Trygve Nilsen

Frequent skipped spawning in the world’s largest cod population

Life-history theory suggests that animals may skip reproductive events after initial maturation to maximize lifetime fitness. In iteroparous teleosts, verifying past spawning history is particularly difficult; the degree of skipped spawning at the population level therefore remains unknown. We unequivocally show frequent skipped spawning in Northeast Arctic cod (NEAC) in a massive field and laboratory effort from 2006 to 2008. This was verified by postovulatory follicles in temporarily arrested ovaries close to the putative spawning period. At the population level, “skippers” were estimated to be approximately equally abundant as spawning females in 2008, constituting ∼24% of the females 60–100 cm. These females never truly started vitellogenesis and principally remained on the feeding grounds when spawners migrated southward, avoiding any migration costs. The proximate cause of skipping seems to be insufficient energy to initiate oocyte development, indicating that skipped spawning may partly be a density-dependent response important in population regulation. Our data also indicate more skipping among smaller females and potential tradeoffs between current and future reproductive effort. We propose that skipped spawning is an integral life-history component for NEAC, likely varying annually, and it could therefore be an underlying factor causing some of the currently unexplained large NEAC recruitment variation. The same may hold for other teleosts.

Abundance of two threatened woodpecker species in relation to the proportion of spruce plantations in native pine forests of western Norway

In a comparative study we investigated woodpecker abundance in forest landscapes with different proportion of native pine forest and spruce plantations in western Norway. In 100 circular study plots of 100 ha each we recorded 38 white-backed –Dendrocopos leucotos, 22 grey-headed –Picus canus, 13 great spotted –Dendrocopos major, 6 green –Picus viridis, and 2 lesser spotted –Dendrocopos minor woodpeckers in the breeding season. The mean number of recorded woodpecker species peaked at 20–40% spruce plantations. The two most common species in the study, the white-backed and the grey-headed woodpeckers are both Red-listed species in Norway and among the rarest woodpeckers in Europe. The white-backed woodpecker preferred plots with higher than average proportions of standing dead trees and deciduous trees, and low proportions of spruce plantations in the plots. The grey-headed woodpecker preferred plots in the western (coastal) parts of the study area with presence of large aspen Populus tremula trees. Logistic regression models did not reveal any clear threshold values with respect to proportion of spruce plantations in plots, although both woodpecker species were extremely rare in plots with >60% spruce plantations. We recommend spruce plantations to be kept at moderate levels to ensure viable populations of woodpeckers in western Norway.

On the distribution of a randomly discounted compound Poisson process

We study the distribution of the stochastic integral [integral operator]0t8e-Rt dPt where R is a Brownian motion with positive drift and P is an independent compound Poisson process. We show that in the special case when the jumps of P are exponentially distributed, the integral has the same distribution as that of a gamma variable divided by an independent beta variable.

Local linear mixed effect models—Model specification and interpretation in a biological context

Clustered data, either as an explicit part of the study design or due to the natural distribution of habitats, populations, and so on, are frequently encountered by biologists. Mixed effect models provide a framework that can handle clustered data by estimating cluster-specific random effects and introducing correlated residual structures. General parametric models have been shown not to suit all biological problems, resulting in an increased popularity for local regression procedures, such as LOESS and splines. To evaluate similar biological problems for clustered data with cluster-specific random effects and potential dependencies between within-cluster residuals, we suggest a local linear mixed model (LLMM). The LLMM approach is a local version of a linear mixed-effect model (LME), and the LLMM approach produces: (1) local shared predictions, (2) local cluster-specific predictions, and (3) estimates of cluster-specific random effects conditioned on the covariates. Thus, in addition to the local estimates of the expected response, we obtain information about how the cluster-specific random variability depends on the values of the covariate. Ovary data are used to illustrate the flexibility and potential of this procedure in biological contexts.