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. Species Distribution Models in EcologyLarge-scale ecological models of how species distributions and abundances vary over space and time are a critical tool in macroecology, biogeography, and conservation biology. They underpin our understanding of how biodiversity is shaped, how it is responding to anthropogenic activities, and how it might change in the future [1][2][3]. There is now a substantial literature on statistical tools for building species distribution models (SDMs) (see Glossary) and best practice in how to fit them [4][5][6][7]. SDMs also form a building block upon which more complex models, incorporating occupancy and/or abundance in space and time, can be built [8,9].
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.
SummaryChanges 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.
Ectomycorrhizal fungi commonly associate with the roots of forest trees where they enhance nutrient and water uptake, promote seedling establishment and have an important role in forest nutrient cycling. Predicting the response of ectomycorrhizal fungi to environmental change is an important step to maintaining forest productivity in the future. These predictions are currently limited by an incomplete understanding of the relative significance of environmental drivers in determining the community composition of ectomycorrhizal (ECM) fungi at large spatial scales. To identify patterns of community composition in ECM fungi along regional scale gradients of climate and nitrogen deposition in Scotland, fungal communities were analysed from 15 seminatural Scots pine (Pinus sylvestris L.) forests. Fungal taxa were identified by sequencing of the ITS rDNA region using fungal-specific primers. Nonmetric multidimensional scaling was used to assess the significance of 16 climatic, pollutant and edaphic variables on community composition. Vector fitting showed that there was a strong influence of rainfall and soil moisture on community composition at the species level, and a smaller impact of temperature on the abundance of ectomycorrhizal exploration types. Nitrogen deposition was also found to be important in determining community composition, but only when the forest experiencing the highest deposition (9.8 kg N ha(-1) yr(-1) ) was included in the analysis. This finding supports previously published critical load estimates for ectomycorrhizal fungi of 5-10 kg N ha(-1) yr(-1) . This work demonstrates that both climate and nitrogen deposition can drive gradients of fungal community composition at a regional scale.
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