scholarly journals Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale

2020 ◽  
Vol 10 (23) ◽  
pp. 13143-13153
Author(s):  
Jarrett Blair ◽  
Michael D. Weiser ◽  
Michael Kaspari ◽  
Matthew Miller ◽  
Cameron Siler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pitfall Trap ◽  
Ground Beetles ◽  
Continental Scale

Batch extraction of morphological and color metrics from invertebrate samples. v1

2021 ◽  
Author(s):  
Michael D Weiser ◽  
Katie E. Marshall ◽  
Cameron D. Siler ◽  
Michael Kaspari
Keyword(s):  
Machine Learning ◽  
North America ◽  
Pitfall Trap ◽  
Ground Beetles ◽  
Pitfall Traps ◽  
Batch Extraction ◽  
Continental Scale ◽  
Color Data ◽  
By Catch

This protocol is the complete methods used to extract abundance, morphology and color data from samples of invertebrates. We developed this protocol specifically to measure invertebrate by-catch from pitfall traps collected by the National Ecological Observatory Network (NEON), but these methods could be extended to any invertebrate samples. These methods were used in the publications: Blair, J.,M.D. Weiser, M. Kaspari, M.J. Miller, C. Siler and K. Marshall. 2020. Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale. Ecology and Evolution 10(23): 13143-13153. DOI:10.1002/ece3.6905. Weiser, M.D., K.E. Marshall, M.J. Miller, C.D. Siler, S.N. Smith & M. Kaspari. in review at Oikos (October 2021). Robust metagenomic evidence that local assemblage richness increases with latitude in ground-active invertebrates of North America.

THE EFFECT OF SOD TYPE ON THE OCCURRENCE OF GROUND BEETLES (COLEOPTERA: CARABIDAE) IN A PEST MANAGEMENT APPLE ORCHARD

1984 ◽  
Vol 116 (2) ◽  
pp. 165-171 ◽  
Author(s):  
N. J. Holliday ◽  
E. A. C. Hagley
Keyword(s):  
Pest Management ◽  
Pitfall Trap ◽  
Common Species ◽  
Ground Beetles ◽  
Apple Orchard ◽  
Soil Samples ◽  
Soil Sampling ◽  
Pitfall Trapping ◽  
Before And After ◽  
Trap Catches

AbstractThe effects on carabids of natural, fescue, and rye sod types and of tillage were investigated in a pest management apple orchard. Carabids were sampled before and after the treatments by pitfall trapping and by two types of soil sampling. There were no significant effects of sod type on pitfall trap catches; however the abundance of all common species in soil samples was significantly affected by sod types. Usually in soil samples carabids were most abundant in natural sod and least abundant in tilled plots; numbers were intermediate in fescue and rye. Sod type did not affect structure or diversity of the carabid fauna.

scholarly journals Impact of extreme weather conditions on European crop production in 2018

2020 ◽  
Vol 375 (1810) ◽  
pp. 20190510 ◽  
Author(s):  
Damien Beillouin ◽  
Bernhard Schauberger ◽  
Ana Bastos ◽  
Phillipe Ciais ◽  
David Makowski
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Large Scale ◽  
Extreme Weather ◽  
Severe Drought ◽  
Yield Losses ◽  
Learning Models ◽  
Yield Data ◽  
Continental Scale ◽  
Machine Learning Models

Extreme weather increases the risk of large-scale crop failure. The mechanisms involved are complex and intertwined, hence undermining the identification of simple adaptation levers to help improve the resilience of agricultural production. Based on more than 82 000 yield data reported at the regional level in 17 European countries, we assess how climate affected the yields of nine crop species. Using machine learning models, we analyzed historical yield data since 1901 and then focus on 2018, which has experienced a multiplicity and a diversity of atypical extreme climatic conditions. Machine learning models explain up to 65% of historical yield anomalies. We find that both extremes in temperature and precipitation are associated with negative yield anomalies, but with varying impacts in different parts of Europe. In 2018, Northern and Eastern Europe experienced multiple and simultaneous crop failures—among the highest observed in recent decades. These yield losses were associated with extremely low rainfalls in combination with high temperatures between March and August 2018. However, the higher than usual yields recorded in Southern Europe—caused by favourable spring rainfall conditions—nearly offset the large decrease in Northern European crop production. Our results outline the importance of considering single and compound climate extremes to analyse the causes of yield losses in Europe. We found no clear upward or downward trend in the frequency of extreme yield losses for any of the considered crops between 1990 and 2018. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

On the data synergy effect of large-sample multi-physics catchment modeling with machine learning

2021 ◽  
Author(s):  
Chaopeng Shen ◽  
Farshid Rahmani ◽  
Kuai Fang ◽  
Zhi Wei ◽  
Wen-Ping Tsai
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Similar Data ◽  
Continental Scale ◽  
Catchment Modeling ◽  
Multiple Regimes ◽  
Scale Models ◽  
Recent Developments ◽  
Data Synergy ◽  
Inherent Advantage

<p>Watersheds in the world are often perceived as being unique from each other, requiring customized study for each basin. Models uniquely built for each watershed, in general, cannot be leveraged for other watersheds. It is also a customary practice in hydrology and related geoscientific disciplines to divide the whole domain into multiple regimes and study each region separately, in an approach sometimes called regionalization or stratification. However, in the era of big-data machine learning, models can learn across regions and identify commonalities and differences. In this presentation, we first show that machine learning can derive highly functional continental-scale models for streamflow, evapotranspiration, and water quality variables. Next, through two hydrologic examples (soil moisture and streamflow), we argue that unification can often significantly outperform stratification, and systematically examine an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions and variables. In fact, the performance of the DL models benefited from some diversity in training data even with similar data quantity. However, allowing heterogeneous training data makes eligible much larger training datasets, which is an inherent advantage of DL. We also share our recent developments in advancing hydrologic deep learning and machine learning driven parameterization.</p>

scholarly journals African Soil Properties and Nutrients Mapped at 30--m Spatial Resolution using Two-scale Ensemble Machine Learning

2020 ◽  
Author(s):  
Tomislav Hengl ◽  
Matthew Miller ◽  
Josip Krizan ◽  
Keith Shepherd ◽  
Andrew Sila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Ph ◽  
Soil Samples ◽  
Total Carbon ◽  
Climatic Data ◽  
Continental Scale ◽  
Ensemble Machine Learning ◽  
Bioclimatic Variables ◽  
Extractable Phosphorus ◽  
Sentinel 2

Abstract Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mappedat all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGOfunded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. Inthis paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensivecompilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and totalnitrogen (N), total carbon, Cation Exchange Capacity (eCEC), extractable — phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg),sulfur (S), sodium (Na), iron (Fe), zinc (Zn) — silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariatelayers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives)images. Our 5–fold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC=0.900) tomore poorly predictable extractable phosphorus (CCC=0.654) and sulphur (CCC=0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11,B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 mresolution covariates. Climatic data images — SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature — however, remainedas the overall most important variables for predicting soil chemical variables at continental scale. The publicly available 30–m soil maps aresuitable for numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmentalprograms, or targeting of nutrition interventions.

Automated mapping of supraglacial hydrology using Machine Learning

2021 ◽  
Author(s):  
Diarmuid Corr ◽  
Amber Leeson ◽  
Malcolm McMillan ◽  
Ce Zhang
Keyword(s):  
Machine Learning ◽  
Sea Level Rise ◽  
Sea Level ◽  
False Negative ◽  
Ice Sheets ◽  
Landsat 8 ◽  
Continental Scale ◽  
Global Sea Level ◽  
Sentinel 2 ◽  
Antarctic Ice Sheets

<p>Mass loss from Greenlandic and Antarctic ice sheets are predicted to be the dominant contribution to global sea level rise in coming years. Supraglacial lakes and channels are thought to play a significant role in ice sheet mass balance by causing the speed-up of grounded ice and weakening, floating ice shelves to the point of collapse. Identifying the location, distribution and life cycle of these hydrological features on both the Greenland and Antarctic ice sheets is therefore important in understanding their present and future contribution to global sea level rise. Supraglacial hydrological features can be easily identified by eye in optical satellite imagery. However, given that there are many thousands of these features, and they appear in many hundreds of satellite images, automated approaches to mapping these features in such images are urgently needed.</p><p> </p><p>Current automated approaches in mapping supraglacial hydrology tend to have high false positive and false negative rates, which are often followed by manual corrections and quality control processes. Given the scale of the data however, methods such as those that require manual post-processing are not feasible for repeat monitoring of surface hydrology at continental scale. Here, we present initial results from our work conducted as part of the 4D Greenland and 4D Antarctica projects, which increases the accuracy of supraglacial lake and channel delineation using Sentinel-2 and Landsat-7/8 imagery, while reducing the need for manual intervention. We use Machine Learning approaches including a Random Forest algorithm trained to recognise water, ice, cloud, rock, shadow, blue-ice and crevassed regions. Both labelled optical imagery and auxiliary data (e.g. digital elevation models) are used in our approach. Our methods are trained and validated using data covering a range of glaciological and climatological conditions, including images of both ice sheets and those acquired at different points during the melt-season. The workflow, developed under Google Cloud Platform, which hosts the entire archive of Sentinel-2 and Landsat-8 data, allows for large-scale application over Greenlandic and Antarctic ice sheets, and is intended for repeated use throughout future melt-seasons.</p>

DESIGN FOR A LOW-COST, COVERED, RAMP PITFALL TRAP

2000 ◽  
Vol 132 (3) ◽  
pp. 387-389 ◽  
Author(s):  
Patrice Bouchard ◽  
Terry A. Wheeler ◽  
Henri Goulet
Keyword(s):  
Low Cost ◽  
Ground Surface ◽  
Soil Surface ◽  
Pitfall Trap ◽  
Ground Beetles ◽  
Pitfall Traps ◽  
Ecological Studies ◽  
Habitat Types ◽  
Metal Design ◽  
Original Metal

Pitfall traps are used extensively to sample ground-dwelling arthropods for systematic and ecological studies. They are inexpensive and easy to use and can be operated for relatively long periods of time without maintenance. These traps can collect arthropods in numbers that are suitable for rigorous statistical analysis, although their efficiency is influenced by many biotic and abiotic variables (Greenslade 1964; Spence and Niemelä 1994). Typically, pitfall traps are most productive when they are buried in the substrate, with the upper edge flush with the soil surface; traps with their upper edge above the substrate are much less effective (Greenslade 1964). Because of this, studies of ground-dwelling arthropods in habitats where soil is thin or lacking, or where digging is difficult, are left with no satisfactory alternatives to pitfall traps. A ramp pitfall trap developed by Bostanian et al. (1983) is useful in these habitats because it can be placed on the ground surface without digging. However, the original metal design was strongly biased toward the collection of large (>10 mm) ground beetles (Coleoptera: Carabidae) (Bostanian et al. 1983); it was also large, bulky, and relatively expensive. In this paper, we describe a ramp pitfall trap that is inexpensive, easily constructed, and durable. The trap is light, portable, easily installed, and effective in collecting all sizes of arthropods and can be used in many habitat types.

scholarly journals African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomislav Hengl ◽  
Matthew A. E. Miller ◽  
Josip Križan ◽  
Keith D. Shepherd ◽  
Andrew Sila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Soil Ph ◽  
Soil Samples ◽  
Total Carbon ◽  
Continental Scale ◽  
Ensemble Machine Learning ◽  
Soil Information ◽  
Extractable Phosphorus ◽  
Sentinel 2

AbstractSoil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ($$N \approx 150,000$$ N ≈ 150 , 000 ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the (Machine Learning in ) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.

scholarly journals Towards observation based gridded runoff estimates for Europe

2014 ◽  
Vol 11 (11) ◽  
pp. 12883-12932 ◽  
Author(s):  
L. Gudmundsson ◽  
S. I. Seneviratne
Keyword(s):  
Machine Learning ◽  
Land Surface ◽  
Spatial Scales ◽  
Model Development ◽  
Machine Learning Techniques ◽  
Continental Scale ◽  
Learning Techniques ◽  
Terrestrial Water ◽  
Model Training ◽  
Small Catchments

Abstract. Terrestrial water variables are the key to understanding ecosystem processes, feed back on weather and climate, and are a prerequisite for human activities. To provide context for local investigations and to better understand phenomena that only emerge at large spatial scales, reliable information on continental scale freshwater dynamics is necessary. To date streamflow is among the best observed variables of terrestrial water systems. However, observation networks have a limited station density and often incomplete temporal coverage, limiting investigations to locations and times with observations. This paper presents a methodology to estimate continental scale runoff on a 0.5° spatial grid with monthly resolution. The methodology is based on statistical up-scaling of observed streamflow from small catchments in Europe and exploits readily available gridded atmospheric forcing data combined with the capability of machine learning techniques. The resulting runoff estimates are validated against (1) runoff from small catchments that were not used for model training, (2) river discharge from nine continental scale river basins and (3) independent estimates of long-term mean evapotranspiration at the pan-European scale. In addition it is shown that the produced gridded runoff compares on average better to observations than a multi-model ensemble of comprehensive Land Surface Models (LSMs), making it an ideal candidate for model evaluation and model development. In particular, the presented machine learning approach may help determining which factors are most relevant for an efficient modelling of runoff at regional scales. Finally, the resulting data product is used to derive a comprehensive runoff-climatology for Europe and its potential for drought monitoring is illustrated.

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