Caso Somalia: Los Derechos Humanos en Somalia

The difference in the amount of singing occurring in humpback whale feeding and breeding grounds is still debated (Mattila et al., 1987; Stimpert et al., 2012; Vu et al., 2012; Garland et al., 2013a; Stanistreet et al., 2013; Magnusdottir et al., 2015; Español-Jiménez and van der Schaar, 2018). Therefore I carried out a further test with model 3 to estimate the effect of singing probability during the feeding season on the emergence of song revolutions. The memory conservatism and the size of feeding and breeding grounds were set to 0.9 and 5 respectively, for all the 400 experiments run. Both movement scenarios 2 and 3 were tested with a feeding season singing probability of 0.08 (i.e. the original runs) and compared to results where the feeding season singing probability was equal to the breeding season, at 0.8 (Figure 3.14).

Compared to results from a low singing probability of 0.08 during the feeding season, a high singing probability (0.8) made revolutions much less likely to occur. In both scenarios there was an initial increase in 𝑆𝑅_𝐷 song frequency during the first feeding season; in few cases this resulted in 𝑆𝑅𝐷 song frequency spreading quickly to

87 all the agents of population E1 but in the majority of cases this was not enough to drive song revolutions. Before the end of the first feeding season the immigrant agent from population D conformed to 𝑆𝑅_𝐸1 song repertoire; subsequently, it either moved back to population D resulting, in a few cases, in inverse revolutions, in which 𝑆𝑅𝐸1 songs

diffused in population D (Scenario 2; Figure 3.14), or it just conformed to 𝑆𝑅_𝐸1

(Scenario 3; Figure 3.14). Hence increased singing probability on the feeding grounds reduced the occurrence of song revolutions dramatically.

Figure 3.14. High singing rates on feeding grounds suppresses song revolutions. 𝑆𝑅𝐷 song frequency compared across two parameter setting for each of the two movement scenarios (2 and 3). Black lines represented experiments in which the singing probability during the feeding season was the same as the one during the breeding season (0.8). Yellow lines represented experiments in which the singing

probability during the feeding season was one order of magnitude lower than the one during the breeding season (0.08). For both sets 𝑐 value was set at 0.9. Thick lines represent the median for each set of 100 experiments (thin lines). The light and dark grey areas represent breeding and feeding seasons

respectively. The size of the breeding and feeding ground was set to 5.

3.5.

Discussion

In this chapter I investigated how individual level movements, learning dynamics and biases could lead to humpback whale song revolutions, using spatially explicit agent-based models. I built upon the modelling framework and results of the previous chapter by introducing new geographical and movement scenarios, and giving agents both a memory of heard songs and a sense of how well or not their own songs

88 fitted with their current population context. I found that song revolutions could occur when learning was mediated by sound transmission loss only when significant portions (that is, at least 70%) of the two simulated populations spatially overlapped during the feeding season. If the spatial interaction between the two populations was constrained to the movement of individual agents, then song revolutions occurred only when agents were equipped with a song memory. In model experiments in which (1) agents were highly conservative towards pre-existing memories, (2) agents’ density in the feeding grounds was high, and (3) the feeding season singing probability was kept low, song revolutions emerged consistently.

3.5.1. Movement Scenario 1

The first movement scenario was designed to explore the potential emergence of song revolutions whilst making the fewest assumptions possible regarding any learning biases or cognitive capacities in humpback whales. For this reason, all model

experiments were run using learning mediated solely by distance (i.e. sound

transmission loss; model 1). The results showed that song revolutions can occur without any specific learning bias, depending on the interplay between agents’ spatial density in the breeding/feeding grounds, the ratio between the size of population D and E1 and proportion of migratory movements towards area IV by population E1’s agents. This only occurred however when at least 70% of the E1 population migrated to a shared feeding ground.

There were two major assumptions built into scenario 1. The first was that the population sizes estimated in the breeding grounds of West and east Australia (Hedley et al., 2011; Noad et al., 2011) corresponded to the population sizes in the respective feeding grounds (i.e. the populations did not disperse to multiple feeding grounds). Recent population estimates for area IV (2001/2002) and V (2004/2005) seemed to align to the breeding ground estimates showing breeding stock D more than twice the size of breeding stock E1 (Matsuoka et al., 2011). The second assumption, that large portions of the E1 population overlapped with breeding stock D in area IV, was less realistic. Robust evidence across multiple studies (discovery tags, photo-id) shows that the two populations predominantly migrate to different feeding areas (Chittleborough,

89 1965; Franklin et al., 2012; Constantine et al., 2014). Thus having 70% of one

population in another’s principal feeding ground seems a highly unlikely occurrence. However, there are also some considerations that have to be taken into account when interpreting movement scenario 1’s results. The distribution of baleen whales across the Antarctic tend to be tightly correlated with the Southern Boundary of the Antarctic Circumpolar Current (ACC) (Tynan, 1998). In area IV and V, the late summer distribution of humpback whales was correlated with offshore areas of high productivity coinciding with the Southern Boundary (Tynan, 1998; Murase et al., 2002; Weinstein et al., 2017); these highly suitable areas for humpback whales to feed can vary both within and between feeding seasons (Bombosch et al., 2014), potentially increasing the

chances for two adjacent populations to interact. Across the Western South Pacific, a consistent pattern of song transmission has emerged when a large population has overlapped on feeding and/or migratory areas with a smaller population: the song type produced by the larger population is usually transmitted to the smaller population within one feeding season. A good example of this yearly song transmission pattern was

represented by the interaction between the large eastern Australian population (N = 14,522, CI= 12,777 – 16,504; Noad et al., 2011) and the small and adjacent New

Caledonian population (N = 533, CV = 0.15; Garrigue et al., 2004). Song types recorded in the eastern Australian population were consistently recorded one year later, in New Caledonia (Garland et al., 2011; Garrigue et al., 2015). The fact that this type of yearly song transmission does not happen between the western and eastern Australian

populations would indicate that the spatial overlap between these two populations is inconsistent, but potentially driven by slower processes with multi-year cycles.

Large scale climatic processes such as ENSO (El Niño-Southern Oscillation) or/and Southern Annular Mode (Murphy et al., 2007) as well as intraspecific

competition (Ryabov et al., 2017) have a profound effect on Antarctic krill biomass cycles; these cyclic increases and decreases in krill biomass occur with a frequency of 5 to 6 years, with variations larger than one order of magnitude (Ryabov et al., 2017). Furthermore, a recent study reported an inter-annual synchronous oscillation of two measures of Area V humpback whales adiposity with environmental variables and climate indices (Nash et al., 2018). The two adiposity markers indicated that in the

90 summer of 2010/2011, which was preceded by positive modes of SAM and ENSO, whales encountered poor feeding conditions; based on dietary signals the authors suggested that whales, in response to lower prey availability, might have diversified either their feeding ground locations or their prey choice, or both (Nash et al., 2018). Lower prey availability might increase the frequency of the long-range movements that humpback whales have been shown to perform between foraging patches in Antarctica (Friedlaender et al., 2006; Dalla Rosa et al., 2008), resulting in poorer physical

conditions (Nash et al., 2018) and a higher probability of overlap with neighbouring populations. If the results of movement scenario 1 could be interpreted in terms of foraging patches, rather than the entire feeding ground, then the increased movement between low quality foraging patches where the song-scape was dominated by the larger breeding stock D could be at the origin of song revolutions in eastern Australia.

Furthermore, an additional demographic aspect should be taken into consideration. Since 2000, both breeding stocks D and E1 increased at an annual rate of ≈ 10-12% (Hedley et al., 2011; Noad et al., 2011; Salgado Kent et al., 2012). As population sizes continue to increase the probability of feeding ground spatial overlap might increase as well. If this were to happen I predict that song revolutions in the eastern Australian populations will become more frequent, eventually reaching a yearly song transmission pattern similar to the one currently observed between the eastern Australian and the New Caledonian population (Garland et al., 2011).