1. A robust scientific conclusion is the result of a rigorous scientific process. In observational ecology, this process involves making inferences about a population from 20 a sample. The sample is crucial, and is the result of implementing a survey design. A good survey design ensures that the data from the survey is capable of answering the research question. An even better design, such as spatially balanced designs, will also aim to reduce uncertainty as far as budgets will allow. 2. In many study areas, there are `legacy sites', that already have a time-series observed, and return visits to these sites are beneficial to enhance examination of temporal variability. We propose a method to incorporate these legacy sites into the survey effort whilst also maintaining spatial balance. This is the first formal method to perform this task. 3. Simulation experiments indicate that incorporating the spatial location of legacy sites increases spatial balance and decreases uncertainty in inferences (smaller standard errors in mean estimates). We illustrate the process using a proposed survey of a large marine reserve in South-Eastern Australia, where quantification of the reserve's biodiversity is required. 4. Our approach allows for integration of legacy sites into a new spatially-balanced 35 design, increasing efficiency. Scientists, managers and funders alike will benefit from this methodology { it provides a tool to provide efficient survey designs around established ones. In this way, it can aid integrated monitoring programs. An R-package that implements these methods, called MBHdesign, is available from CRAN. <b>Citation:</b> Foster, S.D., Hosack, G.R., Lawrence, E., Przeslawski, R., Hedge, P., Caley, M.J., Barrett, N.S., Williams, A., Li, J., Lynch, T., Dambacher, J.M., Sweatman, H.P.A. and Hayes, K.R. (2017), Spatially balanced designs that incorporate legacy sites. <i>Methods Ecol Evol</i>, 8: 1433-1442. https://doi.org/10.1111/2041-210X.12782

Data is currently being used, and reused, in ecological research at unprecedented rates. To ensure appropriate reuse however, we need to ask the question: “Are aggregated databases currently providing the right information to enable effective and unbiased reuse?” We investigate this question, with a focus on designs that purposefully bias the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those that have unequal inclusion probabilities or are stratified. We perform a simulation experiment by creating datasets with progressively more bias, and examine the resulting statistical estimates. The effect of ignoring the survey design can be profound, with biases of up to 250% when naive analytical methods are used. The bias is not reduced by adding more data. Fortunately, the bias can be mitigated by using an appropriate estimator or an appropriate model. These are only applicable however, when essential information about the survey design is available: the randomisation structure (e.g. inclusion probabilities or stratification), and/or covariates used in the randomisation process. The results suggest that such information must be stored and served with the data to support inference and reuse. <b>Citation: </b>S.D. Foster, J. Vanhatalo, V.M. Trenkel, T. Schulz, E. Lawrence, R. Przeslawski, and G.R. Hosack. 2021. Effects of ignoring survey design information for data reuse. Ecological Applications 31(6): e02360. 10.1002/eap.2360