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.
With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species’ potential ...and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.
Integrated modeling of species distributions and abundance is emerging as a powerful tool in statistical ecology.Point processes provide a flexible framework for developing integrated models, combining data representing the locations of individual organisms, local population abundance, and species–site occupancy.These methods provide opportunities to make best use of existing and new data sources.We expect that data integration will underpin the next generation of models predicting the current, future, and potential distributions of species.
Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each ...data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence‐only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence‐only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.
Species distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions ...change over both time and space. Spatio‐temporal models require large amounts of data spread over time and space, and as such are clear candidates to benefit from model‐based integration of different data sources. However, spatio‐temporal models are highly computationally intensive and integrating different data sources can make this approach even more unfeasible to ecologists.
Here we demonstrate how the R‐INLA methodology can be used for model‐based data integration for spatio‐temporally explicit modelling of species distribution change. We demonstrate that this method can be applied to both point and areal data with two contrasting case studies, one using the SPDE approach for modelling spatio‐temporal change in the Gatekeeper butterfly (Pyronia tithonus) across Great Britain and the second using a spatio‐temporal areal model to describe change in caddisfly (Trichoptera) populations across the River Thames catchment.
We show that in the caddisfly case study integrating together different data sources led to greater understanding of the change in abundance across the River Thames both seasonally and over 5 years of data. However, in the butterfly case study moving to a spatio‐temporal context exacerbated differences between the data sources and resulted in no greater ecological insight into change in the Gatekeeper population.
Our work provides a computationally feasible framework for spatio‐temporally explicit integration of data within SDMs and demonstrates both the potential benefits and the challenges in applying this methodology to real ecological data.
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
C/
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
C/
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.
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.
Integrated species distribution models Suhaimi, Siti Sarah Ahmad; Blair, Gordon S.; Jarvis, Susan G.
Diversity & distributions,
06/2021, Letnik:
27, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Aim
Integrated species distribution modelling has emerged as a useful tool for ecologists to exploit the range of information available on where species occur. In particular, the ability to combine ...large numbers of ad hoc or presence‐only (PO) records with more structured presence–absence (PA) data can allow ecologists to account for biases in PO data which often confound modelling efforts. A range of modelling techniques have been suggested to implement integrated species distribution models (IDMs) including joint likelihood models, including one dataset as a covariate or informative prior, and fitting a correlation structure between datasets. We aim to investigate the performance of different types of integrated models under realistic ecological data scenarios.
Innovation
We use a virtual ecologist approach to investigate which integrated model is most advantageous under varying levels of spatial bias in PO data, sample size of PA data and spatial overlap between datasets.
Main conclusions
Joint likelihood models were the best performing models when spatial bias in PO data was low, or could be modelled, but gave poor estimates when there were unknown biases in the data. Correlation models provided good model estimates even when there were unknown biases and when good quality PA data were spatially limited. Including PO data via an informative prior provided little improvement over modelling PA data alone and was inferior to using either the joint likelihood or correlation approach. Our results suggest that correlation models provide a robust alternative to joint likelihood models when covariates related to effort or detection in PO data are not available. Ecologists should be aware of the limitations of each approach and consider how well biases in the data can be modelled when deciding which type of IDM to use.
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.
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•Crop choice is a key driver of agricultural impact on the environment.•We explore apparent 1–5 year rotations in UKCEH Land Cover® plus: Crops 2015–2020.•Rotations fill less area than long-term grassland and complex-rotational sequences.•Three rotation classification systems display a range of distinct spatial patterns.•Predictions more accurate with long-term grassland, local scale and long rotations
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.
Current estimates of habitat extent are often based on remote sensing or field survey, and the two methods can produce very different results. Here, we present a method to combine data from these two sources and use it to estimate the extent of broad habitats across Great Britain.
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