We used demographic data from five populations studied in France (Trois Fontaines and Chizé) and Norway (Storfosna, Østerdalen and Akershus/Østfold). These populations span a wide range of environmental conditions (Table 1). The sampling designs that varied among areas are described elsewhere (see e.g. Gaillard et al. 1993; Nilsen, Linnell & Andersen 2004).
Table 1. The five roe deer populations studied and their main environmental characteristics. Low density is defined as < 5 deer km−2, medium density is 5–10 deer km−2, and high density is > 10 deer km−2. At Chizé, roe deer had a low/medium performance between 1986 and 1992 (high density and density-dependent responses), a high performance between 1994 and 2002 (low density and good climatic conditions during spring–summer), and a very poor performance between 2003 and 2006 (high density and frequent spring–summer droughts). At Trois Fontaines, the population size was weakly variable between 1985 and 2001, whereas population density increased strongly between 2002 and 2006. Red foxes were absent from Storfosna, but present in all other populations
|Population||Predators of adults present||Harvesting||Productivity||Winter climate (temp/snow cover)*||Summer climate (temp/prec.)†||Spring climate (temp/prec.)‡||Roe deer density||Time span||Comments¶|
|Storfosna||No||No||High||1·2 C 6·5 cm−1||12·8 C 85·7 mm−1|| 9·1 C 72·7 mm−1||High||1991–94||Island pop.|
|Østerdalen||Lynx||Yes||Low||−6·1 C 42 cm−1||14·7 C 75·1 mm−1||10·3 C 81·5 mm−1||Low||1995–98||Open pop.|
|Akershus/Østfold||Lynx (and a few wolves)||Yes||Medium||−2·8 C 13·3 cm−1||16·2 C 74·7 mm−1||12·4 C 80·8 mm−1||Medium||2001–04||Open pop.|
|Trois Fontaines||No||No§||High||4·9 C 0 cm−1||19·6 C 70·1 mm−1||15·8 C 70·2 mm−1||Medium||1985–2001||Fenced pop.|
|5·1 C 0 cm−1||20·0 C 69·5 mm−1||16·7 C 51·6 mm−1||High||2002–06|
|Chizé||No||No§||Low||6·7 C 0 cm−1||20·7 C 44·2 mm−1||16·6 C 47·3 mm−1||High||1986–92||Fenced pop.|
|5·5 C 0 cm−1||20·4 C 56·2 mm−1||16·7 C 53·8 mm−1||Medium||1994–2002|
|6·3 C 0 cm−1||21·0 C 50·0 mm−1||17·5 C 36·4 mm−1||High||2003–06|
The study area (7·8 km2) on Storfosna (10·8 km2) is located on a small island 2 km off the coast of central Norway (63°4′ N, 09°3′ E). The island consists of a fine-scaled mosaic of heather-dominated moorland, abandoned meadows, cultivated pasture, and mixed coniferous/deciduous woodland. During the study period, from 1991 to 1994, the population density increased from 10·1 deer km−2 in spring 1991 to 34·5 deer km−2 in May 1994 (Andersen & Linnell 2000). Demographic parameters were estimated based on radiocollared roe deer (n = 128), mostly marked as fawns (n = 94, see Nilsen et al. 2004 for further details).
The study area in Østerdalen is located in south-eastern Norway (61°15′ N, 11°30′ W). The topography consists of parallel river valleys running from north to south at about 200–500 m above sea level with hills ranging from 600 to 900 m above sea level. Agricultural land and human settlements are scattered along most valley bottoms. The vegetation is predominantly boreal coniferous forest (Scots pine Pinus sylvestris, Norwegian spruce Picea abies and birch Betula sp.), with 72% of the study area covered with forest. Based on hunter records and counts made at supplementary feeding stations, the roe deer density is believed to be extremely low (< 1 per km2). Demographic parameters were estimated based on radiocollared roe deer (n = 62) often marked as fawns (n = 23, see Panzacchi et al. 2008 for further details).
The Akershus/Østfold study area, situated 100 km south of Østerdalen, is also dominated by boreal forest, but includes patches of deciduous forest represented mainly by birch. The landscape is human modified, with the forest fragmented by cultivated land and water bodies, and the altitude is not higher than 300 m above sea level. Roe deer density estimated from hunting records suggest that they occur at moderate densities, and that the density declined slightly during the study period from 2001 to 2005. Demographic parameters were estimated based on radiocollared roe deer (n = 116), mostly marked as fawns (n = 44, see Panzacchi et al. 2008 and Ratikainen et al. 2007 for further details).
The study area at Chizé (26·6 km2) in western France (46°05′ N, 0°25′ W) has an oceanic climate with Mediterranean influences, with mild winters and warm often dry summers. This fenced reserve managed by the Office National de la Chasse et de la Faune Sauvage (ONCFS) consists of a forest dominated by oak Quercus sp. and beech Fagus sylvatica. Overall, the forest is not highly productive because of poor soil quality and of frequent summer droughts. Since 1977, capture–mark–recapture estimates of population size and demographic parameters are available (see Gaillard et al. 2003 for further details). High variation in population size (controlled by yearly removals) and climate among years allowed us to define three periods of contrasting roe deer performance (see Table 1). Demographic parameters were estimated based on individually marked (both ear-tags and numbered collar) and known-age roe deer (n = 599) often marked as newborns (n = 322, see Gaillard et al. 2003 for further details).
The study area at Trois Fontaines (13·6 km2) in eastern France (48°43′ N, 54°10′ E) has a continental climate, with quite harsh winters and warm but moist summers. This fenced area managed by the ONCFS consists of a forest dominated by oak and beech. The forest is quite homogeneous at a large spatial scale (> 100 ha) but highly heterogeneous at small spatial scales (< 10 ha). Trois Fontaines is a rich and productive forest (Pettorelli et al. 2006) due to high-quality soils and generally high rainfall in spring. Variation in population size (controlled by yearly removals) allowed us to define two periods of contrasting roe deer performance (see Table 1). Demographic parameters were estimated based on individually marked (both ear-tags and numbered collar) and known-age roe deer (n = 997) mostly marked as newborns (n = 907, see Gaillard et al. 2003 for further details).
As well as differing in climate and density, these populations differed with respect to predation. Hunters, Eurasian lynx and red foxes were predators of roe deer in both Norwegian mainland sites, with both hunters and lynx killing all age classes (Andersen et al. 2007) and foxes killing fawns during the post-natal period (Panzacchi et al. 2008). Although red foxes were present in the two French study sites, there is no evidence of marked predation on fawns in these areas.
estimation of demographic parameters
Due to different sampling regimes between study sites, parameter estimation techniques also varied between the study sites. Most importantly, data collection in the three Norwegian populations was based on radiotelemetry studies, whereas the French populations were based on individually marked animals potentially recaptured once every year.
In the Norwegian populations, survival rates were estimated by known-fate capture–mark–recapture (CMR) models well suited to survival analyses of radiotracked animals (White & Burnham 1999). We censored animals with unknown fate (e.g. due to radiocollar failure). Litter size and proportion of females that gave birth were estimated based on direct observations of radiocollared does in the spring close to the birth period (see also Nilsen et al. 2004 for further details).
In the French populations, survival rates were estimated using standard CMR methods (Lebreton et al. 1992) to account for capture rates being less than 1 (about 0·5 at both Chizé and Trois Fontaines for roe deer older than 1 year of age; Gaillard et al. 1993). Age- and sex-specific survival probabilities and their SE were obtained using the software msurge 7·1 (Choquet et al. 2004). Litter size and proportion of females that gave birth were estimated from ultrasounds performed during winter captures since 1988 and from progesterone assays in the same period before 1988 at Chizé (see Gaillard et al. 1992, 2003 for further details). At Trois Fontaines, we assumed that all females older than 1 year of age produced twins, as supported by the empirical evidence (see Gaillard et al. 1998). This assumption is likely to lead to a slight overestimation of the reproductive output of females during the high-density period.
roe deer life cycle
To be able to quantify the relative contribution from different demographic parameters to variation in λ, a life cycle has to be defined. We based our analysis on a pre-breeding life cycle (Fig. 1), assuming that the populations are censused just before the breeding season each year. This implies that the youngest age class present in the population at the census time is the 1-year-olds and that juvenile survival (between birth and 1 year of age) is included in the recruitment rate (see Caswell 2001 for further details). Consequently, we considered the following demographic parameters in our analyses:
Figure 1. A schematic representation of roe deer life cycle as defined in this study. The stages are yearling (1) and adult (2) respectively, whereas the transitions are given as yearling survival (YS) and adult survival (AS), respectively. Only adult females reproduce (F) in our model and we assume no age dependence in survival and reproduction in the adult stage. See text for justification.
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Recruitment rate (F): number of female offspring that enter the population at time t + 1 (just before the birth season) per female alive at the beginning of year t (just before the birth season). The recruitment is thus given by the product of the proportion of females (≥ 2 years old) that give birth in year t, the mean number of females produced in year t (litter size divided by 2, i.e. assuming balanced sex ratio at birth), juvenile summer survival (from birth to weaning) and juvenile winter survival (from weaning to 1 year of age).
Yearling survival (YS): survival probability through the second year of life (i.e. from 1 to 2 years of age).
Adult survival (AS): annual survival probability for adult females (i.e. aged 2 years or more).
We based our further analysis on perturbation analysis of a transition matrix describing female roe deer population dynamics in five different areas. Although demographic rates of large herbivores show strong age dependency (see e.g. Gaillard et al. 1993 for the case of roe deer survival patterns), we pooled all the adult age classes into one adult stage. The reason for this is twofold; first, in our Norwegian populations the time span of the studies were < 5 years, and ages of the individuals were known accurately only for individuals marked during their first year of life. Consequently, an age-structured model would have contained very few individuals in the older age classes. Second, although both survival and reproduction do vary with age in roe deer, it is usually relatively independent of age for prime-aged females (2–8 years) (Gaillard et al. 1993; Andersen & Linnell 2000; Festa-Bianchet, Gaillard & Côté 2003). Few individuals would reach senescence, but we are aware that this might cause a slight bias in the estimates of demographic rates and their variance. This happens because the survival of senescent females is lower and more variable than that of prime-aged individuals (Gaillard et al. 2000; Festa-Bianchet et al. 2003). To assess the robustness of this assumption, we also performed the calculations based on a fully age-structured model for Chizé (see Table S1) where the proportion of old females was the highest. The general patterns did not differ between the two models, but the contribution from adult survival generally decreased slightly when accounting for senescence.
Perturbation analysis of a projection matrix is based on the assumption that the stable age structure and reproductive values are given by the left and right eigenvectors of the projection matrix (see below). However, both age structure and reproductive values might vary for populations in different phases of development. To fully account for this, using a structured demographic account (SDA) would be required, but this method requires complete historical knowledge of all individuals in the population, and in most cases these two methods will give comparable results when covariation between demographic rates is accounted for (Coulson et al. 2005). As pointed out by several authors (see e.g. Sæther & Bakke 2000; van Tienderen 2000; Coulson et al. 2005), it is important to control for the effect of covariation between demographic rates. In our study, with very short time series from some of the populations and periods (see Table 1), we did not consider covariation between traits. However, the contribution of covariation among parameters accounted for only ~15% of the variation in λ in populations for which we had > 10 years of data (see Table S2).