Dynamic species distribution models from categorical survey data.

1. Species distribution models are static models for the distribution of a species, based on Hutchinson's niche concept. They make probabilistic predictions about the distribution of a species, but do not have a temporal interpretation. In contrast, density-structured models based on categorical abundance data make it possible to incorporate population dynamics into species distribution modelling. 2. Using dynamic species distribution models, temporal aspects of a species' distribution can be investigated, including the predictability of future abundance categories and the expected persistence times of local populations, and how these may respond to environmental or anthropogenic drivers. 3. We built density-structured models for two intertidal marine invertebrates, the Lusitanian trochid gastropods Phorcus lineatus and Gibbula umbilicalis, based on 9 years of field data from around the United Kingdom. Abundances were recorded on a categorical scale, and stochastic models for year-to-year changes in abundance category were constructed with winter mean sea surface temperature (SST) and wave fetch (a measure of the exposure of a shore) as explanatory variables. 4. Both species were more likely to be present at sites with high SST, but differed in their responses to wave fetch. Phorcus lineatus had more predictable future abundance and longer expected persistence times than G. umbilicalis. This is consistent with the longer lifespan of P. lineatus. 5. Where data from multiple time points are available, dynamic species distribution models of the kind described here have many applications in population and conservation biology. These include allowing for changes over time when combining historical and contemporary data, and predicting how climate change might alter future abundance conditional on current distributions.

Comparative methods as a statistical fix: the dangers of ignoring an evolutionary model.

Abstract Comparative methods are widely used in ecology and evolution. The most frequently used comparative methods are based on an explicit evolutionary model. However, recent approaches have been popularized that are without an evolutionary basis or an underlying null model. Here we highlight the limitations of such techniques in comparative analyses by using simulations to compare two commonly used comparative methods with and without evolutionary basis, respectively: generalized least squares (GLS) and phylogenetic eigenvector regression (PVR). We find that GLS methods are more efficient at estimating model parameters and produce lower variance in parameter estimates, lower phylogenetic signal in residuals, and lower Type I error rates than PVR methods. These results can very likely be generalized to eigenvector methods that control for space and both space and phylogeny. We highlight that GLS methods can be adapted in numerous ways and that the variance structure used in these models can be flexibly optimized to each data set.

Phylogenetic conservatism of environmental niches in mammals.

Phylogenetic niche conservatism is the pattern where close relatives occupy similar niches, whereas distant relatives are more dissimilar. We suggest that niche conservatism will vary across clades in relation to their characteristics. Specifically, we investigate how conservatism of environmental niches varies among mammals according to their latitude, range size, body size and specialization. We use the Brownian rate parameter, σ(2), to measure the rate of evolution in key variables related to the ecological niche and define the more conserved group as the one with the slower rate of evolution. We find that tropical, small-ranged and specialized mammals have more conserved thermal niches than temperate, large-ranged or generalized mammals. Partitioning niche conservatism into its spatial and phylogenetic components, we find that spatial effects on niche variables are generally greater than phylogenetic effects. This suggests that recent evolution and dispersal have more influence on species' niches than more distant evolutionary events. These results have implications for our understanding of the role of niche conservatism in species richness patterns and for gauging the potential for species to adapt to global change.

Testing the ability of unmanned aerial systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds).

BACKGROUND: It is important to map agricultural weed populations to improve management and maintain future food security. Advances in data collection and statistical methodology have created new opportunities to aid in the mapping of weed populations. We set out to apply these new methodologies (unmanned aerial systems; UAS) and statistical techniques (convolutional neural networks; CNN) to the mapping of black-grass, a highly impactful weed in wheat fields in the UK. We tested this by undertaking extensive UAS and field-based mapping over the course of 2 years, in total collecting multispectral image data from 102 fields, with 76 providing informative data. We used these data to construct a vegetation index (VI), which we used to train a custom CNN model from scratch. We undertook a suite of data engineering techniques, such as balancing and cleaning to optimize performance of our metrics. We also investigate the transferability of the models from one field to another. RESULTS: The results show that our data collection methodology and implementation of CNN outperform pervious approaches in the literature. We show that data engineering to account for 'artefacts' in the image data increases our metrics significantly. We are not able to identify any traits that are shared between fields that result in high scores from our novel leave one field our cross validation (LOFO-CV) tests. CONCLUSION: We conclude that this evaluation procedure is a better estimation of real-world predictive value when compared with past studies. We conclude that by engineering the image data set into discrete classes of data quality we increase the prediction accuracy from the baseline model by 5% to an area under the curve (AUC) of 0.825. We find that the temporal effects studied here have no effect on our ability to model weed densities. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies.

Phylogenetic comparative methods are increasingly used to give new insights into the dynamics of trait evolution in deep time. For continuous traits the core of these methods is a suite of models that attempt to capture evolutionary patterns by extending the Brownian constant variance model. However, the properties of these models are often poorly understood, which can lead to the misinterpretation of results. Here we focus on one of these models - the Ornstein Uhlenbeck (OU) model. We show that the OU model is frequently incorrectly favoured over simpler models when using Likelihood ratio tests, and that many studies fitting this model use datasets that are small and prone to this problem. We also show that very small amounts of error in datasets can have profound effects on the inferences derived from OU models. Our results suggest that simulating fitted models and comparing with empirical results is critical when fitting OU and other extensions of the Brownian model. We conclude by making recommendations for best practice in fitting OU models in phylogenetic comparative analyses, and for interpreting the parameters of the OU model.

Biodiversity and Resilience of Ecosystem Functions.

Accelerating rates of environmental change and the continued loss of global biodiversity threaten functions and services delivered by ecosystems. Much ecosystem monitoring and management is focused on the provision of ecosystem functions and services under current environmental conditions, yet this could lead to inappropriate management guidance and undervaluation of the importance of biodiversity. The maintenance of ecosystem functions and services under substantial predicted future environmental change (i.e., their 'resilience') is crucial. Here we identify a range of mechanisms underpinning the resilience of ecosystem functions across three ecological scales. Although potentially less important in the short term, biodiversity, encompassing variation from within species to across landscapes, may be crucial for the longer-term resilience of ecosystem functions and the services that they underpin.

Megacycles of atmospheric carbon dioxide concentration correlate with fossil plant genome size.

Tectonic processes drive megacycles of atmospheric carbon dioxide (CO(2)) concentration, c(a), that force large fluctuations in global climate. With a period of several hundred million years, these megacycles have been linked to the evolution of vascular plants, but adaptation at the subcellular scale has been difficult to determine because fossils typically do not preserve this information. Here we show, after accounting for evolutionary relatedness using phylogenetic comparative methods, that plant nuclear genome size (measured as the haploid DNA amount) and the size of stomatal guard cells are correlated across a broad taxonomic range of extant species. This phylogenetic regression was used to estimate the mean genome size of fossil plants from the size of fossil stomata. For the last 400 Myr, spanning almost the full evolutionary history of vascular plants, we found a significant correlation between fossil plant genome size and c(a), modelled independently using geochemical data. The correlation is consistent with selection for stomatal size and genome size by c(a) as plants adapted towards optimal leaf gas exchange under a changing CO(2) regime. Our findings point to the possibility that major episodes of change in c(a) throughout Earth history might have selected for changes in genome size, influencing plant diversification.

Can phylogenetics identify C(4) origins and reversals?

Determining the direction of past transitions between adaptive traits is one of the major objectives of evolutionary biology. Insights can be gained from phylogenies, but violation of the assumptions of the statistical models used to reconstruct traits can result in severe biases and complementary evidence should be considered. Here, we review the weaknesses of relying solely on species phylogenies in reconstructing the evolutionary history of C(4) photosynthesis in grasses, a complex trait present in distinct phylogenetic groups. We argue that evolutionary transitions should be reconstructed by establishing the homology or convergence of the different states based on genetic and phenotypic analyses. Such an approach points to a predominance of C(4) gains over reversals to C(3) and we discuss potential explanations for this asymmetry in transition rates.

The role of ecological theory in microbial ecology.

Microbial ecology is currently undergoing a revolution, with repercussions spreading throughout microbiology, ecology and ecosystem science. The rapid accumulation of molecular data is uncovering vast diversity, abundant uncultivated microbial groups and novel microbial functions. This accumulation of data requires the application of theory to provide organization, structure, mechanistic insight and, ultimately, predictive power that is of practical value, but the application of theory in microbial ecology is currently very limited. Here we argue that the full potential of the ongoing revolution will not be realized if research is not directed and driven by theory, and that the generality of established ecological theory must be tested using microbial systems.