Mycena species can be opportunist-generalist plant root invaders.

Traditional strict separation of fungi into ecological niches as mutualist, parasite or saprotroph is increasingly called into question. Sequences of assumed saprotrophs have been amplified from plant root interiors, and several saprotrophic genera can invade and interact with host plants in laboratory growth experiments. However, it is uncertain if root invasion by saprotrophic fungi is a widespread phenomenon and if laboratory interactions mirror field conditions. Here, we focused on the widespread and speciose saprotrophic genus Mycena and performed (1) a systematic survey of their occurrences (in ITS1/ITS2 datasets) in mycorrhizal roots of 10 plant species, and (2) an analysis of natural abundances of 13 C/15 N stable isotope signatures of Mycena basidiocarps from five field locations to examine their trophic status. We found that Mycena was the only saprotrophic genus consistently found in 9 out of 10 plant host roots, with no indication that the host roots were senescent or otherwise vulnerable. Furthermore, Mycena basidiocarps displayed isotopic signatures consistent with published 13 C/15 N profiles of both saprotrophic and mutualistic lifestyles, supporting earlier laboratory-based studies. We argue that Mycena are widespread latent invaders of healthy plant roots and that Mycena species may form a spectrum of interactions besides saprotrophy also in the field.

Strong altitudinal partitioning in the distributions of ectomycorrhizal fungi along a short (300 m) elevation gradient.

Changes in species richness and distributions of ectomycorrhizal (ECM) fungal communities along altitudinal gradients have been attributed to changes in both host distributions and abiotic variables. However, few studies have considered altitudinal relationships of ECM fungi associated with a single host to identify the role of abiotic drivers. To address this, ECM fungal communities associated with one host were assessed along five altitudinal transects in Scotland. Roots of Scots pine (Pinus sylvestris) were collected from sites between 300 and 550-600 m altitude, and ECM fungal communities were identified by 454 pyrosequencing of the fungal internal transcribed spacer (ITS) region. Soil moisture, temperature, pH, carbon : nitrogen (C : N) ratio and organic matter content were measured as potential predictors of fungal species richness and community composition. Altitude did not affect species richness of ECM fungal communities, but strongly influenced fungal community composition. Shifts in community composition along the altitudinal gradient were most clearly related to changes in soil moisture and temperature. Our results show that a 300 m altitudinal gradient produced distinct shifts in ECM fungal communities associated with a single host, and that this pattern was strongly related to climatic variables. This finding suggests significant climatic niche partitioning among ECM fungal species.

A new approach to characterising and predicting crop rotations using national-scale annual crop maps.

Cropping decisions affect the nature, timing and intensity of agricultural management strategies. Specific crop rotations are associated with different environmental impacts, which can be beneficial or detrimental. The ability to map, characterise and accurately predict rotations enables targeting of mitigation measures where most needed and forecasting of potential environmental risks. Using six years of the national UKCEH Land Cover® plus: Crops maps (2015-2020), we extracted crop sequences for every agricultural field parcel in Great Britain (GB). Our aims were to first characterise spatial patterns in rotation properties over a national scale based on their length, type and structural diversity values, second, to test an approach to predicting the next crop in a rotation, using transition probability matrices, and third, to test these predictions at a range of spatial scales. Strict cyclical rotations only occupy 16 % of all agricultural land, whereas long-term grassland and complex-rotational agriculture each occupy over 40 %. Our rotation classifications display a variety of distinctive spatial patterns among rotation lengths, types and diversity values. Rotations are mostly 5 years in length, short mixed crops are the most abundant rotation type, and high structural diversity is concentrated in east Scotland. Predictions were most accurate when using the most local spatial approach (spatial scaling), 5-year rotations, and including long-term grassland. The prediction framework we built demonstrates that our crop predictions have an accuracy of 36-89 %, equivalent to classification accuracy of national crop and land cover mapping using earth observation, and we suggest this could be improved with additional contextual data. Our results emphasise that rotation complexity is multi-faceted, yet it can be mapped in different ways and forms the basis for further exploration in and beyond agronomy, ecology, and other disciplines.

ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology.

Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

Long-term effects of atmospheric deposition on British plant species richness.

The effects of atmospheric pollution on plant species richness (nsp) are of widespread concern. We carried out a modelling exercise to estimate how nsp in British semi-natural ecosystems responded to atmospheric deposition of nitrogen (Ndep) and sulphur (Sdep) between 1800 and 2010. We derived a simple four-parameter equation relating nsp to measured soil pH, and to net primary productivity (NPP), calculated with the N14CP ecosystem model. Parameters were estimated from a large data set (n = 1156) of species richness in four vegetation classes, unimproved grassland, dwarf shrub heath, peatland, and broadleaved woodland, obtained in 2007. The equation performed reasonably well in comparisons with independent observations of nsp. We used the equation, in combination with modelled estimates of NPP (from N14CP) and soil pH (from the CHUM-AM hydrochemical model), to calculate changes in average nsp over time at seven sites across Britain, assuming that variations in nsp were due only to variations in atmospheric deposition. At two of the sites, two vegetation classes were present, making a total of nine site/vegetation combinations. In four cases, nsp was affected about equally by pH and NPP, while in another four the effect of pH was dominant. The ninth site, a chalk grassland, was affected only by NPP, since soil pH was assumed constant. Our analysis suggests that the combination of increased NPP, due to fertilization by Ndep, and decreased soil pH, primarily due to Sdep, caused an average species loss of 39% (range 23-100%) between 1800 and the late 20th Century. The modelling suggests that in recent years nsp has begun to increase, almost entirely due to reductions in Sdep and consequent increases in soil pH, but there are also indications of recent slight recovery from the eutrophying effects of Ndep.

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition.

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground-based field survey. Financial and logistical constraints mean that on-the-ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km2 (Zi ) is unobserved, but both ground survey and remote sensing can be used to estimate Zi . The model allows the relationship between remote sensing data and Zi to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model-based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.

Model-based hypervolumes for complex ecological data.

Developing a holistic understanding of the ecosystem impacts of global change requires methods that can quantify the interactions among multiple response variables. One approach is to generate high dimensional spaces, or hypervolumes, to answer ecological questions in a multivariate context. A range of statistical methods has been applied to construct hypervolumes but have not yet been applied in the context of ecological data sets with spatial or temporal structure, for example, where the data are nested or demonstrate temporal autocorrelation. We outline an approach to account for data structure in quantifying hypervolumes based on the multivariate normal distribution by including random effects. Using simulated data, we show that failing to account for structure in data can lead to biased estimates of hypervolume properties in certain contexts. We then illustrate the utility of these "model-based hypervolumes" in providing new insights into a case study of afforestation effects on ecosystem properties where the data has a nested structure. We demonstrate that the model-based generalization allows hypervolumes to be applied to a wide range of ecological data sets and questions.

Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts.

Quantitative models play an increasing role in exploring the impact of global change on biodiversity. To win credibility and trust, they need validating. We show how expert knowledge can be used to assess a large number of empirical species niche models constructed for the British vascular plant and bryophyte flora. Key outcomes were (a) scored assessments of each modeled species and niche axis combination, (b) guidance on models needing further development, (c) exploration of the trade-off between presenting more complex model summaries, which could lead to more thorough validation, versus the longer time these take to evaluate, (d) quantification of the internal consistency of expert opinion based on comparison of assessment scores made on a random subset of models evaluated by both experts. Overall, the experts assessed 39% of species and niche axis combinations to be "poor" and 61% to show a degree of reliability split between "moderate" (30%), "good" (25%), and "excellent" (6%). The two experts agreed in only 43% of cases, reaching greater consensus about poorer models and disagreeing most about models rated as better by either expert. This low agreement rate suggests that a greater number of experts is required to produce reliable assessments and to more fully understand the reasons underlying lack of consensus. While area under curve (AUC) statistics showed generally very good ability of the models to predict random hold-out samples of the data, there was no correspondence between these and the scores given by the experts and no apparent correlation between AUC and species prevalence. Crowd-sourcing further assessments by allowing web-based access to model fits is an obvious next step. To this end, we developed an online application for inspecting and evaluating the fit of each niche surface to its training data.