Population Size, Distribution and Status of the Remote and Critically Endangered Bawean Deer Axis Kuhlii

Population size, distribution and status of the remote and Critically Endangered Bawean deer Axis kuhlii

D EDE A ULIA R AHMAN,GEORGES G ONZALEZ and S TÉPHANE A ULAGNIER

Abstract Conservation of rare ungulates requires reliable as Critically Endangered on the IUCN Red List (Semiadi population size estimates and distribution maps for priori- et al., ). The Bawean deer is reported to range over a tizing investments and assessing the effectiveness of conser- very small area restricted to the Bawean Island Nature vation measures. We used both camera trapping and a Reserve and Wildlife Sanctuary and a peninsula on the random encounter model approach, and faecal pellet north-west side of the island (Tanjung Cina; Lachenmeier group counts, to update the range and population size of & Melisch, ; Grubb, ). The protected area is rela- the Bawean deer Axis kuhlii in the Bawean Island Nature tively close to human settlements, and illegal logging is Reserve and Wildlife Sanctuary, Indonesia. We studied not uncommon in the forest habitat. Listed in Appendix I -month periods to fulfil the assumption of population of CITES (), this taxon is legally protected and is one closure. Both methods provided similar population density of  species prioritized for conservation by the estimates (higher in the dry season) of c. – indivi- Indonesian government on the basis of their threatened sta- duals. The estimated range of the species is significantly nar- tus (decree SK./IV-KKH/; Ministry of Environment rower than previously reported. The main threats (habitat and Forestry, ). Despite this status, and threats across its loss as a result of illegal logging, and disturbance by dogs range, surprisingly little is known about the Bawean deer and hunters) are ongoing. Based on these results we suggest and no long-term monitoring has been implemented, partly that the species should retain its Critically Endangered sta- because this is not a charismatic species. tus on the IUCN Red List. Several methods have previously been used to study population trends and distribution in this species: faecal Keywords Axis kuhlii, Bawean deer, camera trapping, sampling (Blouch & Atmosoedirdjo, ; Blouch, ; Cervidae, conservation, faecal pellet count, random encoun- LIPI & IPB, ; Semiadi, ; BBKSDA East Java, ter model ), footprint (UGM & BBKSDA East Java, ) and To view supplementary material for this article, please visit call counts (BBKSDA East Java, ), and camera trap sur- http://dx.doi.org/./S. veys (UGM & BBKSDA East Java, ). The latter study, in which  camera traps were installed at seven locations (Lang Pelem river, Lampeci river, Tambelang river, Mt Tinggi, Angsana block, Tanjung Putri block and Tanjung Cina) during  days, recorded no evidence of the Bawean Introduction deer, although this may be attributable to the short duration of the study and the placement of cameras in unsuitable eliable information on population size and range, and locations. Rany trends in these parameters, is required to assess the Capture–recapture methods for estimating population conservation status of a species using the Red List criteria size require individuals to be recognizable, either by rings  (IUCN, ). In the absence of such information, conser- or collars (e.g. Trolle et al., ; Oliveira-Santos et al., vation management is often based on crude estimates, ) or by natural marks such as stripes, spots or scars expert opinion or educated guesses, which may result in (e.g. Kumbhar et al., ). They are not applicable to the erroneous decisions that can be counter-productive many mammal species that lack distinctive marking, such (Akcakaya,̹ ; Blake & Hedges, ; Murray et al.,   as the Bawean deer, except when bucks are seasonally ). The Bawean deer Axis kuhlii (Temminck, ), antlered. the most isolated deer in the world and the only endemic The development of the random encounter model, a deer species in Indonesian tropical rainforest, is categorized by-product of an ideal gas model (Hutchinson & Waser, ), has facilitated estimations of species densities

DEDE AULIA RAHMAN* (Corresponding author), GEORGES GONZALEZ and STÉPHANE from unmarked individuals with a known speed, and sen- AULAGNIER Comportement et Ecologie de la Faune Sauvage, Institut National de sor detection parameters (Rowcliﬀeetal.,). The ran- la Recherche Agronomique, CS 52627, 31326 Castanet-Tolosan Cedex, France E-mail [email protected] dom encounter model has been implemented successfully for ungulate species by deploying cameras in systematic or *Also at: Faculty of Forestry, Department of Forest Resources Conservation and  Ecotourism, Bogor Agricultural University, Bogor, Indonesia fully randomized arrays (Rowcliffe et al., ; Rovero &   Received  February . Revision requested  March . Marshall, ;Zeroetal., ; Carbajal-Borges et al., Accepted  April . First published online  October . ).

Oryx, 2017, 51(4), 665–672 © 2016 Fauna & Flora International doi:10.1017/S0030605316000429 Downloaded from https://www.cambridge.org/core. IP address: 170.106.33.22, on 27 Sep 2021 at 19:38:25, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0030605316000429 666 D. A. Rahman et al.

FIG. 1 Camera trap grid and camera locations in the Bawean Island Nature Reserve and Wildlife Sanctuary, Indonesia.

In this study we used two methods to estimate the abun- are confined to the steep sides and tops of the higher dance and map the range of the Bawean deer in the Bawean hills and mountains, often occurring as islands surrounded Island Nature Reserve and Wildlife Sanctuary, and assess its by teak (Semiadi, ). IUCN status. Density was estimated using both the random encounter model with camera trapping data and faecal pellet group counts; the latter technique was the most com- Methods monly used in previous studies. Camera trapping

The Bawean Island Nature Reserve and Wildlife Sanctuary Study area  was divided into  -km grid cells using the geographical  Indonesia’s Bawean Island ( km ) is relatively isolated information system ArcGIS .. (ESRI, Redlands, USA). in the Java Sea (Fig. ). Based on the classification of Smith The camera trapping survey was conducted during both and Ferguson, its climate is categorized as type C (Semiadi, the wet (March–April and November ) and dry seasons ). Rainfall is abundant during the north-west mon- (May–October ). Twenty Trophy Cam HD Max digital soon, from the end of October until April, and reaches cameras (Bushnell Outdoor Products, Overland Park, USA) c. , mm on the southern coast. Temperature is almost operating on passive infrared motion sensors were installed uniform throughout the year, with mean maximum and – cm above the ground, perpendicular to the ground, to minimum temperatures of  and °C, respectively record both small and large animals. Herbaceous vegetation  (Semiadi, ). The study area encompasses . km in the vicinity of the cameras was cleared to avoid interfer- of the Bawean Island Nature Reserve and Wildlife ence (Tobler et al., ; Team Network, ; Rovero et al., Sanctuary, which is characterized by steep topography ). The cameras were set at  minute video mode with  (with slopes . °) and a wide altitudinal gradient (up minute intervals. The total survey effort was , trap days. to  m). The main vegetation type is evergreen tropical Sampling precision was assessed as the coefficient of vari- forest, which covers % of the island. A mosaic of closed ation of trap rates with cumulative trapping effort (cameras and open forest as well as permanently dry and seasonally × days). The sampling precision for the Bawean deer flooded habitat types occur in the study area, including gal- increased up to an optimum trapping effort of – cam- lery forest, semi-deciduous forest with understorey, shrub era days (Fig. ). One camera trap per grid cell was deployed, and grassland, and teak Tectona grandis plantations (% in open and accessible locations, applying a buffer equiva- of the area), which are all globally threatened by deforest- lent to half of the mean maximum distance moved, (/ ation and climate change. The remaining natural forests MMDM) to reduce the likelihood of capturing the same

(deposition time of faecal groups). This method is appropri- ate for rapid surveys and when it is quite difficult to find a new group of faecal pellets in the field (Prugh & Krebs, ; St-Laurent & Ferron, ).

Random encounter model

The random encounter model uses the rate of contact between moving animals and static camera traps to estimate species density. It requires estimation of species-specific camera trap detection (Carbone et al., ), along with a FIG. 2 Camera trap sampling precision expressed as the camera trap detection speciﬁed by radius and angle, and coefficient of variation of Bawean deer trapping rates with an estimated day range based on speed of movement and ac- cumulative trap effort (number of cameras × trapping days). tivity data (Rowcliffe et al., , ). The model is based on three main assumptions: () animals conform adequately individual twice (Karanth & Nichols, ; Soisalo & to the model used to describe the detection process (i.e. they Cavalcanti, ). behave like particles of an ideal gas, moving randomly and Before installation we collected evidence of the presence independently of one another), () photographs represent of the Bawean deer (footprints, faeces, food remains, antler independent contacts between animals and cameras, and rubbing on trees) throughout the grid in the Bawean Island () the population is closed (Rowcliffe et al., ). To fulfil Nature Reserve and Wildlife Sanctuary and also in Tanjung these assumptions, camera traps were set at least  m Cina. We selected camera locations using the two following apart to reduce the likelihood of capturing the same individ- procedures. Firstly, we superimposed a grid of  ×  m ual twice, and increase independence of locations (Kays cells over the study area using the Fishnet and Clip tools in et al., ). We studied -month periods in the dry season ArcGIS .. (ESRI, Redlands, USA). This generated , (June–July and August–September) on the basis that in such potential camera locations from which we randomly short sampling times the probability of birth, death, migra-  selected one for each  km grid cell in the field. In general, tion or immigration events is low (Karanth & Nichols, ; cameras were not moved during the study; however, if a Silver et al., ; Soria-Dίaz & Monroy-Vilchis, ). camera did not capture any mammal in – periods of We used the following equation to obtain density esti- checking we moved it – m from the original location mates from camera trap encounter rates (Rowcliffe et al., within the same grid cell. Secondly, we selected locations ): where we found evidence of the presence of the Bawean y p deer. In practice, few camera traps were placed preferentially gD = t vr(2 + u) in this way (only  locations), as signs of the deer were difficult to find in the field. Moreover these cameras, which where y/t = trapping rate (number of independent photographic events per camera trap day), v = a species’ mean were expected to confirm the efficacy of the equipment, − did not take significantly more photographs than randomly daily speed of movement (km day ), r = radius of the cam- θ set cameras. In total,  locations were sampled during the era trap detection zone (km) and = angle of the camera survey period. Cameras were checked once every – days, trap detection zone (radians). The outcome can then be and batteries and memory cards were replaced as necessary. multiplied by g (mean group size), as the independent Malfunctioning cameras were replaced to avoid loss of data. unit recorded by the camera is the group rather than the individual (Rowcliffe et al., ; Zero et al., ). Independent photographic events were defined as indivi- Faecal pellet group count duals entering and exiting the field of view (Cusack et al., ). An individual’s mean daily speed of movement was Within each grid cell we counted faecal pellet groups in four calculated as the speed recorded from camera trap videos plots ( ×  m) around the camera trap, spaced  m apart, multiplied by the proportion of time spent active (Rowcliffe according to Acevedo et al. () and Alves et al. (). A et al., ). The radius of the camera trap detection zone was total of  square plots ( ×  camera trap locations) were calculated by measuring the distance from the camera to the surveyed during the wet (February–March ) and dry location of deer at the first trigger, based upon marked loca- seasons (August–September ). After the initial removal tions in the field. The angle of the camera trap detection zone of all pellets present in each plot we calculated the faecal ac- was obtained in the field by detecting a stick in six paired cumulation rate by recording the monthly deposition of pel- approaches perpendicular to the sensor beam at a distance lets after the initial removal of all pellets present in the plot of  m and using a compass placed on a flat surface directly

below the camera. The angle of maximal detection was then converted to radians for calculations. We used mean values of r and θ from all cameras in the calculations. The mean group

size for this mostly solitary species was influenced mainly by 10.07) – 0.0003 0.0032

does and their young. 0.0484 ± ± 0.05 Sep. (20 cameras) ± 469 – ± – ), for preferentially set  Faecal accumulation rate Fig.  Population density (D, individuals per km ) was estimated using the equation proposed by Eberhardt & Van Etten (): D = (NP × Dpg)/(T ×dR), where NP = number of 5.56) 8.92 (7.77

 – plots per km , Dpg = mean number of faecal pellet groups, 0.00030.0054 0.0074 0.3379 0.0484 1.425 ± ± 0.07 1.17 ± July (20 cameras) Aug. 259 362 ± T = deposition time of faecal pellet groups, and dR = defeca- – tion rate. In the absence of field data we used the observed – defecation rate of captive Bawean deer ( faecal pellet groups per individual per day; Blouch & Atmosoedirdjo, ). We extrapolated the calculated densities to the total protected area on Bawean island to estimate the population 9.42) 4.87 (4.18

size. The standard error of the estimates was computed – 0.00040.0037 0.0082 0.3366 0.0484 1.425

 ± ±

using the delta method (Seber, ). 0.06 1.24 Sep. (11 cameras) June ± 439 195 – ± –

Distribution

We mapped the distribution and abundance based on the

random encounter model and the faecal pellet count (map in the Bawean Island Nature Reserve and Wildlife Sanctuary (

source: Bakosurtanal, ) using ArcGIS. 5.87) 8.12 (6.82 – 0.00050.0070 0.0072 0.3266 0.0484 1.425 ± ± 0.10 1.11 ± July (12 cameras) Aug. 274 318 ± – Axis kuhlii Results –

Random encounter model

We recorded  photographs of Bawean deer (. indivi-

duals per  trap-days), none during March–April,  in 11.37) 4.88 (3.89 – 0.00040.0044 0.0081 0.3332

 0.0484 1.425 ± ±

May (but only two independent photographic events), 0.07 1.24 Sep. (9 cameras) June ± 530 181 –     ± in June, in July, in August, in September, in – October and  in November. Random encounter modelling was performed bimonthly for June–July and August– September, with  and  independent photographic events, respectively. All variables and estimates for preferen-

tially set, randomly set and all cameras are summarized in 5.86) 9.80 (8.23 – 0.00050.0088 0.0076 0.3462

 0.0484 1.425

Table . Obtaining similar estimates for both sets of cameras, ± ± 0.11 1.24 ± July (8 cameras) Aug. 273 384 ±   ±   –

estimations for all cameras were . SE . individuals –  –   ±   –

per km in June July and . SE . in August Preferentially set camerasJune Randomly set cameras All cameras September, yielding population estimates of  ± SE  and  ± SE , respectively.

Faecal accumulation rate ) 1.425 1 We counted  and  faecal pellet groups after  days of − accumulation in the wet and dry seasons, respectively. We  estimated a density of . ± SE . individuals per km in 1 Results of random encounter modelling to estimate the population of Bawean deer the wet season (February–March) and . ± SE . in the ABLE

– (at locations with signs of deer), randomly set, and all cameras. T Model estimate (95% CI) 4.83 (3.80 Trapping rate (captures per day)Detection distance (km) 0.0533Detection arc (radians)Group size (no. of individuals)Speed (km day 0.0083 1.25 0.3410 0.0984 0.0519 0.0686 0.0525 0.0820 dry season (August September). The population size over Population size 177

FIG. 3 Seasonal distribution of abundance (individuals  per km ) of Bawean deer in the Bawean Island Nature Reserve and Wildlife Sanctuary (Fig. ) based on (a) camera trapping and random encounter modelling and (b) faecal pellet counts and faecal accumulation rates.

the sampled area was estimated to be  ± SE  and Reserve and Wildlife Sanctuary, from Mount Bulu to  ± SE  in the wet and dry seasons, respectively. Mount Bengkuang, at – m elevation. Only old faecal pellets were found at Mount Tinggi and Mount Besar. No deer were recorded in Tanjung Cina or in the north-east Distribution of the island by these techniques, nor did we find any footprints or other sign of presence at Mount Tinggi, Mount The presence of Bawean deer was recorded in eight and  Beringin, Kastoba Lake or Mount Payung-Payung. The grid cells using camera traps and faecal pellet counts, re- highest estimated abundance was in the vicinity of Mount  spectively, including seven grid cells by both techniques Dedawang (. and . individuals per km by random en- (Fig. ). The recorded range of the species was restricted counter modelling and faecal accumulation rate, respective- to the south-western part of the Bawean Island Nature ly). Abundance was lower at Mount Duren (. individuals

 per km by faecal accumulation rate) and Mount Besar (. density in the dry season, as previously reported (Blouch &  and . individuals per km by random encounter modelling Atmosoedirdjo, ), which supports the hypothesis that and faecal accumulation rate, respectively), and also around there is less movement in the wet season. The size of the  Mount Bajapati (. and . individuals per km ). It was Bawean deer population was estimated to be  individuals intermediate in the mixed secondary and teak forest of by the faecal pellet count method and  by random en-  Mount Bulu (. and . individuals per km ). counter modelling, which, compared to the previous estimate (–, by faecal pellet count; Semiadi, ), suggests stability. Discussion

Density estimates Distribution and conservation status

We successfully tested the suitability of camera trapping and Our records indicate that the range of the Bawean deer has random encounter modelling for monitoring the status of narrowed significantly. Camera trapping and faecal pellet the Bawean deer in its tropical forest habitat. The absence counts proved to be complementary, with presence at of photographs during the first  months (March–April) Klumpang Gubuk recorded only by the latter technique. may be related to the presence of a high number of research- Unlike Blouch & Atmosoedirdjo () and Semiadi ers at the beginning of the survey period, unsuitable loca- (), we found Bawean deer only in the central mountain tions of cameras, and lower activity of the species in the range and in the south-west of the Bawean Island Nature wet season. The trapping rate and density estimate increased Reserve and Wildlife Sanctuary, around Mount Bulu. We during the dry season, peaking in August. As most deer were assume that the deer is no longer present in Tanjung  photographed when feeding, this could be related to the Cina, where a density of . individuals per km during availability of food plants, which can become more scarce the wet season was reported previously (Semiadi, ). during the dry season, even in tropical habitats (Pontes & No sign of presence was recorded at Mount Tinggi, Chivers, ), leading to wider movements. Mount Beringin, Kastoba Lake or Mount Payung-Payung. Estimates obtained with the random encounter model Records by Sitwell (), Blower (), Blouch & were more precise (narrower confidence intervals) and Atmosoedirdjo () and Blouch () may indicate the higher than those obtained using faecal pellet counts. This existence of transient or survivor individuals rather than a discrepancy may be attributable to the decay of faeces, stable population, possibly associated with increased habitat and the approximate values of some parameters, such as quality in some protected areas where routine patrol activ- the speed of deer movement, and the camera detection ities have reduced human disturbance and damage to zone used in random encounter modelling. All parameters, vegetation. even those that are hard to obtain (Rowcliffe et al., ), The highest densities of Bawean deer were reported in sec- should be measured more accurately for the Bawean deer ondary forests around Mount Dedawang, Mount Nangka, in the future. The combination of camera trapping and glo- Mount Gadung, Mount Duren, Mount Mangoneng, Mount bal positioning system telemetry could improve the accur- Bengkuang and Batulintang, a small area around Mount acy of estimates, not only for performing random Bulu. The lowest densities were estimated in primary forests encounter modelling but also for analysing how species’ at Mount Besar, Mount Bajapati and Klumpang Gubuk. The home ranges can affect the required size of the area sampled. population was centred around Mount Dedawang (cf. Blouch Such a study could also test the assumption of random dis- & Atmosoedirdjo, ), with activity concentrated at low al- tributions of cameras and wildlife (Cusack et al., ). The titudes (,  m), where food and water are abundant. camera detection zone should also be investigated in differ- Hunting activity was recorded at six of  camera trap ent habitats and seasons; for example, we measured a lower locations (Rahman et al., ), and one snare was found detection radius in the wet season, although the difference in Batu Gebang block (the south-eastern part of Mount was too weak to explain the absence of deer detection in Dedawang), close to semi-open cultivated areas used by that season. wild boar for foraging, and with the highest density of Our findings suggest that random encounter modelling Bawean deer. Although wild boar were the poachers’ main may yield accurate density estimates for elusive, rare and un- target, deer could also be trapped, and die from stress- marked species, unlike photographic capture–recapture related causes (BBKSDA East Java, ). Furthermore, techniques, which require both unique markings and high- the Bawean Island Nature Reserve and Wildlife Sanctuary quality photographs for recognition of individuals is close to human settlements, and some areas have been da- (Soria-Dίaz & Monroy-Vilchis, ). Moreover, random maged by illegal logging. The continued presence of Bawean encounter modelling is continually being improved deer in harvested forests suggests some degree of tolerance (Rowcliffe et al., ). Both methods estimated the highest to selective logging. The deer are attracted to settlements by

agricultural crops (Semiadi, ), which places them at risk BAKOSURTANAL () Peta Rupa Bumi Indonesia. Http://bakosurtanal. from feral dogs (Blouch & Atmosoedirdjo, ). We photo- go.id/bakosurtanal/peta-rbi/ [accessed  April ].  graphed feral dogs in  grid cells (Rahman et al., ) and BBKSDA EAST JAVA ( ) Monitoring satwa flagship jenis rusa Bawean. Unpublished report. Balai Besar Konservasi Sumberdaya recorded two cases of Bawean deer killed by feral dogs close Alam Jawa Timur, Surabaya, Indonesia. to settlements. Although such events are rarer now than pre- BLAKE,S.&HEDGES,S.() Sinking the ﬂagship: the case of forest viously (Blouch & Atmosoedirdjo, ), the threat should elephants in Asia and Africa. Conservation Biology, , –. be taken seriously as feral dogs are the main predators of BLOUCH, R.A. () Kuhl’s deer, Bawean Island, Indonesia. Tiger Paper, , –. several deer species in South America (Weber & Gonzalez,   BLOUCH, R.A. & ATMOSOEDIRDJO,S.( ) Preliminary report on the ). status of the Bawean deer (Axis kuhli). In Threatened Deer: The Bawean deer has survived a decade of social turmoil, Proceedings of a Working Meeting of the Deer Specialist Group of the in which food scarcity triggered high levels of hunting and Survival Service Commission, pp. –. IUCN, Morges, illegal logging (Semiadi, ). Despite a stable population Switzerland.  size, the species should retain its Critically Endangered sta- BLOUCH, R.A. & ATMOSOEDIRDJO,S.( ) Biology of the Bawean   deer and prospects for its management. In Biology and Management tus under criterion B ab(ii, iii) and not C a(ii) (which sug- of the Cervidae (ed. C.M. Wemmer), pp. –. Smithsonian gests a population decline) as in the most recent assessment  Institution Press, Washington, DC, USA. (Semiadi et al., ). The extent of occurrence is ,  km , BLOWER,J.() Report on a Visit to Pulau Bawean. UNDP/FAO the area of occupancy is declining and the habitat is frag- Nature Conservation and Wildlife Management Project, ISN//. mented. Further study is needed, including a long-term FAO, Bogor, Indonesia. CARBAJAL-BORGES, J.P., GODÍNEZ-GÓMEZ,O.&MENDOZA,E. monitoring scheme. A captive-breeding programme was es-   ( ) Density, abundance and activity patterns of the endangered tablished in with a founder population of two stags and Tapirus bairdii in one of its last strongholds in southern Mexico. five hinds, and this population had increased to  indivi- Tropical Conservation Science, , –. duals by  (Meijaard et al., ). CARBONE, C., CHRISTIE, S., CONFORTI, K., COULSON, T., FRANKLIN, N., GINSBERG, J.R. et al. () The use of photographic rates to estimate densities of tigers and other cryptic mammals. Animal  – Acknowledgements Conservation, , . 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WEBER,M.&GONZALEZ,S.() Latin American deer diversity ROWCLIFFE, J.M., CARBONE, C., JANSEN, P.A., KAYS,R.& and conservation: a review of status and distribution. Ecoscience, , KRANSTAUBER,B.() Quantifying the sensitivity of camera traps: –. an adapted distance sampling approach. Methods in Ecology and ZERO, V.H., SUNDARESAN, S.R., O’BRIEN, T.G. & KINNAIRD, M.F. Evolution, , –. () Monitoring an Endangered savannah ungulate, Grevy’s zebra ROWCLIFFE, J.M., CARBONE, C., KAYS, R., KRANSTAUBER,B.& Equus grevyi: choosing a method for estimating population JANSEN, P.A. () Bias in estimating animal travel distance: the densities. Oryx, , –. effect of sampling frequency. Methods in Ecology and Evolution, , –. ROWCLIFFE, J.M., FIELD, J., TURVEY, S.T. & CARBONE,C.() Biographical sketches Estimating animal density using camera traps without the need for individual recognition. Journal of Applied Ecology, , –. DEDE AULIA RAHMAN specializes in the ecology of deer species in trop- ROWCLIFFE, J.M., KAYS, R., KRANSTAUBER, B., CARBONE,C.& ical rainforest, and is working to develop the use of camera trapping JANSEN, P.A. () Quantifying levels of animal activity and remote sensing in ungulate conservation projects in Indonesia. using camera trap data. Methods in Ecology and Evolution, , GEORGES GONZALEZ studies the behaviour, management and conserva- –. tion of ungulates in Western Europe, particularly in protected or man- SEBER, G.A.F. () The Estimation of Animal Abundance and Related aged areas. STÉPHANE AULAGNIER’s main area of interest is the Parameters. Macmillan, New York, USA. evolutionary biology and conservation of Palaearctic mammals.