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, 5, e3272

Quantifying Climate Change Impacts Emphasises the Importance of Managing Regional Threats in the Endangered Yellow-eyed Penguin


Quantifying Climate Change Impacts Emphasises the Importance of Managing Regional Threats in the Endangered Yellow-eyed Penguin

Thomas Mattern et al. PeerJ.


Climate change is a global issue with effects that are difficult to manage at a regional scale. Yet more often than not climate factors are just some of multiple stressors affecting species on a population level. Non-climatic factors-especially those of anthropogenic origins-may play equally important roles with regard to impacts on species and are often more feasible to address. Here we assess the influence of climate change on population trends of the endangered Yellow-eyed penguin (Megadyptes antipodes) over the last 30 years, using a Bayesian model. Sea surface temperature (SST) proved to be the dominating factor influencing survival of both adult birds and fledglings. Increasing SST since the mid-1990s was accompanied by a reduction in survival rates and population decline. The population model showed that 33% of the variation in population numbers could be explained by SST alone, significantly increasing pressure on the penguin population. Consequently, the population becomes less resilient to non-climate related impacts, such as fisheries interactions, habitat degradation and human disturbance. However, the extent of the contribution of these factors to declining population trends is extremely difficult to assess principally due to the absence of quantifiable data, creating a discussion bias towards climate variables, and effectively distracting from non-climate factors that can be managed on a regional scale to ensure the viability of the population.

Keywords: Anthropogenic threats; Climate change; Conservation; Demography; Endangered species; New Zealand; Penguins; Population modelling; Species management; Survival rates.

Conflict of interest statement

Yolanda van Heezik is an Academic Editor for PeerJ.


Figure 1
Figure 1. Overview of the breeding range of Yellow-eyed penguins.
Overview of the breeding range of Yellow-eyed penguins, detail of the Otago Peninsula with an aerial view of the Boulder Beach Complex (henceforth Boulder Beach) with outlines indicating the locations of the four main monitoring plots. The inset map also indicates Kumo Kumo Whero Bay, the location of the historic population study conducted from the 1930s to 1950s.
Figure 2
Figure 2. Observed penguin numbers at Kumo Kumo Whero and Boulder Beach.
Observed penguin numbers at Kumo Kumo Whero 1937–1948 (from data published in Richdale 1957, see ‘Methods’ for details) and at the Boulder Beach complex 1982–2015 as extracted from the Yellow-eyed penguin database. ‘New breeders’ represents the portion of all ‘breeding adults’ that were recorded as breeders for the first time. Red arrows indicate years with observed die-off events affecting adult breeders. Note that as some sections of the Boulder Beach complex were not monitored in all years, data for the years 1986–1989 were adjusted by adding the mean proportion these areas contributed to the total count in all other years.
Figure 3
Figure 3. Age of breeding Yellow-eyed penguins.
Average age of breeding Yellow-eyed penguins active at Boulder Beach between 1982 and 2015. Red arrows indicate years with observed die-off events affecting adult breeders.
Figure 4
Figure 4. Adult survival and SST anomalies.
Top graph: local Sea Surface Temperature anomalies recorded at Portobello Marine Lab, Otago Peninsula, between 1953 and 2016. Bottom graph: detail of SST anomalies 1980–2016 and associated deviance (black line: mean; grey area: 95% credible interval) in survival of adult Yellow-eyed penguins as determined from a MR recapture model.
Figure 5
Figure 5. Population projections for Yellow-eyed penguins at Boulder Beach, Otago Peninsula.
Population projections for Yellow-eyed penguins at Boulder Beach, Otago Peninsula. The graphs show the observed (red line) and estimated (black line) number of female penguins, and associated 95% credible interval (grey area), as derived from the population model. The dashed vertical line indicates the last year used to parameterise the MR model and the starting year of the simulation. Population projections were modelled using survival rate estimates until 2012; beyond this year estimates get increasingly unreliable because these are based on data about individual absence from breeding rather than from reported mortalities (see ‘Methods’).
Figure 6
Figure 6. Probability density functions for growth rates.
Probability density functions for deterministic annual population growth rates derived from survival rates that were rescaled for periods of cooler (1982–1996) and warmer (1996–2014) than average sea surface temperatures.

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Grant support

This work was supported by an University of Otago Research Grant (issued to PJS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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