scholarly journals Multiple-Stressor Interactions in Tributaries Alter Downstream Ecosystems in Stream Mesocosm Networks

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1194
Author(s):  
Ana M. Chará-Serna ◽  
John S. Richardson
Keyword(s):  
Population Dynamics ◽  
Second Order ◽  
Physical Habitat ◽  
Stream Networks ◽  
Stream Network ◽  
Dispersal Patterns ◽  
Multiple Stressor ◽  
Ept Taxa ◽  
Stream Mesocosm ◽  
Ept Richness

We studied how multiple-stresssors in tributaries affect function, diversity, and physical habitat of recipient downstream ecosystems. Using a mesocosm model of a stream network, we manipulated sediment and nutrients individually and in combination in tributaries of second-order channels, to test the effect of complex stressor interactions within tributaries on recipient channels. Sedimentation in second-order channels increased with the level of disturbance of the tributaries. Moreover, Ephemeroptera, Plecoptera, and Trichoptera (EPT) density and EPT richness were higher in second-order channels fed by tributaries where the stressors were applied separately, compared to those fed by tributaries where the stressors were applied simultaneously. Our observations suggest this result was due to the combination of the two stressors within the same tributary reducing EPT drift from the tributaries further than the addition of the stressors in separate tributaries. These results support the hypothesis that cumulative upstream disturbance can influence downstream recipient ecosystems in stream networks. However, contrary to our expectations, most observed effects were due to impacts on dispersal patterns of EPT taxa, rather than downstream accumulation of disturbances throughout the network. Our results underscore the importance of metacommunity frameworks to understand how tributary disturbance may influence population dynamics in downstream ecosystems.

Predicting Distribution, Production, Recreational Fishing Demand, and Conservation Concern of Indigenous Rainbow Trout Populations Based on Landscape Characteristics

2021 ◽  
Author(s):  
Ariane Cantin ◽  
Anne Farineau ◽  
Darren J. Bender ◽  
John R. Post
Keyword(s):  
Population Dynamics ◽  
Rainbow Trout ◽  
Recreational Fishing ◽  
Lake Area ◽  
Fishing Pressure ◽  
Stream Network ◽  
Landscape Characteristics ◽  
Rainbow Trout Oncorhynchus Mykiss ◽  
Conservation Concern ◽  
Patterns And Processes

Landscape ecology has mainly been integrated in aquatic science to describe patterns and processes in stream networks, but many lakes are connected through their tributaries and are also impacted by their position and connectivity within the watershed. This information on lake characteristics can be used by inland fisheries managers that oversee large landscapes comprising many waterbodies to predict: (1) species composition; (2) population dynamics and productivity; (3) recreational fishing pressure; and (4) overall conservation concern. We developed a methodology to assess these four items for the rainbow trout (Oncorhynchus mykiss) fishery of British Columbia by presenting a case study focused on the Clearwater and North Thompson watersheds using: the connectivity of lakes within the stream network to predict rainbow trout presence, stream order and lake area to estimate habitat availability and predict population dynamics and productivity (supply), and travel time from population centres to predict recreational fishing pressure (demand). By incorporating connectivity and environmental proxies of habitat, we explore patterns in population dynamics that can be used by fisheries managers to identify populations sensitive to overfishing or disturbance.

scholarly journals Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals

2020 ◽  
Vol 10 (24) ◽  
pp. 9005
Author(s):  
Chien-Cheng Lee ◽  
Zhongjian Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Recognition Accuracy ◽  
Signal Interference ◽  
Stream Networks ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Stream Network ◽  
Value Decomposition

Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.

scholarly journals Beaver dam capacity of Canada’s boreal plain in response to environmental change

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nichole-Lynn Stoll ◽  
Cherie J. Westbrook
Keyword(s):  
Environmental Conditions ◽  
Environmental Changes ◽  
Water Cycle ◽  
Stream Networks ◽  
Stream Network ◽  
Beaver Dam ◽  
Riding Mountain National Park ◽  
Induced Changes ◽  
Response To Environmental Change ◽  
Beaver Dams

Abstract Environmental changes are altering the water cycle of Canada’s boreal plain. Beaver dams are well known for increasing water storage and slowing flow through stream networks. For these reasons beavers are increasingly being included in climate change adaptation strategies. But, little work focuses on how environmental changes will affect dam building capacity along stream networks. Here we estimate the capacity of the stream network in Riding Mountain National Park, Manitoba, Canada to support beaver dams under changing environmental conditions using a modelling approach. We show that at capacity, the park’s stream network can support 24,690 beaver dams and hold between 8.2 and 12.8 million m3 of water in beaver ponds. Between 1991 and 2016 the park’s vegetation composition shifted to less preferred beaver forage, which led to a 13% decrease in maximum dam capacity. We also found that dam capacity is sensitive to the size of regularly-occurring floods—doubling the 2-year flood reduces the park’s dam capacity by 21%. The results show that the potential for beaver to offset some expected climatic-induced changes to the boreal water cycle is more complex than previously thought, as there is a feedback wherein dam capacity can be reduced by changing environmental conditions.

Point effluent discharges have unclear direct impacts on local biogeochemical P cycling against high background complexity in catchment pollution processes and ecosystem responses

2020 ◽  
Author(s):  
Marc Stutter ◽  
Samia Richards
Keyword(s):  
Spatial Data ◽  
Stream Water ◽  
Ex Situ ◽  
Stream Network ◽  
Ecosystem Responses ◽  
High Background ◽  
Ecosystem Effects ◽  
Multiple Stressor ◽  
Downstream Effects ◽  
Substrate Induced Respiration

<p>Point discharges of pollution such as effluents, enriched in bioavailable nutrients, organic matter and multiple contaminants, are often considered as having both strong local and cumulative downstream effects on aquatic ecosystem quality. Since potential impacts of effluents involve many multiple stressor interactions it requires an integrated suite of in-situ and ex-situ techniques to evaluate the biotic and abiotic interplay of the ecosystem effects. This study aimed to evaluate impacts using sampling transects around discharges from wastewater treatment works (WWTW) to a range of watercourses. The hypothesis was that major effluent discharges would lead to local downstream enrichment in nutrient and microbial contaminants, altered microbial communities and impairment in P processing rates with downstream recovery distances related to cumulative upstream pollution.</p><p>Five river transects were evaluated on two dates comprising points 100m above then 100, 200, 500 m below stream-side WWTW. Stream water samples were collected (effluents where possible) and analysed for C, N, P forms, coliforms, pesticides and pharmaceuticals. Biofilms (grown on tiles between sampling dates) and recovered for analysis alongside bed sediments for stoichiometry, P enzyme activity, substrate induced respiration assays and chlorophyll (biofilms). Catchments were characterised using spatial data on landcover, stream network and cumulative pollution sources.</p><p>Patterns of pollution presence in the waters and cycling indicators in the bed and periphyton did not show clear patterns of high local and declining downstream impacts. Instead a surprising complexity of weak transect effects amongst a high background heterogeneity was seen. This likely results from a heterogeneous biophysical environment of the channel as well as the complexity of the catchment ‘diffuse’ pollution inputs. Hence, WWTW impacts on aquatic pollution presence and processing factors were unclear and masked by catchment system heterogeneity and complexity.   </p>

scholarly journals Explicit simulations of stream networks to guide hydrological modelling in ungauged basins

2010 ◽  
Vol 14 (8) ◽  
pp. 1435-1448 ◽  
Author(s):  
S. Stoll ◽  
M. Weiler
Keyword(s):  
Regional Climate ◽  
Model Development ◽  
Hydrological Modelling ◽  
Distributed Data ◽  
Ungauged Basins ◽  
Stream Networks ◽  
Stream Network ◽  
Network Modelling ◽  
Discharge Data ◽  
Parsimonious Model

Abstract. Rainfall-runoff modelling in ungauged basins is still one of the greatest challenges in hydrological research. The lack of discharge data necessitates the establishment of new innovative approaches to guide hydrological modelling in ungauged basins. Besides the transfer of calibrated parameters from similar gauged catchments, the application of distributed data as a hydrological response in addition to discharge seems to be promising. A new approach to guide hydrological modelling based on explicit simulation of the spatial stream network was tested in four different catchments in Germany. In a first step we used a simplified version of the process-based model Hill-Vi together with regional climate normals to simulate stream networks. The calculation of gravity driven lateral subsurface and groundwater flow is used to identify patterns of stream cells, which were compared to reference stream networks and their degree of spatial agreement was evaluated. Significant differences between good and poor simulations could be distinguished and the corresponding parameter sets relate well with the hydrogeological properties of the catchments. The optimized parameters were subsequently used to simulate daily discharge using an observed time series of precipitation and air temperature. The performance was evaluated against observed discharge and water balance. This approach shows some promising results but also some limitations. Although the model's parsimonious model structure could be further improved regarding discharge recession and evapotranspiration, the performance was similar to regionalisation methods. Stream network modelling, which has minimal data requirements, seems to be a reasonable alternative for model development and parameter evaluation in ungauged basins.

scholarly journals Incorporating lakes in stream fish habitat models: are we missing a key landscape attribute?

2017 ◽  
Vol 74 (5) ◽  
pp. 629-635 ◽  
Author(s):  
Marc Pépino ◽  
Marco A. Rodríguez ◽  
Pierre Magnan
Keyword(s):  
Relative Abundance ◽  
Brook Trout ◽  
Environmental Variability ◽  
Fish Habitat ◽  
Stream Fish ◽  
Drainage Network ◽  
Stream Networks ◽  
Stream Network ◽  
Habitat Models ◽  
Critical Attribute

Although lakes and rivers are intimately connected, more effort is needed to develop conceptual approaches accounting for lake–stream interactions within the drainage network. Lakes can buffer the impacts of environmental variability in streams and facilitate stream fish recolonization processes. However, lakes have rarely been incorporated in habitat models for stream fish. We examine whether including the presence of lakes in habitat models can improve our understanding of brook trout (Salvelinus fontinalis) abundance in streams. We quantified brook trout relative abundance in 36 streams over 3 consecutive years by single-pass electrofishing. Relative abundance of brook trout in streams was greatest when lakes were present in the stream network. Lakes had greater influence on relative abundance in headwater streams than in larger streams. These results emphasize the importance of considering lakes as a critical attribute in landscape fish habitat models, many of which focus on terrestrial landscape variables. We discuss potential gains from incorporating the presence of lakes in (i) multiscale habitat models, (ii) analyses of spatiotemporal distribution of thermal refuges, and (iii) metrics of habitat connectivity in lake–stream networks.

Fruit dispersal of two dipterocarp species in a Malaysian rain forest

2001 ◽  
Vol 17 (6) ◽  
pp. 911-917 ◽  
Author(s):  
NORIYUKI OSADA ◽  
HIROSHI TAKEDA ◽  
AKIO FURUKAWA ◽  
MUHAMAD AWANG
Keyword(s):  
Population Dynamics ◽  
Seed Dispersal ◽  
Rain Forest ◽  
Distribution Patterns ◽  
Dispersal Patterns ◽  
Fruit Dispersal

Seed dispersal is the predominant mobile stage for sessile plants, and critically affects the distribution patterns of the species (Nathan & Muller-Landau 2000). Thus, seed dispersal patterns are important in understanding the population dynamics of the species.

scholarly journals On the uncertainty of stream networks derived from elevation data: the error propagation approach

2010 ◽  
Vol 7 (1) ◽  
pp. 767-799 ◽  
Author(s):  
T. Hengl ◽  
G. B. M. Heuvelink ◽  
E. E. van Loon
Keyword(s):  
Open Source ◽  
Cell Size ◽  
Error Propagation ◽  
Grid Cell ◽  
Sequential Gaussian Simulation ◽  
Small Data ◽  
Data Sets ◽  
Stream Networks ◽  
Stream Network ◽  
Gaussian Simulation

Abstract. DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The method is illustrated using two small data sets: Baranja hill (30 m grid cell size; 16 512 pixels; 6367 sampled elevations), and Zlatibor (30 m grid cell size; 15 000 pixels; 2051 sampled elevations). All computations are run in the open source software for statistical computing R: package geoR is used to fit variogram; package gstat is used to run sequential Gaussian simulation; streams are extracted using the open source GIS SAGA via the RSAGA library. The resulting stream error map (Information entropy of a Bernoulli trial) clearly depicts areas where the extracted stream network is least precise – usually areas of low local relief, slightly concave. In both cases, significant parts of the study area (17.3% for Baranja Hill; 6.2% for Zlatibor) show high error (H>0.5) of locating streams. By correlating the propagated uncertainty of the derived stream network with various land surface parameters sampling of height measurements can be optimized so that delineated streams satisfy a required accuracy level. Remaining issue to be tackled is the computational burden of geostatistical simulations: this framework is at the moment limited to small to moderate data sets with several hundreds of points. Scripts and data sets used in this article are available on-line via the http://www.geomorphometry.org/ website and can be easily adopted/adjusted to any similar case study.

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