Computational protocol: Can Static Habitat Protection Encompass Critical Areas for Highly Mobile Marine Top Predators? Insights from Coastal East Africa

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Protocol publication

[…] We implemented the information-theoretic approach to evaluate competing models by assessing their relative support in relation to observed data, rather than using the best single model approach []. Models were constructed for all possible combinations of explanatory variables and then ranked depending on the support of each of these models using the AIC values and the Akaike weight []. The Akaike weight of each model is the relative likelihood of that model compared with the remaining models and was used to identify the 95% confidence set of models. To identify the 95% confidence set, we selected the model with the highest Akaike weight and added the models with the next highest weights until the cumulative Akaike weights > 0.95. When the model with lowest AIC value has an Akaike weight value lower than 0.9, a model averaging procedure might be more appropriate to account for model and parameter uncertainty []. The model averaged predictions were expected to be more robust than those from single best model approach. Averaged coefficients were estimated using the MuMIn package [] [...] Species distribution data are characterised by spatial autocorrelation since distribution data in close location are more similar than would be expected in randomly distributed data []. Significant spatial autocorrelation can invalidate the common assumption that observations are independent, and identify spurious significant relationships (Type I error) []. Spatial autocorrelation was checked on the residuals of the model with the lowest AIC using the Moran’s I index [] and spatial correlograms with the ‘ncf’ package []. The Moran’s I index ranges from -1 (negative autocorrelation—perfect dispersion) to +1 (positive autocorrelation—perfect correlation), with values around zero being indicative of random spatial patterns []. The spatial correlogram estimate the spatial dependence through testing significance within each distance class by a randomization test []. We did not include any spatial autocorrelation structure in our models since we did not find significant spatial autocorrelation (). […]

Pipeline specifications

Software tools MuMIn, Spdep
Applications Miscellaneous, Phylogenetics
Organisms Tursiops aduncus