The Next Decade of Big Data in Ecosystem Science

LaDeau, Shannon L.; Han, Barbara A.; Rosi‐Marshall, Emma J.; Weathers, Kathleen C.

doi:10.1007/s10021-016-0075-y

Cited by 79 publications

(64 citation statements)

References 64 publications

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“…Synthesizing big data to generate new working hypotheses will be key to advancing our understanding of ecosystem function, predicting changes, and informing managers and policymakers (LaDeau, Han, Rosi-Marshall, & Weathers, 2017;Peters et al, 2014). Big data will also be important for understanding large-scale environmental phenomena (Levy et al, 2014), such as those resulting from transformative change.…”

Section: Summary and Synthesesmentioning

confidence: 99%

Advancing ecohydrology in the changing tropics: Perspectives from early career scientists

et al. 2017

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Tropical ecosystems offer a unique setting for understanding ecohydrological processes, but to date, such investigations have been limited. The purpose of this paper is to highlight the importance of studying these processes—specifically, how they are being affected by the transformative changes taking place in the tropics—and to offer an agenda for future research. At present, the ongoing loss of native ecosystems is largely due to agricultural expansion, but parallel processes of afforestation are also taking place, leading to shifts in ecohydrological fluxes. Similarly, shifts in water availability due to climate change will affect both water and carbon fluxes in tropical ecosystems. A number of methods exist that can help us better understand how changes in land use and climate affect ecohydrological processes; these include stable isotopes, remote sensing, and process‐based models. Still, our knowledge of the underlying physical mechanisms, especially those that determine the effects of scale on ecosystem processes, remains incomplete. We assert that development of a knowledge base concerning the effects of transformative change on ecological, hydrological, and biogeochemical processes at different spatio‐temporal scales is an urgent need for tropical regions and should serve as a compass for emerging ecohydrologists. To reach this goal, we advocate a research agenda that expands the number and diversity of ecosystems targeted for ecohydrological investigations and connects researchers across the tropics. We believe that the use of big data and open source software—already an important integrative tool/skill for the young ecohydrologist—will be key in expanding research capabilities.

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Section: Summary and Synthesesmentioning

confidence: 99%

Advancing ecohydrology in the changing tropics: Perspectives from early career scientists

et al. 2017

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“…; LaDeau et al . ). This ever increasing wealth of data provides unique opportunities for ecologists to enhance ecosystem understanding and characterisation (Ter Braak & Van Tongeren ; Reichman et al .…”

Section: Introductionmentioning

confidence: 97%

“…During the past few decades increasingly larger volumes of ecological data have become available in response to continued advances in measurement techniques and the rapid expansion of long-term monitoring networks (Running et al 1999;Aanensen et al 2009;LaDeau et al 2017). This ever increasing wealth of data provides unique opportunities for ecologists to enhance ecosystem understanding and characterisation (Ter Braak & Van Tongeren 1995;Reichman et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs

et al. 2017

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The daunting complexity of ecosystems has led ecologists to use mathematical modelling to gain understanding of ecological relationships, processes and dynamics. In pursuit of mathematical tractability, these models use simplified descriptions of key patterns, processes and relationships observed in nature. In contrast, ecological data are often complex, scale-dependent, space-time correlated, and governed by nonlinear relations between organisms and their environment. This disparity in complexity between ecosystem models and data has created a large gap in ecology between model and data-driven approaches. Here, we explore data assimilation (DA) with the Ensemble Kalman filter to fuse a two-predator-two-prey model with abundance data from a 2600+ day experiment of a plankton community. We analyse how frequently we must assimilate measured abundances to predict accurately population dynamics, and benchmark our population model's forecast horizon against a simple null model. Results demonstrate that DA enhances the predictability and forecast horizon of complex community dynamics.

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“…Freely available geospatial datasets have transformed biological research capabilities by providing extensive information on environmental conditions (Hampton et al, ; Ladeau, Han, Rosi‐Marshall, & Weathers, ). The biological community has made good use of this information to address a diversity of questions, commonly through the application of species distribution models (SDMs, Elith & Leathwick, ).…”

Section: Introductionmentioning

confidence: 99%

New regionally modelled soil layers improve prediction of vegetation type relative to that based on global soil models

Cramer

Wootton

Mazijk

et al. 2019

Diversity and Distributions

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Aim: High-resolution spatial soil data are crucial to species distribution modelling for fundamental research and conservation planning. Recent globally modelled soil layers (e.g. SoilGrids) have transformed distribution modelling, but may fail to represent regional soil characteristics accurately. We hypothesize that in the Cape biodiversity hotspot of South Africa, the use of global soil layers has led to underestimation of the importance of edaphic factors as determinants of species' and vegetation distributions. We present a series of new, regionally modelled layers to address this deficiency. Location: Greater Cape Floristic Region (GCFR, South Africa). Methods:We georeferenced edaphic characteristics from literature and other sources and used boosted regression trees (BRT) to associate edaphic characteristics with spatially explicit topographic, climatic, soil texture and biotic variables. Multinomial BRTs were used to predict mapped vegetation types from the collated edaphic and other data.Results: BRTs reliably predicted pH (92% of variance), Na (87%), K (85%), electrical conductivity (81%) and P (73%), but were less accurate for total N (55%) and total C (61%), for which data were sparser. Soil clay and pH values differed markedly in range and in spatial variation from those in SoilGrids. Using our new edaphic layers, we were able to accurately predict spatial distributions of vegetation types within the GCFR (multi-class AUC = 0.96). The multinomial BRT predicted vegetation less well when based on SoilGrids data alone (AUC = 0.84). Main conclusions:The more faithful representation of soil properties in our model is attributable both to its use of ca. 10-fold more samples, and to its regional focus. Our model of edaphic characteristics captures important edaphic variability that is vital for understanding plant and consequently faunal distributions, with wide-ranging conservation implications. Ongoing development of global syntheses of soil data requires more samples, especially in areas with high spatial heterogeneity and extreme edaphic conditions.

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The Next Decade of Big Data in Ecosystem Science

Cited by 79 publications

References 64 publications

Advancing ecohydrology in the changing tropics: Perspectives from early career scientists

Advancing ecohydrology in the changing tropics: Perspectives from early career scientists

Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs

New regionally modelled soil layers improve prediction of vegetation type relative to that based on global soil models

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