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 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.
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.
Understanding the effects of warming on greenhouse gas feedbacks to climate change represents a major global challenge. Most research has focused on direct effects of warming, without considering how ...concurrent changes in plant communities may alter such effects. Here, we combined vegetation manipulations with warming to investigate their interactive effects on greenhouse gas emissions from peatland. We found that although warming consistently increased respiration, the effect on net ecosystem CO₂ exchange depended on vegetation composition. The greatest increase in CO₂ sink strength after warming was when shrubs were present, and the greatest decrease when graminoids were present. CH₄ was more strongly controlled by vegetation composition than by warming, with largest emissions from graminoid communities. Our results show that plant community composition is a significant modulator of greenhouse gas emissions and their response to warming, and suggest that vegetation change could alter peatland carbon sink strength under future climate change.
Understanding how plant species coexist in tropical rainforests is one of the biggest challenges in community ecology. One prominent hypothesis suggests that rare species are at an advantage because ...trees have lower survival in areas of high conspecific density due to increased attack by natural enemies, a process known as negative density dependence (NDD). A consensus is emerging that NDD is important for plant-species coexistence in tropical forests. Most evidence comes from short-term studies, but testing the prediction that NDD decreases the spatial aggregation of tree populations provides a long-term perspective. While spatial distributions have provided only weak evidence for NDD so far, the opposing effects of environmental heterogeneity might have confounded previous analyses. Here we use a novel statistical technique to control for environmental heterogeneity while testing whether spatial aggregation decreases with tree size in four tropical forests. We provide evidence for NDD in 22% of the 139 tree species analyzed and show that environmental heterogeneity can obscure the spatial signal of NDD. Environmental heterogeneity contributed to aggregation in 84% of species. We conclude that both biotic interactions and environmental heterogeneity play crucial roles in shaping tree dynamics in tropical forests.
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
Abstract
Due to rising demand for both food and environmental services, agriculture is increasingly required to deliver multiple outcomes. Characterising differences, across agricultural landscapes, ...via the identification of broad archetypal groupings, is an important step in exploring spatial patterns in the capacity of land to deliver these potentially competing functions. Creating characterisations at multiple levels, for landscape and farm management, can allow policy-makers and land managers to harmonise delivery of ecosystem services at different intervention scales. This can identify ways to increase the complementarity of public goods and the sustainability of farmed landscapes. We used data-driven machine learning to create landscape and agricultural management archetypes (1 km resolution) at three levels, defined by opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. By using a non-nested hierarchy, we identified which types of management are restricted to certain landscape settings, and which are applicable across multiple landscape contexts. Understanding variation within and between agricultural landscapes and farming practices has implications for planning environmental sustainability and food security. It can also aid understanding of the scale at which interventions could be most effective, from incentivising changes in farmer behaviour to policy drivers of large-scale land use change.
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.
Global warming has advanced the timing of biological events, potentially leading to disruption across trophic levels. The potential importance of phenological change as a driver of population trends ...has been suggested. To fully understand the possible impacts, there is a need to quantify the scale of these changes spatially and according to habitat type. We studied the relationship between phenological trends, space and habitat type between 1965 and 2012 using an extensive UK dataset comprising 269 aphid, bird, butterfly and moth species. We modelled phenologies using generalized additive mixed models that included covariates for geographical (latitude, longitude, altitude), temporal (year, season) and habitat terms (woodland, scrub, grassland). Model selection showed that a baseline model with geographical and temporal components explained the variation in phenologies better than either a model in which space and time interacted or a habitat model without spatial terms. This baseline model showed strongly that phenologies shifted progressively earlier over time, that increasing altitude produced later phenologies and that a strong spatial component determined phenological timings, particularly latitude. The seasonal timing of a phenological event, in terms of whether it fell in the first or second half of the year, did not result in substantially different trends for butterflies. For moths, early season phenologies advanced more rapidly than those recorded later. Whilst temporal trends across all habitats resulted in earlier phenologies over time, agricultural habitats produced significantly later phenologies than most other habitats studied, probably because of nonclimatic drivers. A model with a significant habitat‐time interaction was the best‐fitting model for birds, moths and butterflies, emphasizing that the rates of phenological advance also differ among habitats for these groups. Our results suggest the presence of strong spatial gradients in mean seasonal timing and nonlinear trends towards earlier seasonal timing that varies in form and rate among habitat types.
The study charts the phenology of more than 250 UK birds and insects in the context of global warming, using information about habitat and local geography to explain variation in aphid, moth and butterfly migration and bird egg‐laying.
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.
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•Semi-natural plant species richness depends on soil pH and net primary productivity.•Parameterized model fitted to field richness data for four vegetation types.•Richness 1800–2010 predicted from measured soil pH and modelled productivity.•Calculated richness reduced by nitrogen and sulphur atmospheric pollution.•Recent recovery in richness, owing to declines in sulphur pollution.
Capsule: Modelling the long-term effects of atmospheric deposition indicates that both nutrient enrichment and soil acidification reduced plant species richness, which is now recovering from acidification.
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