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Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences (D) (DY and DIWR for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha-1) and IWR (137 mm). DY variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions.

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998–2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference ( r AD ¯ ) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a r AD ¯ of 0.7–12.1%, with high values especially for areas with pronounced differences in elevation. The r AD ¯ of soil data was higher (5.6–26.3%) than r AD ¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed ( r AD ¯ = 14.5%) than irrigated conditions ( r AD ¯ = 3.0%). The r AD ¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

Despite the proliferation of connectivity modelling approaches, static models have limited usefulness for decision-making by policy-makers and land managers, particularly where significant changes in land uses might be expected into the future. This study presents a flexible, scenario-based approach for modelling fine-scaled connectivity using graph-theory with least-cost paths for modelling connectivity at the regional scale and circuit theory at the local scale. The method allows for the assessment of a range of scenarios based on varying land use practices. Using the Lower Hunter region, Australia as a case study we tested five scenarios that describe the impact of different development choices on connectivity, ranging from high rates of urbanisation to revegetation of a designated green corridor. The changes in connectivity from the current state were assessed by visualising component boundaries and link locations and calculating patch- and landscape-scale graph metrics. In the Lower Hunter we found the green corridor scenario increased connectivity both visually and quantitatively, as shown by a 105% increase in the integral index of connectivity (IIC) which measures habitat availability (reachability) at the landscape scale. In contrast the urbanisation scenario resulted in a decrease in connectivity, with a 39% decrease in the IIC. The approach outlined in this paper is flexible, enabling a range of interests to be included, depending on the datasets available and the issues that need to be addressed. Such methods can be readily and rapidly applied by consultants or government agencies, in this region and elsewhere, to incorporate connectivity modelling into development plans.

Fine-scale landscape features such as scattered trees are increasingly thought to be critical for dispersal, and need to be considered in connectivity modelling and planning. Yet existing modelling approaches struggle to adequately take fine-scale features and threshold dynamics of dispersal behaviour into account, in part because of computational limitations. We present a framework for modelling connectivity at fine spatial resolutions over large spatial extents. Our framework involves a novel approach to characterising fine-scale dispersal behaviour within the context of existing modelling methods, and uses key parameters of dispersal behaviour to link models and their interpretation at multiple scales. We address computational limitations by creating a gap-crossing threshold layer, which identifies areas where dispersal is possible because of the presence and spacing of fine-scale connectivity elements. This layer is combined with a dispersal-cost layer within a graph-network analysis to identify the optimal least-cost path between patches. Graph metrics are used to assess the importance of specific patches at the regional-scale and to describe connectivity for the whole landscape. A local-scale connectivity model using the Circuitscape software complements the regional analysis outputs by considering all possible pathways across a landscape simultaneously rather than a single least-cost path. The framework was designed specifically to be applied by land use planners who need to quantify the impacts of property development on fine-scale connectivity, yet need to assess implications at the regional scale. We demonstrate the framework by applying it in the Lower Hunter region, Australia.

Behavioural thermoregulation has the potential to alleviate the short-term impacts of climate change on some small ectotherms, without the need for changes to species distributions or genetic adaptation. We illustrate this by measuring the effect of behaviour in a cool temperate species of grasshopper (Phaulacridium vittatum) over a range of spatial and temporal scales in laboratory and natural field experiments. Microhabitat selection at the site scale was tested in free-ranging grasshoppers and related to changing thermal quality over a daily period. Artificial warming experiments were then used to measure the temperature at which common thermoregulatory behaviours are initiated and the subsequent reductions in body temperature. Behavioural means such as timing of activity, choice of substrates with optimum surface temperatures, shade seeking and postural adjustments (e.g. stilting, vertical orientation) were found to be highly effective at maintaining preferred body temperature. The maximum voluntarily tolerated temperature (MVT) was determined to be 44°C±0.4°C, indicating the upper bounds of thermal flexibility in this species. Behavioural thermoregulation effectively enables small ectotherms to regulate exposure to changing environmental temperatures and utilize the spatially and temporally heterogeneous environments they occupy. Species such as the wingless grasshopper, although adapted to cool temperate conditions, are likely to be well equipped to respond successfully to coarse scale climate change.