Priority sites for wildfowl conservation in Mexico

A set of priority sites for wildfowl conservation in Mexico was determined using contemporary count data (1991–2000) from the U.S. Fish & Wildlife Service mid-winter surveys. We used a complementarity approach implemented through linear integer programming that addresses particular conservation concerns for every species included in the analysis and large fluctuations in numbers through time. A set of 31 priority sites was identified, which held more than 69% of the mid-winter count total in Mexico during all surveyed years. Six sites were in the northern highlands, 12 in the central highlands, six on the Gulf of Mexico coast and seven on the upper Pacific coast. Twenty-two sites from the priority set have previously been identified as qualifying for designation as wetlands of international importance under the Ramsar Convention and 20 sites are classified as Important Areas for Bird Conservation in Mexico. The information presented here provides an accountable, spatially-explicit, numerical basis for ongoing conservation planning efforts in Mexico, which can be used to improve existing wildfowl conservation networks in the country and can also be useful for conservation planning exercises elsewhere

LakeEnsemblR: an R package that facilitates ensemble modelling of lakes

Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes