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Context: Dryland rivers are unique ecosystems, where drought and flood play an important role in shaping the ecosystem. River regulation has altered the natural flow regime in many of these systems, affecting migration cues and connectivity for many species. Aims: To quantify the discharge-related movements of Murray cod and golden perch within the Condamine–Balonne River subject to differing levels of river regulation. Methods: We quantified flow regime variability, river regulation and fish movement to develop generalised additive mixed models to predict movement probability for Murray cod and golden perch. Results: Both species showed strong positive relationships between discharge and movement. Murray cod did not show any association with river regulation; however, medium-sized individuals were significantly more likely to move than were smaller or larger fish. Golden perch movements varied among levels of regulation, were more likely to move as body weight increased and showed seasonality of movement, moving less during winter. Conclusions: This study presents the largely unobserved movement behaviours of fish across a gradient of river regulation and environmental conditions in the northern Murray–Darling Basin. Implications: This information is valuable for informing policy and management decisions that may affect species' life-history requirements in analogous river systems. Quantifying flow-related movements of Murray cod and golden perch subject to differing levels of flow regulation is important for water management decision-making. Our study observed that movement patterns for either species were driven by combinations of discharge, river regulation, season, and body size. Tailored management strategies for these fish species should be developed on the basis of location, river regulation and flow regime. This article belongs to the collection Environmental Flows in Northern Murray–Darling Basin: what we know about the science and management after a decade of practice. [ABSTRACT FROM AUTHOR]

Data from the DLR Earth Sensing Imaging Spectrometer (DESIS), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. Twenty DESIS images were used from three widely separated forested study sites representing deciduous and conifer forests. Gross primary production (GPP) values from eddy covariance flux towers at the sites were matched with DESIS spectral reflectances collected on the same days. Multiple algorithms were successful relating spectral reflectance with GPP, including: spectral vegetation indices (SVI) sensitive to chlorophyll content, SVI used in a photosynthetic light-use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to multiband hyperspectral indices from partial least squares regression. Successful algorithms were able to achieve R2 better than 0.7 using a diverse set of observations combining data from different sites from multiple years and at multiple times during the year. The demonstrated robustness of the algorithms provides some confidence in using DESIS imagery to map spatial patterns of GPP. [ABSTRACT FROM AUTHOR]