Common loon reproduction and chick feeding on acidified lakes in the Adirondack Park, New York

1988 ◽  
Vol 66 (4) ◽  
pp. 804-810 ◽  
Author(s):  
Karl E. Parker
Keyword(s):  
New York ◽  
Food Resource ◽  
Resource Depletion ◽  
Low Ph ◽  
Lake Acidification ◽  
Common Loon ◽  
Feeding Problems ◽  
Short Term ◽  
Lake Acidity ◽  
Adirondack Lakes

The effects of lake acidification on common loon reproduction were studied on a total of 24 Adirondack lakes from May through August in 1983 and 1984. The lakes ranged in size from 10.5 to 179 ha; pH ranged from 4.65 to 6.77 and alkalinity from −66 to 111 μequiv./L. Although loons nesting on small, low-pH lakes had a high fledging rate, possibly because of reduced disturbance or predation, no significant relationship (P > 0.10) was found between lake acidity status and loon reproductive success. No chick mortality could be attributed to lake acidification, but chicks on low-pH lakes were generally fed prey much smaller or much larger than those normally preferred. A pair nesting on a fishless lake fed aquatic insects to their constantly begging chick, spending two to four times longer feeding the chick compared with loons on lakes with fish. This pair, alternating absences, flew to another lake to feed, and on three occasions returned to the nesting lake carrying a fish. Loons on the low-pH study lakes apparently adapted, at least in the short term, to food resource depletion associated with acidification. Despite this, acidification creates potentially severe feeding problems for chicks by reducing prey diversity and quantity.

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City

2017 ◽  
Vol 62 ◽  
pp. 3-11 ◽  
Author(s):  
Nicholas E. Johnson ◽  
Olga Ianiuk ◽  
Daniel Cazap ◽  
Linglan Liu ◽  
Daniel Starobin ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Gradient Boosting ◽  
Waste Generation ◽  
Short Term

scholarly journals Common Loon, Gavia immer, Breeding Success in Relation to Lake pH and Lake Size Over 25 Years

2009 ◽  
Vol 123 (2) ◽  
pp. 146 ◽  
Author(s):  
Robert Alvo
Keyword(s):  
Breeding Success ◽  
Low Ph ◽  
Single Pair ◽  
Gavia Immer ◽  
Common Loon ◽  
Sulphur Deposition ◽  
Acidic Lakes ◽  
Ph Range ◽  
Lake Size ◽  
Critical Ph

I monitored Common Loon (Gavia immer) breeding success in relation to lake pH (range 4.0–8.5) between 1982 and 2007 on 38 single-pair lakes (5–88 ha) in the Sudbury, Ontario, area. No chicks fledged on lakes with pH < 4.4. Chicks fledged on lakes with slightly higher pH only if the lakes were relatively large. Acidic lakes became less acidic as sulphur dioxide emissions from the Sudbury smelters and sulphur deposition from other long-range sources decreased. Two lakes initially too acidic to support successful loon reproduction eventually had successful reproduction. One loon pair used two large acidic lakes (combined area 140 ha) connected by shallow rapids, and one of the adults made extremely long dives (average = 99 s) while foraging for the chicks. One chick died on that lake after apparently ingesting a very large food item; the lack of smaller items was attributed to the lake’s acidity. My results suggest that a shortage of food for chicks is the main reason why low pH reduces breeding success. I suggest that, for lakes without high levels of dissolved organic carbon (DOC), the critical pH for loon breeding success is approximately 4.3, and the suboptimal pH is approximately 4.4–6.0.

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

scholarly journals Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic

2021 ◽  
Vol 251 ◽  
pp. 01017
Author(s):  
Zhixiang Lu
Keyword(s):  
Machine Learning ◽  
New York ◽  
Regional Differences ◽  
Prediction Accuracy ◽  
Visual Analysis ◽  
Sharing Economy ◽  
Short Term ◽  
Emerging Industries ◽  
Machine Learning Model ◽  
The Impact

With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.

scholarly journals Effects of air pollution on children from a socioecological perspective

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jong In Kim ◽  
Gukbin Kim ◽  
Yeonja Choi
Keyword(s):  
Air Pollution ◽  
Life Expectancy ◽  
Natural Resource ◽  
Solid Fuel ◽  
Resource Depletion ◽  
Short Term ◽  
Environmental Threats ◽  
Country Level ◽  
Per Capita

Abstract Background Country-level inequality in life expectancy (ILE) and deaths of children under age five due to air pollution (DCAP) can be influenced by country-level income per capita, solid fuel, electrification, and natural resource depletion. The ILE and DCAP in the short-term are useful indicators that can help in developing ways to reduce environmental threats. This study confirms evidence for ILE and DCAP as the effects of environmental threats by country-level income, energy, and natural resource levels from a socioecological approach. Methods This study based on life expectancy and children data on 164 countries acquired from the United Nations Development Programme. We obtained the country-level socioecological data from the United Nations and the World Bank database. We assessed the associations between ILE, DCAP, and the country-level indicators applying correlations coefficient and the regression models. Results These study findings showed considerable correlations between ILE and country-level socioecological indicators: gross national income per capita (GNI), non-solid fuel (NSF), electrification rate (ER), and natural resource depletion (NRD). The DCAP in short-term predictors were low NSF and low ER (R2 = 0.552), and ILE predictors were low GNI, NSF, and ER and higher NRD (R2 = 0.816). Thus, the countries with higher incomes and electrification rates and more sustainable natural resources had lower expected DCAP in the short-term and ILE in the long-term. Conclusions Based on our results, we confirmed that country-level income, energy, and natural resource indicators had important effects on ILE in long-term and DCAP in short-term. We recommend that countries consider targeting high standards of living and national incomes, access to non-solid fuel and electricity as energy sources, and sustainable natural resources to reduce ILE and DCAP in short-term.

Distribution of scaled chrysophytes in 17 Adirondack (New York) lakes with special reference to pH

1988 ◽  
Vol 66 (7) ◽  
pp. 1391-1403 ◽  
Author(s):  
Peter A. Siver
Keyword(s):  
New York ◽  
Special Reference ◽  
Surface Sediments ◽  
Species Distributions ◽  
Franklin County ◽  
Scaled Chrysophytes ◽  
Bog Lakes ◽  
Adirondack Lakes ◽  
The Common ◽  
Similarities And Differences

Thirty-four scaled chrysophyte taxa are described from 17 Adirondack lakes located in either Hamilton or Franklin counties. Ten taxa, Synura sphagnicola, Syn. echinulata, Syn. petersenii, Mallomonas acaroides var. muskokana, M. hamata, M. caudata, M. crassisquama, M. galeiformis, Spiniferomonas trioralis, and Chrysosphaerella longispina, were found in more than 40% of the study lakes. Mallomonas acaroides var. muskokana, M. hamata, Syn. sphagnicola, Syn. echinulata, and Spiniferomonas coronacircumspina were common and important components of the phytoplankton floras in lakes with a pH < 5.4; however, M. hamata and Sp. coronacircumspina were noticeably absent from bog lakes. Synura sphagnicola and Syn. echinulata were as common in samples with a pH between 5.4 and 7 as they were below pH 5.4. Mallomonas crassisquama, M. caudata, M. pseudocoronata, and Synura spinosa were found in a significantly greater percentage of samples from circumneutral lakes and were lacking from waters with a pH < 5.4. Differences in species distributions between Hamilton County (higher mean pH) and Franklin County lakes further supported the observed pH ranges of the common taxa. Similarities and differences are made with a study of Smol et al. (Can. J. Bot. 62: 911–923), who described the distribution of scaled chrysophytes from the surface sediments of Adirondack lakes.

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