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Animal coloration serves a variety of visually related functions in nature (e.g. mate choice, aposematism and camouflage) but the pigments in integumentary tissues such as skin, scales and feathers may also serve functions unrelated to the visual environment (e.g. temperature regulation, detoxification and pollutant protection). Our understanding of the significance of the non‐visual functions of animal integumentary pigments, as well as how they interact with the visually related functions to shape animal visual systems, remains limited. Furthermore, due to their important roles in shaping species interactions and mediating interactions in the environment, animal colour traits are likely to be impacted by global change (e.g. increased temperatures, altered habitat quality and quantity, increased environmental stochasticity, pollutants and novel species assemblages). Considering the effects of global change on both visual and non‐visual functions is important for understanding whether the selection is acting directly on the pigment or on coloration. Since changing the trait distributions can then lead to changes in visual systems, we advocate for studies to consider all potential functions of integumentary pigments, both visual and non‐visual functions and their interaction. Towards this goal, we first highlight common functions of pigments with a focus on non‐visual functions across animal systems. Then we synthesize our current understanding of how global change can impact pigmentation and discuss factors that can modify the interactions between climate change and pigment function. Lastly, we discuss how changes in colour traits can impact visual systems and provide an example using amphibians and their responses to climate change as a model. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

An organism's body shape is influenced by its ecology and environment through selective pressure and phenotypic plasticity. The shape of turtle shells is impacted by locomotor efficiency, and shell shape has been shown to differ between populations occupying different water flow regimes. However, it is not known whether other aspects of the environment, such as water turbidity or plant density, similarly impact shape by influencing locomotor efficiency. We used geometric morphometric methods to analyze the shell shapes of Painted Turtles (Chrysemys picta) from several populations in Wisconsin. Water turbidity was found to be correlated with different shell shapes among the populations. In more turbid water, turtle shells were narrower than shells in clear water, and there was an interactive effect between water turbidity and plant density such that shells were anteriorly taller in high plant densities with high water turbidity than in low water turbidity. Shell shape also differed between the sexes and varied with adult size. These correlations suggest that aspects of the aquatic environment in addition to water flow may impact turtle shell shape by influencing locomotor efficiency and that this variation is detectable even within a species and region. [ABSTRACT FROM AUTHOR]

The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.)