A Darwinian Uncertainty Principle.

Reconstructing ancestral characters and traits along a phylogenetic tree is central to evolutionary biology. It is the key to understanding morphology changes among species, inferring ancestral biochemical properties of life, or recovering migration routes in phylogeography. The goal is 2-fold: to reconstruct the character state at the tree root (e.g., the region of origin of some species) and to understand the process of state changes along the tree (e.g., species flow between countries). We deal here with discrete characters, which are "unique," as opposed to sequence characters (nucleotides or amino-acids), where we assume the same model for all the characters (or for large classes of characters with site-dependent models) and thus benefit from multiple information sources. In this framework, we use mathematics and simulations to demonstrate that although each goal can be achieved with high accuracy individually, it is generally impossible to accurately estimate both the root state and the rates of state changes along the tree branches, from the observed data at the tips of the tree. This is because the global rates of state changes along the branches that are optimal for the two estimation tasks have opposite trends, leading to a fundamental trade-off in accuracy. This inherent "Darwinian uncertainty principle" concerning the simultaneous estimation of "patterns" and "processes" governs ancestral reconstructions in biology. For certain tree shapes (typically speciation trees) the uncertainty of simultaneous estimation is reduced when more tips are present; however, for other tree shapes it does not (e.g., coalescent trees used in population genetics).

Synchrony erodes spatial portfolios of an anadromous fish and alters availability for resource users.

Environmental forces can create spatially synchronous dynamics among nearby populations. However, increased climate variability, driven by anthropogenic climate change, will likely enhance synchrony among spatially disparate populations. Population synchrony may lead to greater fluctuations in abundance, but the consequences of population synchrony across multiple scales of biological organization, including impacts to putative competitors, dependent predators or human communities, are rarely considered in this context. Chinook salmon Oncorhynchus tshawytscha stocks distribute across the Northeast Pacific, creating spatially variable portfolios that support large ocean fisheries and marine mammal predators, such as killer whales Orcinus orca. We rely on a multi-population model that simulates Chinook salmon ocean distribution and abundance to understand spatial portfolios, or variability in abundance within and among ocean distribution regions, of Chinook salmon stocks across 17 ocean regions from Southeast Alaska to California. We found the expected positive correlation between the number of stocks in an ocean region and spatial portfolio strength; however, increased demographic synchrony eroded Chinook salmon spatial portfolios in the ocean. Moreover, we observed decreased resource availability within ocean fishery management jurisdictions but not within killer whale summer habitat. We found a strong portfolio effect across both Southern Resident and Northern Resident killer whale habitats that was relatively unaffected by increased demographic synchrony, likely a result of the large spatial area included in these habitats. However, within the areas of smaller fishing management jurisdictions we found a weakening of Chinook salmon portfolios and increased but inconsistent likelihood of low abundance years as demographic synchrony increased. We suggest that management and conservation actions that reduce spatial synchrony can enhance short-term ecosystem resilience by promoting the stabilizing effect multiple stocks have on aggregate Chinook salmon populations and overall resource availability.

Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework.

AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.

Ecological patterns and processes of temporal turnover within lung infection microbiota.

BACKGROUND: Chronic infection and consequent airway inflammation are the leading causes of morbidity and early mortality for people living with cystic fibrosis (CF). However, lower airway infections across a range of chronic respiratory diseases, including in CF, do not follow classical 'one microbe, one disease' concepts of infection pathogenesis. Instead, they are comprised of diverse and temporally dynamic lung infection microbiota. Consequently, temporal dynamics need to be considered when attempting to associate lung microbiota with changes in disease status. Set within an island biogeography framework, we aimed to determine the ecological patterns and processes of temporal turnover within the lung microbiota of 30 paediatric and adult CF patients prospectively sampled over a 3-year period. Moreover, we aimed to ascertain the contributions of constituent chronic and intermittent colonizers on turnover within the wider microbiota. RESULTS: The lung microbiota within individual patients was partitioned into constituent chronic and intermittent colonizing groups using the Leeds criteria and visualised with persistence-abundance relationships. This revealed bacteria chronically infecting a patient were both persistent and common through time, whereas intermittently infecting taxa were infrequent and rare; respectively representing the resident and transient portions of the wider microbiota. It also indicated that the extent of chronic colonization was far greater than could be appreciated with microbiological culture alone. Using species-time relationships to measure temporal turnover and Vellend's rationalized ecological processes demonstrated turnover in the resident chronic infecting groups was conserved and underpinned principally by the deterministic process of homogenizing dispersal. Conversely, intermittent colonizing groups, representing newly arrived immigrants and transient species, drove turnover in the wider microbiota and were predominately underpinned by the stochastic process of drift. For adult patients, homogenizing dispersal and drift were found to be significantly associated with lung function. Where a greater frequency of homogenizing dispersal was observed with worsening lung function and conversely drift increased with better lung function. CONCLUSIONS: Our work provides a novel ecological framework for understanding the temporal dynamics of polymicrobial infection in CF that has translational potential to guide and improve therapeutic targeting of lung microbiota in CF and across a range of chronic airway diseases. Video Abstract.

Passive directed dispersal of plants by animals.

Conceptual gaps and imprecise terms and definitions may obscure the breadth of plant-animal dispersal relationships involved in directed dispersal. The term 'directed' indicates predictable delivery to favourable microsites. However, directed dispersal was initially considered uncommon in diffuse mutualisms (i.e. those involving many species), partly because plants rarely influence post-removal propagule fate without specialized adaptations. This rationale implies that donor plants play an active role in directed dispersal by manipulating vector behaviour after propagule removal. However, even in most classic examples of directed dispersal, participating plants do not influence animal behaviour after propagule removal. Instead, such plants may take advantage of vector attraction to favourable plant microsites, indicating a need to expand upon current interpretations of directed dispersal. We contend that directed dispersal can emerge whenever propagules are disproportionately delivered to favourable microsites as a result of predictably skewed vector behaviour. Thus, we propose distinguishing active and passive forms of directed dispersal. In active directed dispersal, the donor plant achieves disproportionate arrival to favourable microsites by influencing vector behaviour after propagule removal. By contrast, passive directed dispersal occurs when the donor plant takes advantage of vector behaviour to arrive at favourable microsites. Whereas predictable post-removal vector behaviour is dictated by characteristics of the donor plant in active directed dispersal, characteristics of the destination dictate predictable post-removal vector behaviour in passive directed dispersal. Importantly, this passive form of directed dispersal may emerge in more plant-animal dispersal relationships because specialized adaptations in donor plants that influence post-removal vector behaviour are not required. We explore the occurrence and consequences of passive directed dispersal using the unifying generalized gravity model of dispersal. This model successfully describes vectored dispersal by incorporating the influence of the environment (i.e. attractiveness of microsites) on vector movement. When applying gravity models to dispersal, the three components of Newton's gravity equation (i.e. gravitational force, object mass, and distance between centres of mass) become analogous to propagules moving towards a location based on characteristics of the donor plant, the destination, and relocation processes. The generalized gravity model predicts passive directed dispersal in plant-animal dispersal relationships when (i) animal vectors are predictably attracted to specific destinations, (ii) animal vectors disproportionately disperse propagules to those destinations, and (iii) those destinations are also favourable microsites for the dispersed plants. Our literature search produced evidence for these three conditions broadly, and we identified 13 distinct scenarios where passive directed dispersal likely occurs because vector behaviour is predictably skewed towards favourable microsites. We discuss the wide applicability of passive directed dispersal to plant-animal mutualisms and provide new insights into the vulnerability of those mutualisms to global change.

Broader Impacts for Ecologists: Biological Soil Crust as a Model System for Education.

Biological soil crusts (biocrusts) are a complex community of algae, cyanobacteria, lichens, bryophytes, and assorted bacteria, fungi, archaea, and bacteriophages that colonize the soil surface. Biocrusts are particularly common in drylands and are found in arid and semiarid ecosystems worldwide. While diminutive in size, biocrusts often cover large terrestrial areas, provide numerous ecosystem benefits, enhance biodiversity, and are found in multiple configurations and assemblages across different climate and disturbance regimes. Biocrusts have been a focus of many ecologists, especially those working in semiarid and arid lands, as biocrusts are foundational community members, play fundamental roles in ecosystem processes, and offer rare opportunities to study biological interactions at small and large spatial scales. Due to these same characteristics, biocrusts have the potential to serve as an excellent teaching tool. The purpose of this paper is to demonstrate the utility of biocrust communities as a model system in science education. Functioning as portable, dynamic mini ecosystems, biocrusts can be used to teach about organisms, biodiversity, biotic interactions, abiotic controls, ecosystem processes, and even global change, and can be easy to use in nearly every classroom setup. For example, education principles, such as evolution and adaptation to stress, or structure and function (patterns and processes) can be applied by bringing biocrusts into the classroom as a teaching tool. In addition, discussing the utility of biocrusts in the classroom - including theory, hypothesis testing, experimentation, and hands-on learning - this document also provides tips and resources for developing education tools and activities geared toward impactful learning.