We present BonZeb-a suite of modular Bonsai packages which allow high-resolution zebrafish tracking with dynamic visual feedback. Bonsai is an increasingly popular software platform that is ...accelerating the standardization of experimental protocols within the neurosciences due to its speed, flexibility, and minimal programming overhead. BonZeb can be implemented into novel and existing Bonsai workflows for online behavioral tracking and offline tracking with batch processing. We demonstrate that BonZeb can run a variety of experimental configurations used for gaining insights into the neural mechanisms of zebrafish behavior. BonZeb supports head-fixed closed-loop and free-swimming virtual open-loop assays as well as multi-animal tracking, optogenetic stimulation, and calcium imaging during behavior. The combined performance, ease of use and versatility of BonZeb opens new experimental avenues for researchers seeking high-resolution behavioral tracking of larval zebrafish.
Animal sensory systems are tightly adapted to the demands of their environment. In the visual domain, research has shown that many species have circuits and systems that exploit statistical ...regularities in natural visual signals. The zebrafish is a popular model animal in visual neuroscience, but relatively little quantitative data is available about the visual properties of the aquatic habitats where zebrafish reside, as compared to terrestrial environments. Improving our understanding of the visual demands of the aquatic habitats of zebrafish can enhance the insights about sensory neuroscience yielded by this model system. We analyzed a video dataset of zebrafish habitats captured by a stationary camera and compared this dataset to videos of terrestrial scenes in the same geographic area. Our analysis of the spatiotemporal structure in these videos suggests that zebrafish habitats are characterized by low visual contrast and strong motion when compared to terrestrial environments. Similar to terrestrial environments, zebrafish habitats tended to be dominated by dark contrasts, particularly in the lower visual field. We discuss how these properties of the visual environment can inform the study of zebrafish visual behavior and neural processing and, by extension, can inform our understanding of the vertebrate brain.
Summary
Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non‐destructive estimates of leaf traits, ...but it remains unclear whether general trait‐spectra models can yield accurate estimates across functional groups and ecosystems.
We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least‐squares regression (PLSR) to build empirical models for estimating traits from spectra.
Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55–0.85; %RMSE = 12.7–19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy.
We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Animals benefit from knowing if and how they are moving. Across the animal kingdom, sensory information in the form of optic flow over the visual field is used to estimate self-motion. However, ...different species exhibit strong spatial biases in how they use optic flow. Here, we show computationally that noisy natural environments favor visual systems that extract spatially biased samples of optic flow when estimating self-motion. The performance associated with these biases, however, depends on interactions between the environment and the animal's brain and behavior. Using the larval zebrafish as a model, we recorded natural optic flow associated with swimming trajectories in the animal's habitat with an omnidirectional camera mounted on a mechanical arm. An analysis of these flow fields suggests that lateral regions of the lower visual field are most informative about swimming speed. This pattern is consistent with the recent findings that zebrafish optomotor responses are preferentially driven by optic flow in the lateral lower visual field, which we extend with behavioral results from a high-resolution spherical arena. Spatial biases in optic-flow sampling are likely pervasive because they are an effective strategy for determining self-motion in noisy natural environments.
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