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This dataset contains biodiversity and landscape data of 100 US metropolitans and 1-km cells, relating to the paper Detecting biodiversity patterns across a multidimensional urban landscape for the journal Ecology Letters 

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science, and drug discovery. This paper develops a novel algorithm for optimizing molecular properties via an Expectation- Maximization (EM) like explainable evolutionary process. The algorithm is designed to mimic human experts in the process of searching for desirable molecules and alternate between two stages: the first stage on explainable local search which identifies rationales, i.e., critical subgraph patterns accounting for desired molecular properties, and the second stage on molecule completion which explores the larger space of molecules containing good rationales. We test our approach against various baselines on a real-world multi-property optimization task where each method is given the same number of queries to the property oracle. We show that our evolution-by-explanation algorithm is 79% better than the best baseline in terms of a generic metric combining aspects such as success rate, novelty, and diversity. Human expert evaluation on optimized molecules shows that 60% of top molecules obtained from our methods are deemed successful. 

Explicit characterisation of the complexity of urban landscapes is critical for understanding patterns of biodiversity and for detecting the underlying social and ecological processes that shape them. Urban environments exhibit variable heterogeneity and connectivity, influenced by different historical contingencies, that affect community assembly across scales. The multidimensional nature of urban disturbance and co‐occurrence of multiple stressors can cause synergistic effects leading to nonlinear responses in populations and communities. Yet, current research design of urban ecology and evolutionary studies typically relies on simple representation of the parameter space that can be observed. Sampling approaches apply simple urban gradients such as linear transects in space or comparisons of urban sites across the urban mosaic accounting for a few variables. This rarely considers multiple dimensions and scales of biodiversity, and proves to be inadequate to explain observed patterns. We apply a multidimensional approach that integrates distinctive social, ecological and built characteristics of urban landscapes, representing variations along dimensions of heterogeneity, connectivity and historical contingency. Measuring species richness and beta diversity across 100 US metropolitan areas at the city and 1‐km scales, we show that distinctive signatures of urban biodiversity can result from interactions between socioecological heterogeneity and connectivity, mediated by historical contingency.