4.3.1.1 Demographic data and parameter estimation
Demographic data have been collected from 200 annually monitored nests at the
approximately 1,000 breeding pair colony at Cañon des Sourcils Noirs, Kerguelen Island (48.24° S, 68.218° E, Fig. 4.2; Barbraud et al., 2011; Rolland et al., 2008). From 1967, adults and chicks were ringed with stainless still rings and since 1979 a capture-mark-recapture program has been undertaken annually between October and March. Most rings of breeding birds were checked in October just after laying. Two additional visits to monitored nests in late December and late March allowed determining the breeding success of each pair. Each year, all unringed breeding individuals found in the study area and all chicks were ringed just before fledging with a
stainless steel band.
Using these data, multi-event capture-mark-recapture models were used to calculate demographic parameters (survival probability, breeding probability, success probability) for the period 1979-2013 (Pradel, 2005)1. These models incorporate uncertainty in the state (e.g.
successful or failed breeder) of an individual at a given time. As the relationship between states and events is probabilistic, these models are within the Hidden Markov model family (Pradel, 2005). The model structure of the multi-event capture-mark-recapture model was similar to Barbraud et al. (2013) for wandering albatrosses. Juvenile survival until age 5 was estimated using a multistate capture-mark-recapture model following Lebreton & Pradel (2002) and
1 Estimation of demographic parameters using multi-event capture mark recapture models was performed by Christophe Barbraud; Centre d'Etudes Biologiques de Chizé - Centre national de la recherche scientifique / Universite La Rochelle, France
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Nevoux et al. (2010b) for the period 1967-2002. Juvenile demographic data for the years 2003- 2013 were excluded since recruitment of individuals from these cohorts was not terminated (Nevoux et al., 2010). Our initial model for adults was a model were survival, breeding
probability, success probability, capture probability and state assignment probability were state and time dependent. From this model, we first tested for temporal trends in detection probability and state assignment probability by fitting models where these probabilities varied according to a quadratic (or linear) trend on a logit scale as follows:
𝑙𝑜𝑔 ( 𝜃
1 − 𝜃𝑡) = 𝑙𝑜𝑔𝑖𝑡(𝜃𝑡) = 𝑎 + 𝑏 ∗ 𝑇𝑡+ 𝑐 ∗ 𝑇𝑡
2
where is the parameter of interest, a is the intercept, b and c are the slopes of the linear and quadratic terms, respectively, on the logit scale. We then tested for state dependence on the demographic parameters. Model selection was performed using the AIC and maximum likelihood estimates for demographic parameters were obtained from the program E-SURGE 1.8.4 (Choquet et al., 2009). These demographic estimates are used by the population model (below) to estimate the virgin (not impacted by fisheries) population demographic parameters. 4.3.1.2 Distribution at-sea data
Distribution at-sea was modelled as the utility distribution (e.g. Weimerskirch et al., 2015) for 13 different categories of sex, time of year, breeding status, and age (Appendix B.1, Table B.1). Breeding periods are based on those reported in Delord et al. (2013).
4.3.1.3 Fishing effort data
Given their relatively broad distribution, Kerguelen BBA are likely to interact with a broad range of fishing fleets. Albatross foraging around longline or trawl fishing activities can become hooked or entangled in fishing gear resulting in bycatch (Brothers, 1991; Croxall, 2008;
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Sullivan et al., 2006). Monthly reported effort data in 5° x 5° spatial cells were acquired from the Indian Ocean Tuna Commission (IOTC), the Secretariat of the Pacific Community (SPC), the International Commission for the Conservation of Atlantic Tunas (ICCAT), Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), the French Museum of Natural History (French and Ukrainian trawl fleets within the Kerguelen French Exclusive Economic Zone), and the national fishing agencies of South Africa, New Zealand and Australia. As data within the Kerguelen French Exclusive Economic Zone did not include spatial coordinates, the location of effort relative to 5° x 5° spatial cells was estimated based on Weimerskirch et al. (2000). Data from Namibia and South American countries were obtained from Tuck et al. (2015). Further refinements to the effort data used in Tuck et al. (2015) are detailed therein and available at: DRYAD entry: doi: 10.5061/dryad.7f63m. These refinements were based upon updated publicly available effort data from the respective Regional Fisheries Management Organization. See Appendix B.2 for details on effort refinements regarding southern Indian Ocean fleets and Table B.2 for all fleets included in the model.
Effort was grouped into five ‘super-fleets’, composed of fleets from multiple nations based off gear type and reported bycatch rates as follows: (i) Japanese mitigated pelagic longline south of 30˚ S, (ii) other pelagic longline, (iii) trawl, (iv) demersal longline, and (iv) illegal, unreported and unregulated demersal longline (IUU; Fig. 4.2; Appendix B.3 Table B.2). As the Japanese pelagic longline fleet south of 30°S introduced voluntary bycatch mitigation measures in the late 1980s and early 1990s, we assumed that this resulted in a bycatch reduction of 20% after 1992 (Tuck et al. 2015). The estimated parameter values were insensitive to this choice. However, allowing a separate bycatch catchability to be estimated for the Japanese pelagic fleet, this did not significantly improve the model (likelihood ratio test, df = 1, p = 0.7, difference
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between log likelihoods = 0.14). Therefore, the same parameter value was used for the Japanese and all other pelagic longline fleets. When the available effort data ended before 2011, the effort for the last year of data was repeated for each year through 2011, with the exception of fleets known or suspected to be inactive, in which case effort was set at zero.
Figure 4.2 Annual estimated magnitude (million hooks or thousand trawl hours) south of 30° S by super-fleet
Figure 4.2: Annual estimated magnitude of effort (million hooks or thousand trawl hours) south
of 30°S by super-fleet. Effort estimates include the last year of available data repeated until 2011, excluding fleets assumed to be inactive (i.e. IUU).
4.3.1.4 Fishery bycatch data
Reported bycatch rates (longline: birds / 1,000 hooks, trawl: birds / 1,000 hrs.) were obtained from primary literature and reports (summarized in Appendix B.4 Table B.3). As no direct observations of IUU bycatch rates are available, a single estimated value was used, which is based on bootstrapped observed bycatch rates from the legal demersal longline fleet in the same region in 1996/97 when relatively few mitigation measures were applied (Anon 2006, p. 78). To assess the sensitivity of the model estimated parameters to our assumptions regarding the IUU demersal longline super-fleet including: magnitude of effort, bycatch rate, and the
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assumed probability of a BBA being caught originating from the study population, we evaluated a range of model scenarios altering our assumptions of this super-fleet (Appendix D), but do not report these in detail in the present chapter.
4.3.1.5 Environmental variables
Environmental variation could impact breeding success by altering chick mortality, as in Thomson et al. (Thomson et al., 2015; Appendix C.4). Here, four environmental co-variates were created for use as model co-variates. We assess the average SST in the (i) foraging grounds near Kerguelen during the incubation (October – December) and (ii) rearing (January – April) period, and the (iii) SST during the winter prior to breeding (May – September) in areas of broad habitat use south of Australia, as well as a relatively intense area of concentrated use (iv)
northwest of Tasmania (Fig. 4.3). While environmental variation has been associated with the survival of adults (Rolland et al. 2010, Pardo et al. 2012) and inexperienced birds (Nevoux et al. 2010b), we only assess the impacts on chick mortality as the current model framework does not allow covariates on adult survival rate. The average SST across these regions for the given monthly time periods was calculated using NOAA ¼° monthly optimum interpolation dataset (Reynolds et al., 2008).
102 Figure 4.3 Location of black-browed albatross breeding colony on Kerguelen at Cañon des Sourcils Noirs
Figure 4.3: Location of black-browed albatross breeding colony on Kerguelen, at (triangle)
Cañon des Sourcils Noirs. The at-sea area defining average SST during the (solid lines)
incubation and rearing period and wintering period (dashed lines) south of Australia and (dotted lines) northwest of Tasmania are shown.