Amélie A. Augé

Aggregation and dispersion of female New Zealand sea lions at the Sandy Bay breeding colony, Auckland Islands: How unusual is their spatial behaviour?

We investigated the spatial behaviour adopted by female New Zealand sea lions, Phocarctos hookeri, at the Sandy Bay breeding colony in 2002 and 2003. Each breeding female exhibited a spatio-temporal behaviour based on two phases: breeding and dispersion. The breeding phase, typical of all otariids, led to the formation of the breeding aggregation where all pupping took place. Each female later moved outside the breeding area and entered a dispersion phase. The female population spread inland, and progressively decreased as females took their pups away from Sandy Bay. Pup survival was not affected by this spatial behaviour though the year had an effect. A larger population size during one year may have created a dilution of male aggressiveness and resulted in fewer movements of females. Females that had to move more during the pupping day were found to be more likely to lose their pups. Although a few studies have shown that mother and pup pairs of other species may exhibit dispersal after breeding, the observed terrestrial dispersion phase of the female New Zealand sea lions has never been reported for any other pinniped species and is likely unusual.

Autumn diet of recolonising female New Zealand sea lions based at Otago Peninsula, South Island, New Zealand

New Zealand (NZ) sea lions (Phocarctos hookeri) are slowly recolonising the Otago coast, South Island, New Zealand. The increase in their numbers may lead to resource competition with other marine predators and fisheries. We determined the diet of female NZ sea lions at Otago during autumn. In total, 571 scats and 110 regurgitations were collected on Otago Peninsula during 2008 and 2009. Barracouta (Thyrsites atun) and jack mackerel (Trachurus sp.) were the two main prey species and accounted for 26% and 31% of the reconstituted biomass, respectively. This was consistent between two years. Only five other species contributed > 5% of the diet by biomass in either year. Prey species are all found on the narrow continental shelf surrounding Otago Peninsula. The main prey species of Otago NZ sea lions may be of higher energy content than prey in the Auckland Islands (remnant breeding area). Resource overlap with other marine predators and fisheries appears to occur around Otago Peninsula. A marine trophic model of the area off Otago Peninsula would help understanding potential competition between marine predators and fisheries in this area.

Foraging behaviour of juvenile female New Zealand sea lions (Phocarctos hookeri) in contrasting environments.

Foragers can show adaptive responses to changes within their environment through morphological and behavioural plasticity. We investigated the plasticity in body size, at sea movements and diving behaviour of juvenile female New Zealand (NZ) sea lions (Phocarctos hookeri) in two contrasting environments. The NZ sea lion is one of the rarest pinnipeds in the world. Most of the species is based at the subantarctic Auckland Islands (AI; considered to be marginal foraging habitat), with a recolonizing population on the Otago Peninsula, NZ mainland (considered to be more optimal habitat). We investigated how juvenile NZ sea lions adjust their foraging behaviour in contrasting environments by deploying satellite-linked platform transmitting terminals (PTTs) and time-depth recorders (TDRs) on 2–3 year-old females at AI (2007–2010) and Otago (2009–2010). Juvenile female NZ sea lions exhibited plasticity in body size and behaviour. Otago juveniles were significantly heavier than AI juveniles. Linear mixed effects models showed that study site had the most important effect on foraging behaviour, while mass and age had little influence. AI juveniles spent more time at sea, foraged over larger areas, and dove deeper and longer than Otago juveniles. It is difficult to attribute a specific cause to the observed contrasts in foraging behaviour because these differences may be driven by disparities in habitat/prey characteristics, conspecific density levels or interseasonal variation. Nevertheless, the smaller size and increased foraging effort of AI juveniles, combined with the lower productivity in this region, support the hypothesis that AI are less optimal habitat than Otago. It is more difficult for juveniles to forage in suboptimal habitats given their restricted foraging ability and lower tolerance for food limitation compared to adults. Thus, effective management measures should consider the impacts of low resource environments, along with changes that can alter food availability such as potential resource competition with fisheries.

Quantifying apart what belongs together: A multi-state species distribution modelling framework for species using distinct habitats

Summary 1.Species distribution models (SDMs) have been used to inform scientists and conservationists about the status and change of occurrence patterns in threatened species. Many mobile species use multiple functionally distinct habitats, and cannot occupy one habitat type without the other being within a reachable distance. For such species, classical applications of SDMs might lead to erroneous representations of habitat suitability, as the complex relationships between predictors are lost when merging occurrence information across multiple habitats. To better account for the spatial arrangement of complementary—yet mandatory—habitat types, it is important to implement modeling strategies that partition occurrence information according to habitat use in a spatial context. Here, we address this issue by introducing a multi-state SDM framework. 2.The multi-state SDM framework stratifies occurrences according to the temporal or behavioral use of distinct habitat types, referred to as “states.” Multiple SDMs are then run for each state and statistical thresholds of presence are used to combine these separate predictions. To identify suitable sites that account for distance between habitats, two optional modules are proposed where the thresholded output is aggregated and filtered by minimum area size, or through moving windows across maximum reachable distances. 3.We illustrate the full use of this framework by modeling the dynamic terrestrial breeding habitat preferences of the New Zealand sea lion (NZSL; Phocarctos hookeri), using Maxent and trialing both modules to identify suitable sites for possible recolonization. 4.The Maxent predictions showed excellent performance, and the multi-state SDM framework highlighted 36 to 77 potential suitable breeding sites in the study area. 5.This framework can be applied to inform management when defining habitat suitability for species with complex changes in habitat use. It accounts for temporal and behavioral changes in distribution, maintains the individuality of each partitioned SDM, and considers distance between distinct habitat types. It also yields one final, easy-to-understand output for stakeholders and managers. This article is protected by copyright. All rights reserved.