scholarly journals Quantifying temporal change in biodiversity: challenges and opportunities

2013 ◽  
Vol 280 (1750) ◽  
pp. 20121931 ◽  
Author(s):  
Maria Dornelas ◽  
Anne E. Magurran ◽  
Stephen T. Buckland ◽  
Anne Chao ◽  
Robin L. Chazdon ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Change ◽  
Biodiversity Loss ◽  
Phase Shifts ◽  
Underlying Structure ◽  
Temporal Data ◽  
Statistical Procedures ◽  
Analytic Methods ◽  
Challenges And Opportunities ◽  
Biodiversity Change

Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series—lack of physical boundaries, uni-dimensionality, autocorrelation and directionality—that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions.

scholarly journals Biodiversity trends are stronger in marine than terrestrial assemblages

2018 ◽  
Author(s):  
Shane Blowes ◽  
Sarah Supp ◽  
Laura Antão ◽  
Amanda Bates ◽  
Helge Bruelheide ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Change ◽  
Species Turnover ◽  
Temporal Trends ◽  
Compositional Change ◽  
Taxa Richness ◽  
Rates Of Change ◽  
Extinction Rates ◽  
Consistent Change ◽  
Biodiversity Change

SummaryHuman activities have fundamentally altered biodiversity. Extinction rates are elevated and model projections suggest drastic biodiversity declines. Yet, observed temporal trends in recent decades are highly variable, despite consistent change in species composition. Here, we uncover clear spatial patterns within this variation. We estimated trends in the richness and composition of assemblages in over 50,000 time-series, to provide the most comprehensive assessment of temporal change in biodiversity across the planet to date. The strongest, most consistent pattern shows compositional change dominated by species turnover, with marine taxa experiencing up to fourfold the variation in rates of change of terrestrial taxa. Richness change ranged from no change to richness gains or losses of ~10% per year, with tropical marine biomes experiencing the most extreme changes. Earth is undergoing a process of spatial reorganisation of species and, while few areas are unaffected, biodiversity change is consistently strongest in the oceans.

Are local losses of biodiversity causing degraded ecosystem function?

2017 ◽  
Author(s):  
Mark Vellend
Keyword(s):  
Ecosystem Function ◽  
Species Interactions ◽  
Regional Scale ◽  
Temporal Trends ◽  
Biodiversity Loss ◽  
Large Degree ◽  
Global Scale ◽  
Degraded Ecosystem ◽  
Biodiversity Change ◽  
Local Extirpation

This chapter highlights the scale dependence of biodiversity change over time and its consequences for arguments about the instrumental value of biodiversity. While biodiversity is in decline on a global scale, the temporal trends on regional and local scales include cases of biodiversity increase, no change, and decline. Environmental change, anthropogenic or otherwise, causes both local extirpation and colonization of species, and thus turnover in species composition, but not necessarily declines in biodiversity. In some situations, such as plants at the regional scale, human-mediated colonizations have greatly outnumbered extinctions, thus causing a marked increase in species richness. Since the potential influence of biodiversity on ecosystem function and services is mediated to a large degree by local or neighborhood species interactions, these results challenge the generality of the argument that biodiversity loss is putting at risk the ecosystem service benefits people receive from nature.

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach

2018 ◽  
Vol 40 ◽  
pp. 34-44 ◽  
Author(s):  
Mingquan Wu ◽  
Wenjiang Huang ◽  
Zheng Niu ◽  
Changyao Wang ◽  
Wang Li ◽  
...  
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Data ◽  
Ndvi Time Series ◽  
Fusion Approach

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

scholarly journals Assemblage Time Series Reveal Biodiversity Change but Not Systematic Loss

Science ◽  
2014 ◽  
Vol 344 (6181) ◽  
pp. 296-299 ◽  
Author(s):  
M. Dornelas ◽  
N. J. Gotelli ◽  
B. McGill ◽  
H. Shimadzu ◽  
F. Moyes ◽  
...  
Keyword(s):  
Time Series ◽  
Biodiversity Change

scholarly journals Freshwater-salt water interface dynamics during pumping tests

2019 ◽  
Author(s):  
Yongcheol Kim ◽  
Heesung Yoon ◽  
Soo-Hyung Lee
Keyword(s):  
Time Series ◽  
Coastal Aquifer ◽  
Time Series Data ◽  
Temporal Change ◽  
Salt Water ◽  
Series Data ◽  
Water Interface ◽  
Aquifer Management ◽  
Pumping Tests ◽  
Short Period

Freshwater-salt water interface (FSI) location is very important information for decision maker in managing coastal aquifer system, however, its temporal change have been hard to get using conventional method such as EC monitoring at one or several fixed depths, geophysical logging or remote sensing techniques. A FSI tracking device, which has density between freshwater and salt water and hence can moves up and down as the freshwater-salt water transition zone moves, is used to get a temporal change data for the interface during several different types of pumping tests, which were performed at coastal monitoring wells in Seocheon, middle west of Korean Peninsula. Four short period pumping tests, three long-period pumping tests, one step-drawdown test, one reverse step-drawdown test were performed at different pumping rate ranging 19.86 to 48.71 m3/d for different pumping period of 60 minutes to 2851 minutes. Time series data shows that the Interface-Egg rises up from -86.0 to -77.6 m amsl after 24-hours pumping and to -40.8 m amsl after 2-days pumping and freshwater lens thickness is getting thinner from 88.1 m to 78.4 m after 24-hours pumping and then 42.3 m after 2-days pumping. These salt water up-coning phenomena are supported by EC profiles which were logged before and after the whole pumping periods. Time series data tell us that salt water upconing in the pumping well happens quickly and recovers at a very slow rate which is about 1.5 cm/d at 3 months after stopping pumping. The time series data of groundwater head and the interface-Egg’s location also shows that there is a tidal influence between pumping periods. The FSI tracker is expected to be practically applied to coastal aquifer management preventing from salt water intrusion, especially at dynamically pumping area for agricultural and/or domestic water supply.

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