Interpretation of interannual variability in long-term aquatic ecological surveys

Long-term ecological surveys (LTES) often exhibit strong variability among sampling dates. The use and interpretation of such interannual variability is challenging due to the combination of multiple processes involved and sampling uncertainty. Here, we analysed the interannual variability in ∼30 years of 150 species density (fish and invertebrate) and environmental observation time series in four aquatic systems (stream, river, estuary, and marine continental shelf) with different sampling efforts to identify the information provided by this variability. We tested, using two empirical methods, whether we could observe simultaneous fluctuation between detrended time series corresponding to widely acknowledged assumptions about aquatic population dynamics: spatial effects, cohort effects, and environmental effects. We found a low number of significant results (36%, 9%, and 0% for spatial, cohort, and environmental effects, respectively), suggesting that sampling uncertainty overrode the effects of biological processes. Our study does not question the relevance of LTES for detecting important trends, but clearly indicates that the statistical power to interpret interannual variations in aquatic species densities is low, especially in large systems where the degree of sampling effort is always limited.

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Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

Journal of Marine Science and Engineering ◽

10.3390/jmse9040363 ◽

2021 ◽

Vol 9 (4) ◽

pp. 363

Author(s):

Camilla Bertolini ◽

Edouard Royer ◽

Roberto Pastres

Keyword(s):

Time Series ◽

Primary Productivity ◽

Clustering Analysis ◽

Venice Lagoon ◽

Spatial Effects ◽

Spatial Gradients ◽

Climate Forecasting ◽

Oxygen Production Rate ◽

Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

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Minimum time required to detect population trends: the need for long-term monitoring programs

10.7287/peerj.preprints.3168v2 ◽

2017 ◽

Author(s):

Easton R White

Keyword(s):

Time Series ◽

Statistical Power ◽

Time Series Data ◽

Conservation Status ◽

Monitoring Program ◽

Minimum Time ◽

Series Data ◽

Series Length ◽

Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

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Minimum time required to detect population trends: the need for long-term monitoring programs

10.7287/peerj.preprints.3168 ◽

2018 ◽

Author(s):

Easton R White

Keyword(s):

Time Series ◽

Statistical Power ◽

Conservation Status ◽

Species Conservation ◽

Population Data ◽

Iucn Red List ◽

Series Length ◽

Large Variability ◽

Monitoring Programs

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. What is the minimum population time series length required to detect significant trends in abundance? I first present an overview of the theory and past work that has tried to address this question. As a test of these approaches, I then examine 822 populations of vertebrate species. I show that 72% of time series required at least 10 years of continuous monitoring in order to achieve a high level of statistical power. However, the large variability between populations casts doubt on commonly used simple rules of thumb, like those employed by the IUCN Red List. I argue that statistical power needs to be considered more often in monitoring programs. Short time series are likely under-powered and potentially misleading.

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Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

Remote Sensing ◽

10.3390/rs13183618 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3618

Author(s):

Stefan Dech ◽

Stefanie Holzwarth ◽

Sarah Asam ◽

Thorsten Andresen ◽

Martin Bachmann ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Observation Time ◽

Sensor Data ◽

High Temporal Resolution ◽

Statistical Parameters ◽

Medium Resolution ◽

Long Time ◽

Earth Observation Satellite

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

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A pragmatic re-engineering approach to extend short observation time series by climate anomaly gridding

10.5194/ems2021-421 ◽

2021 ◽

Author(s):

Ole Einar Tveito

Keyword(s):

Time Series ◽

Mean Value ◽

Observation Time ◽

Data Series ◽

Climate Anomaly ◽

Data Set ◽

Actual Problem ◽

Climate Gradients ◽

Observation Series

For many purposes, including the estimation of climate normals, requires long, continuous&#160; and preferably homogeneous time series. Many observation series do not meet these requirements, especially due to modernisation and automation of the observation network. Despite the lack of long series there is still a need to provide climate parameters representing a longer time period than available. An actual problem is the calculation of new standard climate normals for the 1991-2020 period, where normal values need to be assigned also for observation series not meeting the requirements of WMO to estimate climate normals from observations.&#160;One possible approach to estimate monthly time series is to extract value from gridded climate anomaly fields. In this study this approach is applied to complete time series that will be the basis for calculation of long term reference values.The calculation of the long term time series is a two step procedure. First monthly anomaly grids based on homogenised data series are produced. The homogenized series provide more stable and reliable spatial estimates than applying non homogenised data. The homogenised data set is also complete ensuring a spatially consistent input throughout the analysis period 1991-2020.The monthly anomalies for the location of the series to be complete are extracted from the gridded fields. By combining the interpolated anomalies with the observations the long term mean value can be estimated. The study shows that this approach provides reliable estimates of long term values, even with just a few events for calibration. The precision of the estimates depend more on the representativity of the grid estimates than length of the observation series. At locations where the anomaly grids represent the spatial climate variability well, stable estimates are achieved. On the other hand will the estimates at locations where the anomaly grids are less accurate due to sparse data coverage or steep climate gradients lead to estimates with a larger variability, and&#160; thus more uncertain estimates.&#160;

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Minimum time required to detect population trends: the need for long-term monitoring programs

10.7287/peerj.preprints.3168v1 ◽

2017 ◽

Author(s):

Easton R White

Keyword(s):

Time Series ◽

Statistical Power ◽

Time Series Data ◽

Conservation Status ◽

Monitoring Program ◽

Minimum Time ◽

Series Data ◽

Series Length ◽

Time Required

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Minimum time required to detect population trends: the need for long-term monitoring programs

10.7287/peerj.preprints.3168v4 ◽

2018 ◽

Author(s):

Easton R White

Keyword(s):

Time Series ◽

Statistical Power ◽

Conservation Status ◽

Species Conservation ◽

Population Data ◽

Iucn Red List ◽

Series Length ◽

Large Variability ◽

Monitoring Programs

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. What is the minimum population time series length required to detect significant trends in abundance? I first present an overview of the theory and past work that has tried to address this question. As a test of these approaches, I then examine 822 populations of vertebrate species. I show that 72% of time series required at least 10 years of continuous monitoring in order to achieve a high level of statistical power. However, the large variability between populations casts doubt on commonly used simple rules of thumb, like those employed by the IUCN Red List. I argue that statistical power needs to be considered more often in monitoring programs. Short time series are likely under-powered and potentially misleading.

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Interannual variability of the GNSS derived precipitable water vapour in the light of tropical climate patterns

10.5194/ems2021-470 ◽

2021 ◽

Author(s):

Grzegorz Nykiel ◽

Zofia Baldysz ◽

Beata Latos ◽

Mariusz Figurski

Keyword(s):

Time Series ◽

Surface Temperature ◽

Interannual Variability ◽

Water Vapour ◽

Precipitable Water ◽

Precipitable Water Vapour ◽

Climate Patterns ◽

Non Linear ◽

Linear Trends

Among various greenhouse gases, water vapour is characterized by the single highest positive feedback on the surface temperature and dominates increasing of the Earth&#8217;s surface temperature. Hence, long-term changes in its concentration in the atmosphere are one of the indicators for the assessment of the global warming rate. Consequently, monitoring of water vapour interannual variability is an important element in climate observing system, especially considering limitations of the surface technology that is traditionally used for this purpose. In this work, we have used 18 years of global navigation satellite system (GNSS) observations derived from 43 International GNSS Service (IGS) stations located across the global tropics. Based on them, we have estimated zenith tropospheric delay (ZTD) time series by precise point positioning (PPP) approach, and in next step converted them to long-term and homogenous precipitable water vapour (PWV) time series. We have investigated their interannual variability through estimation of non-linear trends and assessment which climate phenomena affect GNSS PWV long-term variability the most. Results have shown that for most of the analysed stations, GNSS PWV time series present distinct analogies to the global and regional climate phenomena such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) or North Pacific Gyro Oscillation (NPGO). Comparative analysis between GNSS PWV non-linear trends and selected climate indices showed strong cross-correlation, that amounted to 0.78. Moreover small-scale weather phenomena, such as local droughts, were clearly distinguishable, thus showing how GNSS PWV time series are sensitive to the combined effect of various weather and climate patterns.&#160;

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Minimum time required to detect population trends: the need for long-term monitoring programs

10.7287/peerj.preprints.3168v3 ◽

2017 ◽

Cited By ~ 1

Author(s):

Easton R White

Keyword(s):

Time Series ◽

Statistical Power ◽

Time Series Data ◽

Conservation Status ◽

Monitoring Program ◽

Minimum Time ◽

Series Data ◽

Series Length ◽

Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? I determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, I use simulation methods and examine 822 populations of vertebrate species. Here I show that on average 15.9 years of continuous monitoring are required in order to achieve a high level of statistical power. However, there is a wide distribution around this average, casting doubt on simple rules of thumb. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. However, there were no life-history traits (e.g. generation length) that were predictive of the minimum time required. These results point to the importance of sampling populations over long periods of time. I argue that statistical power needs to be considered in monitoring program design and evaluation. Short time series are likely under-powered and potentially misleading.

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