Kaitlin C. Maguire

Has the Earth’s sixth mass extinction already arrived?

Palaeontologists characterize mass extinctions as times when the Earth loses more than three-quarters of its species in a geologically short interval, as has happened only five times in the past 540 million years or so. Biologists now suggest that a sixth mass extinction may be under way, given the known species losses over the past few centuries and millennia. Here we review how differences between fossil and modern data and the addition of recently available palaeontological information influence our understanding of the current extinction crisis. Our results confirm that current extinction rates are higher than would be expected from the fossil record, highlighting the need for effective conservation measures.

Prelude to the Anthropocene: Two new North American Land Mammal Ages (NALMAs)

Human impacts have left and are leaving distinctive imprints in the geological record. Here we show that in North America, the human-caused changes evident in the mammalian fossil record since c. 14,000 years ago are as pronounced as earlier faunal changes that subdivide Cenozoic epochs into the North American Land Mammal Ages (NALMAs). Accordingly, we define two new North American Land Mammal Ages, the Santarosean and the Saintagustinean, which subdivide Holocene time and complete a biochronologic system that has proven extremely useful in dating terrestrial deposits and in revealing major features of faunal change through the past 66 million years. The new NALMAs highlight human-induced changes to the Earth system, and inform the debate on whether or not defining an Anthropocene epoch is justified, and if so, when it began.

Controlled comparison of species- and community-level models across novel climates and communities.

Species distribution models (SDMs) assume species exist in isolation and do not influence one anothers distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.

Multiresponse algorithms for community‐level modelling: Review of theory, applications, and comparison to species distribution models

1.Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting biodiversity patterns. CLMs simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co-occurrence. Many CLMs are direct extensions of well-known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here we review this emerging field and provide examples in R to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits, and limitations relative to SDMs. 2.We review i) statistical implementations and applications of CLMs, ii) their advantages and limitations, and iii) comparative analyses of CLMs and SDMs. We also suggest directions for future research. 3.We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species-level modeling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. CLMs have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g., studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modeling rare species, and projecting to no-analog climates. A major shortcoming of CLMs is their reliance on presence-absence community composition data. 4.Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: i) under which circumstances CLMs improve predictions for rare species, ii) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co-occurrence patterns are structured by biotic interactions, and iii) ability to project across time/space. This article is protected by copyright. All rights reserved.

Correction to 'Controlled comparison of species- and community-level models across novel climates and communities'.

[ Proc. R. Soc. B 283 , 20152817. (16 March 2016; Published online 9 March 2016) ([doi:10.1098/rspb.2015.2817][2])][2] One of the six climate variables used to fit the models was listed incorrectly in the Environmental variables section under Material and methods [[1][2]]. Mean yearly