Planetarium mergorum:
Seabirds as a global laboratory for statistical ecology

Collectively, the seabird guild envelops the planet, from the equator to the poles. Individual species can span entire hemispheres, responding to both terrestrial and aquatic environments. They are also an emblematic taxon, including some of conservation’s most charismatic poster-children. As a result, seabirds have attracted generations of professional and amateur scientists, leaving an extraordinary legacy of diverse ecological datasets. 
I will take you on a statistical journey through a series of ecological mysteries, where the complexity of the questions has prompted the development of bespoke—but generalisable—statistical approaches. How do we combine multiple data types to model the climatic future of interconnected metapopulations? How can light-level geolocation reveal where animals travel during vast migrations? What forms of density dependence shape populations, and how can we disentangle them? How can we explain and predict the global spread of avian flu through wildlife? And what can remarkably sparse mark–recapture data tell us about connectivity across networks of colonies? 
I will weave a thread through these and other questions, towards a more holistic statistical ecology of seabird communities—and the signals they may provide about the health of our planet.

Systematic conservation planning in Mexico: addressing data gaps, biases, and pressures

The National Commission for the Knowledge and Use of Biodiversity (CONABIO) was established in Mexico 34 years ago with the primary mandate of creating the National Biodiversity Information System. A key component of this system is the continuous expansion and curation of primary biodiversity records, each of which should include, at a minimum, the scientific name of the species, georeferenced locality data, the date of collection or observation, and the identity of the collector or observer. Currently, the system holds over 40 million such records covering various groups of flora, fauna, and fungi. However, given the country’s immense biological diversity, significant data and information biases persist. Nevertheless, this information remains invaluable for supporting public policy decision-making. One such application involves identifying gaps and omissions in biodiversity conservation, which need to be assessed within the national context; in recent decades, the country has experienced significant changes in the pressures and threats affecting biodiversity, alongside advances in  knowledge regarding species and ecosystem distributions, ecological dynamics, vulnerability, and the impacts of these pressures. In this context, prioritization analyses aim to incorporate the latest data on Mexican biodiversity, integrate emerging pressure factors, and leverage advances in systematic conservation planning tools. This overview highlights key aspects of data management within these analyses, which seek to identify areas of critical importance for the protection, restoration, and sustainable management of Mexican ecosystems. By maximizing biodiversity representation and incorporating key socio-ecological barriers and opportunities, these analyses aim to guide informed decision-making in support of Mexico’s national and international commitments.

Recursive Computing for Ecological Models

Bayesian modeling has become invaluable for ecologists. It allows us to formally consider ecological processes while accommodating uncertainty about data collection and our understanding of mechanisms. Several excellent software packages are available for fitting Bayesian models to data and are being applied every day to analyze ecological data. The adoption of Bayesian methods has led to increasingly complex models for answering tough questions using large and varied data sets. Bigger models and data sets lead to computing bottlenecks, but the structure of Bayesian models allows us to rearrange them so that we can perform computing in stages. Recursive Bayesian approaches can save us time by leveraging the parallel architecture of modern computers. A conditional perspective allows us to see Bayesian models in a way that facilitates multistage computing. I will demonstrate the recursive procedure with a set of examples including spatial and spatio-temporal models for ecological data.

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