width = 6 m, range = 1-45 m, n = 459) and narrow forest paths (median verge width = 0.6 m,
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range = 0.1-1 m, n = 205) (see Appendix C). Rates of diurnal vehicle use and recreational activity
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(with potential for disturbance and residual influence on nocturnal deer distribution) differed among
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trackway classes (ranked highest in fire-routes and lowest in forest paths).
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Five growth stages of conifer plantation were classified following Hemami, Watkinson and
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Dolman (2004) and Hemami et al. (2005b) for distance analysis were pooled into three a priori
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habitat classes based on vegetation density, detectability and visibility of deer as: (1) open habitat ,
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(2) dense and (3) mature habitats (sampling effort across habitats: muntjac 15-17%; roe 20-22%)
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(see Appendix A: Fig. A.1, Appendix C). The seven forest blocks differed in the proportion
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contributed by each growth stage, though most were dominated by mature habitats (mean = 68.6%
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± 8.2 SD, range: 50-83%), with an intermediate extent of dense (mean = 23.7% ± 8.4 SD, 13-45%) and
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limited extent of open habitats (mean = 7.8% ± 2.1 SD, 5-12%) (Appendix D: Table D.1).
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Drive counts
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Drive counts were conducted in two forest blocks (Cro: 7.1 km2; and Har: 9.8 km2) on 31
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March and 01 April 2007 using 60-80 volunteers. Within each block, replicate individual even-aged
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stands were sampled on separate drives –practicable due to the trackway network that divides the
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forest into discrete units bounded by clear sight-lines. In each block, drives were conducted in
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replicate coupes of pre-thicket, thicket, pole and mature growth stages, selected randomly (see
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Appendix B).
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For each block mean muntjac density was estimated as the area-weighted mean of habitat-
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specific estimates (from replicate drives), following the method of Greenwood and Robinson (2006)
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and compared to estimates from thermal imaging distance transects in the same blocks in February
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2007. To provide robust detection functions, thermal imaging data collected from that block over
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the four winters (2007-10) were analysed, stratifying encounter rate by year. Density estimates for
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2007 were compared between the two methods by Z-tests.
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Distance transect thermal imaging surveys
Nocturnal distance sampling was conducted using thermal imaging equipment during 20.00
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hrs-04.00 hrs between January and March in three consecutive years (2008-10). Sampling was
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conducted in late winter as thermal contrast is greater in cold weather and vegetation had died
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back. In each surveyed block, transect routes along forest trackways covered every sub-
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compartment, viewing along tree-planting rows (Appendix A: Fig. A.1). One-sided transects
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(sampling fraction = 0.5) were conducted from the front passenger seat of a four-wheel drive
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vehicle, driven with headlights off at a maximum speed of 16 km.hr-1. Transects were sub-divided
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into sections according to the adjacent forest sub-compartment, to allow data stratification by crop
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age. Block area and transect length varied among forest blocks, with a mean transect density of 5.5
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km/km2 per block ± 3.0 SD, and a total length of driven transects across surveyed blocks of 529.4 km
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per year ± 40.3 SD (Table D.1). In each winter, transect routes in five forest blocks were repeated at
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least twice, with transects in the remaining two blocks surveyed once.
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Within forest landscapes, for deer distance surveys using vehicles are inevitably constrained to
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use trackways (Gill et al. 1997). However, placement of transects on a trackway network potentially
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violates assumptions of distance analysis, particularly random placement with respect to the
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population being measured (Buckland et al. 2001). In this study, although not randomly located,
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trackways were evenly distributed across the forest and a similar proportion of each habitat was
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sampled for each species (see Appendix C).
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The distance to each observed deer was measured using a Leica Laser Range Finder ‘LRF 800’
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rigidly mounted on a night vision ‘Maxi-Kite Mk 4’ (THALES optics, St. Asaph, UK) fitted with an
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infrared illuminator. The laser was sighted in with the recticle of the night vision equipment by a
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gunsmith (see Appendix C). The bearing relative to the transect centre line was measured using an
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angle board to an accuracy of 5°. If detected animals move prior to distance measurement, then
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density estimates may be underestimated by an unknown amount (Buckland et al. 2001). Therefore,
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the behaviour of deer when first seen (lying, standing, or walking / running away from observer) was
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recorded. Care was taken not to double count animals which moved ahead of the vehicle. All survey
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work was carried out by a single observer (KW).
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Analysis
Sighting distance xd and angle αd were used to calculate perpendicular distance yp as sin(αd) xd.
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As verge-width and thus the distance from the observer to the edge of the planted forest coupe
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differed between trackway classes, perpendicular distances were left-truncated according to
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transect section-specific verge width, measured once for each section during diurnal fieldwork.
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Perpendicular distance at which the animal was detected (square root transformed, after 5%
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right truncation of detected animals) (Buckland et al. 2001) was related to behaviour (categorical:
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walking/running/lying or standing) and trackway classes, controlling for habitat and the potential
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interaction between trackway classes and behaviour, using Generalised linear models (GLMs) with
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normal error distribution.
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Distance data were analysed using Distance 6.0 release 2 (Thomas, Buckland, Rexstad, Laake,
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Strindberg et al. 2010). Prior to analysis, following Buckland et al. (2001) and Marques et al. (2007)
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observations were right-truncated with 5% of the most distal observations discarded if this improved
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the fit of the detection function (Kolmogorov-Smirnov test), following Buckland et al. (2001).
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For each data set the models with the lowest values of Akaike’s information criterion (AIC)
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were selected; when alternative models differed by less than 2 AIC units then that with the best
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goodness-of-fit (q-q plots) was chosen.
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We examined whether recorded group size was affected by detection distance (after 5% right
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truncation), using species-specific GLMs with Poisson error. Where there was no distance bias (P >>
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0.1) the overall mean observed group size for that species was used in density estimation without
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adjustment (Table 2, see Appendix C).
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Distance analyses were performed separately for each year. For each year of analysis, five
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different analytical models were explored, to examine whether any discrepancy among results from
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different models were consistent across year replicates. Model structures are summarised in Table
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1, and are characterised as follows:
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1a) ‘Basic Design’ ignored potential differences in detectability among habitats, or blocks, and also
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ignored potential differences in deer density and survey effort among blocks (mean = 4.3 km/km2
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across blocks ± 0.4 SD).
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1b) ‘Basic Design Single’ as 1a) but with single survey effort (mean = 2.3 km/km2 across blocks ± 0.05
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SD).
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2) ‘Forest Block’, again ignored block- or habitat-specific differences in detectability, but controlled
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for potential differences in sampling effort and deer density among blocks by estimating annual
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forest-wide density (DF) as the area-weighted mean of block-specific densities (Db). Insufficient
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observations were obtained in each block in each year, to allow block-year specific detection
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functions to be fitted to data pooled across the three years.
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3) ‘Forest Block Grouped 30 m’ grouped data in 30 m bins to account for potential movement prior
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to detection and examine whether this affected density estimates. The design again ignored block-
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or habitat-specific differences in detectability, but controlled for potential differences in sampling
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effort and deer density among blocks by estimating annual forest-wide density (DF) as the area
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weighted mean of block-specific densities (Db).
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4) ‘Habitat Covariate’ included habitat (categorical, three levels: open, dense, mature) as a covariate
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in the detection function, to account for potential differences in detectability among habitats, and
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thus also for any bias due to differences in growth stage composition among forest blocks. Again, DF
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was estimated as the area-weighted mean of all Db, accounting for differences in stratified estimates
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of block-specific density.
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To quantify potential differences in detectability among habitat classes (open, dense and
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mature), and thus assist interpretation of differences in results from contrasting models, we also
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explored one further analysis design:
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5) `Habitat Stratified`, with detection function stratified by transect-section-specific habitat class. For
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this it was necessary to pool data across years in order to obtain robust habitat-specific estimates of
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ESW, as insufficient observations were obtained for habitat-year-specific detection functions to be
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fitted. Forest-wide density (DF) (pooling across years) was again estimated as the area-weighted
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mean of habitat-specific densities (Dh).
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Forest-wide annual estimates of ESW, density and precision (CV of density estimate calculated
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by Distance software) were compared across designs (factor) whilst controlling for deer species and
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years, in GLMs with normal error. Habitat-specific detection probabilities, ESWs and densities were
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compared using Z-tests following Buckland et al. (2001):
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𝑧=
𝑑̅𝐻1 − ̅𝑑𝐻2
2
2
√(𝑆𝐸 (𝑑̅𝐻1 )) +(𝑆𝐸(𝑑̅𝐻2 ))
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where 𝑑̅𝐻 is the habitat-specific detection probability, ESW or density estimate for the habitat
H; and SE(𝑑̅𝐻 ) the standard error of this estimate.
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Results
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Comparisons of estimated muntjac density from drive counts and distance sampling
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During drive counts 102 muntjac were observed across two forest blocks in 2007. Distance
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surveys across these blocks in 2007 provided 165 muntjac observations comprising 225 individuals.
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Detection functions were fitted for each block, pooling block-specific observations across 2007-10
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(Cro: 364 individuals in 318 groups; Har: 723 individuals in 600 groups). Resulting estimates of
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muntjac density in 2007 were similar between methods, for both blocks (Fig. 1). Distance sampling
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achieved greater sampling intensity (Cro: 7.9 km transects driven/km2, accounting for ESW, 33% of
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block searched; Har: 7.5 km/km2, 30%) for less effort (eight people-nights) than drive counts (140
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people-days; Cro: 14% of block searched; Har: 6%).
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Annual distance sampling conducted 2008-2010
During distance transect sampling in 2008-2010, a total of 1725 (2008: n = 645; 2009: 587;
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2010: 493) roe deer in 921 groups and 2264 (2008: 960; 2009: 751; 2010: 553) muntjac in 1926
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groups were detected.
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Potential trackway avoidance and potential behavioural responses to observer
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For roe deer but not for muntjac, we found slightly fewer animals in the immediate proximity
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of the transect line (≤ 30 m) irrespective of trackway class (Fig. 2). Most roe (42%) were detected
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within 30-60 m (detection probability > 1.0) (Fig. 2), suggesting redistribution away from the
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immediate vicinity of either the trackway or the forest edge. When first detected, most deer groups
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in 2008-2010 were either stationary (roe: 80.1%; muntjac: 73.3%) or walking (roe 17.8%; muntjac
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24.8%); few were running (roe 2.0%; muntjac 1.9%). Controlling for habitat and behaviour, the
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perpendicular distance at which deer were detected did not differ among trackway types for either
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species (Table 3). That perpendicular distances did not differ between regularly driven trackways and
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those driven so infrequently they merely constituted forest edges or canopy breaks, suggests roe
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deer may have been avoiding the immediate edge of the forest crop, perhaps seeking thermal
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shelter. Perpendicular distance did not differ amongst behaviours for roe (running: n = 17, 41.8 m ±
340
7.1 SE, lying: 242, 42.4 m ± 2.1 SE, walking: 150, 45.1 m ± 2.5 SE, standing: 439, 46.6 m ± 1.8 SE)
341
(Table 3). For muntjac, although differing significantly (Table 3) mean distances were nevertheless
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similar among behaviours (lying: n = 525, mean = 35.2 m ± 1.2 SE, running: 40, 37.9 m ± 4.0 SE,
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walking: 518, 38.7 m ± 1.2 SE, standing: 1002, 41.4 m ± 0.9 SE). Furthermore, for both roe and
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muntjac many individuals or groups that were walking were moving parallel to, not away from, the
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trackway suggesting movement was not primarily in response to the vehicle. It is possible that deer
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may be moving for other reasons, transiting between foraging and lying up locations or interacting.
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Overall, perpendicular distances appeared to reflect the original distribution rather than movement
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away from the observer.
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Habitat, density and variance estimates
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Detection functions and goodness of fit statistics are reported in Appendix D (Table D.2). The
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potential redistribution of roe from ≤ 30 m to 30-60 m lay within perpendicular distances for which
353
probability of detection was predicted to be >1.0; thus ESW and density estimates are expected to
14
354
be unaffected by localised edge avoidance. Analysis repeated with data binned to 30 m resulted in
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higher CV but did not affect ESW or density.
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Across all designs and years the ESW for roe deer was 27% ± 0.04 SD greater than that for
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muntjac (F1,22 = 383.70, P < 0.001). For both species, the ESW was similar among distance models
358
‘Basic Design’, ‘Basic Design Single’, ‘Forest Block’ and ‘Habitat Covariate’ whilst controlling for year
359
(Fig. 3, Table 3).
360
However, when pooling observations across years to stratify detection functions by habitat
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(Table 1, model 5) the ESW was wider in open habitat (roe: n = 173; muntjac: n = 178); than in either
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mature (roe: 31% less, Z = -4.22, P < 0.001; muntjac: 27% less, Z = -3.70, P < 0.01), or dense habitats
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(roe: 45% less Z = -5.45, P < 0.001; muntjac 46% less, Z = -6.08, P < 0.001) (Fig. 3), reflecting the
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significant differences in perpendicular distances among habitat. In this model design, the detection
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probability was greatest in open habitats (muntjac: detection probability = 0.79 ± 0.06 SD; roe: 0.78
366
± 0.06 SD), lower in mature (muntjac: 0.57 ± 0.02 SD; roe: 0.54 ± 0.02 SD) and lowest in dense
367
(muntjac: 0.42 ± 0.02 SD; roe: 0.42 ± 0.04 SD) habitats (muntjac, 3 Z-tests, all P < 0.01; roe, all P <
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0.01). For both species, most groups were detected in mature habitats (muntjac 72% of all groups;
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roe 59%) (Appendix D: Table D.2) reflecting the predominance of these habitats.
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Pooling across years (Table 1, model 5), habitat-specific density of roe deer was similar in
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mature (n= 522 observed, mean = 13.3 km-2 ± 1.1 SE) and open habitats (173, mean = 16.2 km-2 ± 2.1
372
SE, Z = -1.22, P = 0.11) but lower in dense habitats (128, 11.1 km-2 ± 1.4 SE, compared to open Z = -
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2.02, P = 0.02, compared to mature Z = -1.18; P = 0.12). For muntjac, habitat-specific density was
374
greatest in mature (1158, 23.8 km-2 ± 1.5 SE) and dense (303, 23.0 km2 ± 2.4 SE, Z = -0.28; P = 0.38)
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and lowest in open habitats (178, 14.2 km-2 ± 1.7 SE, compared to dense Z = -2.99, P = 0.001,
376
compared to mature Z = -4.23, P = 0.001) (Appendix E: Fig. E.1).
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For muntjac landscape-scale density estimated using ‘Basic design’ was significantly higher
378
than estimates from all other analytical designs, which gave similar results (controlling for survey
379
year, Table 3) (Fig. 4). Averaging across survey years, this discrepancy was 14%. Roe landscape-scale
15
380
density estimates did not differ among designs (Table 3). Landscape-scale muntjac density declined
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from 2008 to 2010 whilst for roe deer temporal patterns and annual landscape-scale density
382
estimates were similar (Fig. 4). The magnitude of variation (CV) in density among forest blocks was
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greater than variation among habitats for muntjac (forest blocks: n = 7, CV = 54%, habitat: 3, CV =
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26%) and roe (forest blocks: 7, CV = 74%, habitat: 3, CV = 19%) (Appendix E: Fig. E.1).
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The precision of year-specific forest-wide density estimates was examined in terms of the CV.
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For muntjac precision increased from ‘Basic Design Single’, ‘Forest Block Grouped 30 m’, ‘Basic
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Design’ to ‘Forest Block’ design, by 41% based on the means across years for muntjac and from
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‘Basic Design Single’, ‘Basic Design’, ‘Forest Block’ to ‘Forest Block Grouped 30 m’ by 37% for roe.
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Precision was further improved in the ‘Habitat Covariate’ model (by 4% for muntjac and 6% for roe).
390
The improvement in precision (CV) of year-specific forest-wide density estimates from basic to more
391
complex model designs was significant for both species (GLM controlling for year, Table 3). For
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annual population estimates the best model (‘Habitat Covariate’) achieved a precision (CV) for
393
muntjac of 5.3-7.5% and for roe deer of 8.8-12.6% in 2008-10 (Appendix D: Table D.2).
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Discussion
We estimated density of muntjac and roe deer using an exceptionally large set of distance
398
sampling thermal imaging data comprising more than 3000 observed deer groups at landscape-scale.
399
For muntjac, similar density estimates obtained by distance sampling and independent drive surveys
400
lends further reassurance that distance sampling is appropriate, as previously found for independent
401
comparisons of distance sampling with faecal pellet counts for roe deer (Hemami et al. 2007) and
402
capture mark recapture for wild boar (Franzetti et al. 2012). Given the potential effects of
403
movement, it is important to record behaviour during distance sampling to allow any response to
404
the observer to be examined. In the present study a key assumption - that measures of distance
16
405
were not biased by a response to the observer and distances reflect their original location – appears
406
to have been met.
407
Distance analysis controlling for heterogeneity in detectability and encounter rate among
408
habitats or forest blocks tended to give lower density estimates than simpler analytical designs. For
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muntjac density estimates from the ‘Basic Design’ (with uneven sampling intensity among habitats
410
and blocks) were significantly higher than all other models. However, estimated landscape-scale
411
muntjac densities did not differ significantly among the remaining analytical models. For roe deer,
412
differences among analytical designs were not significant. Overall, mean densities estimated by
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distance sampling thermal imaging were broadly robust to forest composition and analysis design.
414
We had expected density estimates would differ more substantially among analytical designs.
415
However, as the majority of the observations for both species (muntjac: 72%; roe: 59%) were in the
416
most widespread habitat (mature), with an ESW intermediate between open and dense habitats, the
417
estimation process was greatly influenced by these data. We would expect greater influence of
418
analytical design if deer density were greater in less widespread habitats, or if the ESW was extreme
419
in the most widespread habitat and recommend that in such circumstances simple analytical designs
420
should not be used. In our study area, variation in density of both species was greater amongst
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forest blocks than amongst habitats, contributing to the similarity in estimated density among
422
analytical designs.
423
Although density estimates were broadly similar across models, block and covariate designs
424
provided greater precision for both species. Similarly, Marques et al. (2007) showed covariate
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distance sampling improved precision of avian density estimates. As detection distances differed
426
among habitats, including habitat as a covariate in the modelling process improved precision
427
without the need to estimate separate habitat-specific detection functions - that can be problematic
428
depending on the number of observations obtained (Hemami et al. 2007; Marques et al. 2007).
429
Within our research area the discrete planting of even-aged growth stages of different detectability
430
character made habitat characterisation simple; distinctions between contrasting forest structures
17
431
may be more gradual in other landscapes. Considering habitat as a detectability covariate also
432
enabled stratification of density by block and year, to establish area-weighted forest-wide mean
433
density for each year of survey.
434
We have shown that distance sampling thermal imaging was reasonably robust to different
435
analysis designs, but that accounting for differences in detectability among forest habitats provided
436
lower and more precise (narrower CI) estimates. Particularly for the smaller and more abundant
437
muntjac the mean density with narrow CI (‘Habitat Covariate’: CV 5.3-7.5% in 2008-10) is a good
438
basis for evidenced-based management. For roe deer to base management decisions on mean
439
densities (‘Habitat Covariate’: CV 8.8-12.6% in 2008-10), estimates may need to be pooled across
440
several years to obtain more precise Cis (Fig. 4). Distance sampling thermal imaging is appropriate
441
where the extent of the surveyed area is much greater than that of individual home ranges, so that
442
nocturnal and foraging movement do not confound results. However, the method may be less
443
practical for small woodlands (< 300 ha) (Gill et al. 1997) unless co-operative surveys across a wider
444
landscape can be achieved, covering multiple woodland fragments and intervening farmland. More
445
research is needed to investigate the performance of distance sampling thermal imaging across a
446
range of landscapes, but the current study supports its use in coniferous plantation forest as a
447
robust tool for estimating deer numbers as a basis for management.
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450
Acknowledgements
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455
456
457
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This study was funded by the University East Anglia and the Forestry Commission England with
support from the Deer Initiative, Defence Estates and the Saint Hubert Club of Great Britain. We are
grateful to the wildlife ranger team of Thetford Forest; T. Banham, B. Ball, D. Gunn, S. Hetherington,
P. Mason, T. Parr and J. Upson and also B. Monckton of the Defence Estates, for advice and
assistance to this study. We thank all volunteers from the University of East Anglia, Saint Hubert Club
of Great Britain, and the British Deer Society who helped during the drive counts and distance
sampling. We are grateful to Klaus Hövemeyer, Stefano Focardi and Alastair Ward and one
anonymous reviewer for perceptive and helpful comments on an earlier version of this paper.
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18
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Appendix A - E. Supplementary data
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Supplementary data associated with this article can be found, in the online version, at XXXXX.
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463
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