In 2011, we observed many dead birds on the cliffs when we arrived at the colony. At that time, the most experienced observer (K.H. Elliott) considered that the density of mosquitoes was the greatest he had seen in 10 years of visits. On our study plots, we observed a minimum of 22 carcasses ci out of 878 breeding birds (2.5%).
Researchers found unusually high bird mortality upon arrival, signaling a potential environmental or biological disturbance that year.
We report here on recent observations of simultaneous parasitism and predation effects on breeding seabirds, including the effects of competition and commensalism among predators, as an example of interacting stressors caused by ongoing climate change. Multiple interactive climate-driven changes are likely to occur as climate change proceeds, but to date the interaction of different climate-associated factors has not been described in detail for specific ecosystems
This shows how different ecological interactions occur together and are influenced by climate change. It creates the idea that multiple stressors can act at the same time, demonstrating the complex effects of environmental change on species.
Of the two hypotheses proposed to explain lunar activity patterns – the predation risk hypothesis and the visual acuity hypothesis – neither was completely supported by our results. The predation risk hypothesis predicts that, if predation is more successful under bright moonlight, prey species will become lunarphobic by reducing full moon activity. Although 50% of the prey species exhibited the reduction in full moon activity predicted by the predation risk model, the remaining species were either lunarphilic or exhibited no lunar activity pattern.
Researchers tested two ideas about how moonlight affects animal behavior but didn’t find full support for either. Only half of the prey acted as expected others were more active or showed no pattern at all.
Lunar activity patterns of coyote, ocelot, and puma were significantly non-random by Rao’s spacing test (coyote: U2136 = 231.0, P < 0.001; ocelot: U505 = 182.0, P < 0.001; puma: U633 = 189.1, P < 0.001), while the Rao’s P-values for the activity patterns of predators with smaller sample sizes were non-significant (tayra: U26 = 129.8, NS; oncilla: U157 = 142.5, NS; margay: U25 = 158.8, NS; jaguar: U46 = 15.0, NS; Table 4, Figure 5).
Statistical tests showed that only coyote, ocelot, and puma had meaningful activity patterns. The others didn’t, likely due to small sample sizes.
. One explanation is that brighter moonlight is associated with increased risk of predation (Predation Risk hypothesis), but it has also been proposed that nocturnal activity may be influenced by the sensory ecology of a species, with species that rely on visual detection of food and danger predicted to increase their activity during bright moonlight, while species relying on non-visual senses should decrease activity (Visual Acuity hypothesis).
This explains two different hypotheses about how moonlight affects nocturnal animal behavior. The Predation Risk hypothesis suggests that animals reduce activity during bright moonlight to avoid predators that can see them easily. The Visual Acuity hypothesis argues that species that depend on sight to find food or detect danger become more active when the moon is bright, while those relying on other senses become less active.
However, such experiments are not always feasible for all organisms, for a number of reasons, including high mortalities under laboratory conditions, long-lived or endangered species.
What can we do to make more organisms feasible for these type of experiments?
However, the confounding processes previously mentioned cannot be ruled out based solely on these experiments and a direct estimation of the genetic determination is needed (de Villemereuil et al., 2016).
I like this statement because it reminds us that the experiment is not enough to prove adaptation because of other factors.
The two species of mussels responded differently, to warming and pCO2. The native mussel T. hirsuta grew more under warming (Fig. 1; ANOVA Species × Temperature F1,32 = 6.13, P < 0.05; Supplementary Table 2). In contrast, M. galloprovincialis grew the same at ambient and elevated temperatures (Fig. 1; Supplementary Table 2). There was no effect of elevated pCO2 on growth in either of the mussel species (ANOVA CO2 F1,32 = 0.53, P > 0.05; Supplementary Table 2).
This shows us that M. galloprovincialis was not affected by the temperatures which can lead to a conclusion that temperatures does not affect them. Also, that they can thrive better no matter if the temperatures warm up.