Can internal range structure predict range shifts?

Poleward and uphill range shifts are a common-but variable-response to climate change. We lack understanding regarding this interspecific variation; for example, functional traits show weak or mixed ability to predict range shifts. Characteristics of species' ranges may enhance prediction of range shifts. However, the explanatory power of many range characteristics-especially within-range abundance patterns-remains untested. Here, we introduce a hypothesis framework for predicting range-limit population trends and range shifts from the internal structure of the geographic range, specifically range edge hardness, defined as abundance within range edges relative to the whole range. The inertia hypothesis predicts that high edge abundance facilitates expansions along the leading range edge but creates inertia (either more individuals must disperse or perish) at the trailing range edge such that the trailing edge recedes slowly. In contrast, the limitation hypothesis suggests that hard range edges are the signature of strong limits (e.g. biotic interactions) that force faster contraction of the trailing edge but block expansions at the leading edge of the range. Using a long-term avian monitoring dataset from northern Minnesota, USA, we estimated population trends for 35 trailing-edge species and 18 leading-edge species and modelled their population trends as a function of range edge hardness derived from eBird data. We found limited evidence of associations between range edge hardness and range-limit population trends. Trailing-edge species with harder range edges were slightly more likely to be declining, demonstrating weak support for the limitation hypothesis. In contrast, leading-edge species with harder range edges were slightly more likely to be increasing, demonstrating weak support for the inertia hypothesis. These opposing results for the leading and trailing range edges might suggest that different mechanisms underpin range expansions and contractions, respectively. As data and state-of-the-art modelling efforts continue to proliferate, we will be ever better equipped to map abundance patterns within species' ranges, offering opportunities to anticipate range shifts through the lens of the geographic range.

Wild hummingbirds discriminate nonspectral colors.

Many animals have the potential to discriminate nonspectral colors. For humans, purple is the clearest example of a nonspectral color. It is perceived when two color cone types in the retina (blue and red) with nonadjacent spectral sensitivity curves are predominantly stimulated. Purple is considered nonspectral because no monochromatic light (such as from a rainbow) can evoke this simultaneous stimulation. Except in primates and bees, few behavioral experiments have directly examined nonspectral color discrimination, and little is known about nonspectral color perception in animals with more than three types of color photoreceptors. Birds have four color cone types (compared to three in humans) and might perceive additional nonspectral colors such as UV+red and UV+green. Can birds discriminate nonspectral colors, and are these colors behaviorally and ecologically relevant? Here, using comprehensive behavioral experiments, we show that wild hummingbirds can discriminate a variety of nonspectral colors. We also show that hummingbirds, relative to humans, likely perceive a greater proportion of natural colors as nonspectral. Our analysis of plumage and plant spectra reveals many colors that would be perceived as nonspectral by birds but not by humans: Birds' extra cone type allows them not just to see UV light but also to discriminate additional nonspectral colors. Our results support the idea that birds can distinguish colors throughout tetrachromatic color space and indicate that nonspectral color perception is vital for signaling and foraging. Since tetrachromacy appears to have evolved early in vertebrates, this capacity for rich nonspectral color perception is likely widespread.

Applying a deep learning pipeline to classify land cover from low-quality historical RGB imagery.

Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and class specificity. However, deploying deep learning algorithms to characterize present-day, modern land cover based on state-of-the-art data is insufficient for understanding trends in land cover change and identifying changes in and drivers of ecological and social variables of interest. These identifications require characterizing past land cover, for which imagery is often lower-quality. We applied a deep learning pipeline to classify land cover from historical, low-quality RGB aerial imagery, using a case study of Vancouver, Canada. We deployed an atrous convolutional neural network from DeepLabv3+ (which has previously shown to outperform other networks) and trained it on modern Maxar satellite imagery using a modern land cover classification. We fine-tuned the resultant model using a small dataset of manually annotated and augmented historical imagery. This final model accurately predicted historical land cover classification at rates similar to other studies that used high-quality imagery. These predictions indicate that Vancouver has lost vegetative cover from 1995-2021, including a decrease in conifer cover, an increase in pavement cover, and an overall decrease in tree and grass cover. Our workflow may be harnessed to understand historical land cover and identify land cover change in other regions and at other times.

I see your false colours: how artificial stimuli appear to different animal viewers.

The use of artificially coloured stimuli, especially to test hypotheses about sexual selection and anti-predator defence, has been common in behavioural ecology since the pioneering work of Tinbergen. To investigate the effects of colour on animal behaviour, many researchers use paints, markers and dyes to modify existing colours or to add colour to synthetic models. Because colour perception varies widely across species, it is critical to account for the signal receiver's vision when performing colour manipulations. To explore this, we applied 26 typical coloration products to different types of avian feathers. Next, we measured the artificially coloured feathers using two complementary techniques-spectrophotometry and digital ultraviolet--visible photography-and modelled their appearance to mammalian dichromats (ferret, dog), trichromats (honeybee, human) and avian tetrachromats (hummingbird, blue tit). Overall, artificial colours can have dramatic and sometimes unexpected effects on the reflectance properties of feathers, often differing based on feather type. The degree to which an artificial colour differs from the original colour greatly depends on an animal's visual system. 'White' paint to a human is not 'white' to a honeybee or blue tit. Based on our analysis, we offer practical guidelines for reducing the risk of introducing unintended effects when using artificial colours in behavioural experiments.

Camouflage and Clutch Survival in Plovers and Terns.

Animals achieve camouflage through a variety of mechanisms, of which background matching and disruptive coloration are likely the most common. Although many studies have investigated camouflage mechanisms using artificial stimuli and in lab experiments, less work has addressed camouflage in the wild. Here we examine egg camouflage in clutches laid by ground-nesting Snowy Plovers Charadrius nivosus and Least Terns Sternula antillarum breeding in mixed aggregations at Bahía de Ceuta, Sinaloa, Mexico. We obtained digital images of clutches laid by both species. We then calibrated the images and used custom computer software and edge detection algorithms to quantify measures related to three potential camouflage mechanisms: pattern complexity matching, disruptive effects and background color matching. Based on our image analyses, Snowy Plover clutches, in general, appeared to be more camouflaged than Least Tern clutches. Snowy Plover clutches also survived better than Least Tern clutches. Unexpectedly, variation in clutch survival was not explained by any measure of egg camouflage in either species. We conclude that measures of egg camouflage are poor predictors of clutch survival in this population. The behavior of the incubating parents may also affect clutch predation. Determining the significance of egg camouflage requires further testing using visual models and behavioral experiments.