Rakiura Māori muttonbirding diaries: monitoring trends in tītī (Puffinus griseus) abundance in New Zealand

Human exploitation assisting a threatened species? The case of muttonbirders and Buller's albatross

Albatrosses are flexible and adaptable predators, relying on live prey as well as carrion. Use of predictable food sources and reliance on human-produced resources are well-known trait in long-range feeders like albatrosses and petrels. Breeding Buller’s albatrosses studied at Solander I. (Hautere), New Zealand fed their chicks the remains of sooty shearwater juveniles (tītī in Māori), which are harvested from nearby muttonbirding sites. Evidence of this food type was found at over 10% of nests examined, and 17–40% birds that were fitted with GPS loggers visited muttonbirding sites in this and previous studies. Muttonbirding is a traditional practice that has continued for centuries, with up to 120 tonnes of offal discharged to the sea annually during the present day harvest. It coincides with the energetically-demanding early chick period for the albatrosses. Our finding suggests that the offal may be an important, but overlooked element in the albatross diet. As an important supplementary food for the albatrosses it is likely to have contributed to the 3% per annum growth of their populations since the first comprehensive population surveys in 1969.

Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data

Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori 'muttonbirder' diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as "flyways" by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.

Pattern recognition in long‐term Sooty Shearwater data: applying machine learning to create a harvest index

Rakiura Māori (New Zealand's southernmost group of indigenous peoples) have harvested the chicks of burrow-nesting Sooty Shearwaters (Tītī; Puffinus griseus) for generations. As part of the harvest process, some families have maintained annual harvest diaries, some dating back to the 1950s. We used generalized boosted regression models, a machine-learning algorithm, to calculate a harvest index that takes into account factors that could impact the numbers of birds taken on any given hunt. For predicted vs. observed values, r2 was between 0.59 and 0.90 for the nanao (first half of the season, when chicks are harvested from burrows during the day) and 0.67 and 0.88 for the rama (second half of the season, during which chicks are harvested from the surface at night). Exploration of the controlling factors of the models revealed that “day of season” plays an important role in predicting daily harvest during the second half of the season (the rama). The nightly tally in the rama peaked approximately halfway through (10–15 days in), which is probably related to the timing of birds emerging from burrows to fledge. The models also suggested that data from the rama (when chicks are 100–120 days old) may be the most suitable for long-term monitoring of populations of Sooty Shearwaters due to consistencies in calculated harvest indices between diaries. Nanao harvest indices, although less consistent, showed patterns similar to those of the rama. When comparing these data to the harvest indices calculated by general linear models by Clucas and colleagues, we found that the agreement between both indices was r2 = 0.31 and r2 = 0.59 for the nanao and rama, respectively. The use of machine learning to correct for extraneous factors (e.g., hunting effort, skill level, or weather) and to create standardized measures could be applied to other systems such as fisheries or terrestrial resource management.