Landscape Influences on Stream Habitats and Biological Assemblages

<em>Abstract.</em>—Habitat data collected at three spatial scales (catchments, reaches, and sites) were used to predict individual fish species occurrences and assemblage structure at 150 sites in the Kansas River basin. Habitat measurements for the catchments and reaches of each sample site were derived from available geographic information system (GIS) data layers. Habitat measurements at the sample sites were collected at the time of fish sampling. Because habitat measurements are typically more difficult to collect as the spatial scale of sampling decreases (i.e., field measurement versus a GIS analysis), our objective was to quantify the relative increase in predictive ability as we added habitat measurements from increasingly finer spatial scales. Although the addition of site-scale habitat variables increased the predictive performance of models, the relative magnitude of these increases was small. This was largely due to the general association of species occurrences with measurements of catchment area and soil factors, both of which could be quantified with a GIS. Habitat measurements taken at different spatial scales were often correlated; however, a partial canonical correspondence analysis showed that catchment- scale habitat measurements accounted for a slightly higher percent of the variation in fish-assemblage structure across the 150 sample sites than reach- or site-scale habitat measurements. We concluded that field habitat measurements were less informative for predicting species occurrences within the Kansas River basin than catchment data. However, because of the hierarchical nature of the geomorphological processes that form stream habitats, a refined understanding of the relationship between catchment-, reach- and site-scale habitats provides a mechanistic understanding of fish–habitat relations across spatial scales.

2010 ◽  
Vol 67 (1) ◽  
pp. 143-156 ◽  
Author(s):  
Darren J. Thornbrugh ◽  
Keith B. Gido

We found that riverine confluences had localized effects (within 20 km) on stream fish assemblages of the Kansas River basin. The majority of variation in fish assemblages occurred from east to west and along a stream size gradient. After controlling for the influences of longitude and stream size, distance of sample sites from streams ≥ 5th order accounted for a small proportion of taxonomic variability. However, species richness was significantly higher and assemblage structure was different in tributary stream segments directly connected to larger-ordered streams, suggesting that the effects of spatial position within this stream network were greatest in close proximity to tributary–mainstem confluences. Fish collections from three intensively sampled tributaries also indicated an abrupt change in species assemblages between mainstem river sites and tributary sample sites above confluences, followed by a gradual taxonomic change with increasing distance up to 20 km from the confluence. Changes in fish assemblages were associated with the reduced abundance of adult stream species near the confluence with the mainstem, rather than the occurrences of riverine species in the tributary.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.


2002 ◽  
Vol 32 (7) ◽  
pp. 1109-1125 ◽  
Author(s):  
Theresa B Jain ◽  
Russell T Graham ◽  
Penelope Morgan

Many studies have assessed tree development beneath canopies in forest ecosystems, but results are seldom placed within the context of broad-scale biophysical factors. Mapped landscape characteristics for three watersheds, located within the Coeur d'Alene River basin in northern Idaho, were integrated to create a spatial hierarchy reflecting biophysical factors that influence western white pine (Pinus monticola Dougl. ex D. Don) development under a range of canopy openings. The hierarchy included canopy opening, landtype, geological feature, and weathering. Interactions and individual-scale contributions were identified using stepwise log–linear regression. The resulting models explained 68% of the variation for estimating western white pine basal diameter and 64% for estimating height. Interactions among spatial scales explained up to 13% of this variation and better described vegetation response than any single spatial scale. A hierarchical approach based on biophysical attributes is an excellent method for studying plant and environment interactions.


2021 ◽  
Author(s):  
Santiago Duarte ◽  
Gerald Corzo ◽  
Germán Santos

&lt;p&gt;Bogot&amp;#225;&amp;#8217;s River Basin, it&amp;#8217;s an important basin in Cundinamarca, Colombia&amp;#8217;s central region. Due to the complexity of the dynamical climatic system in tropical regions, can be difficult to predict and use the information of GCMs at the basin scale. This region is especially influenced by ENSO and non-linear climatic oscillation phenomena. Furthermore, considering that climatic processes are essentially non-linear and possibly chaotic, it may reduce the effectiveness of downscaling techniques in this region.&amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we try to apply chaotic downscaling to see if we could identify synchronicity that will allow us to better predict. It was possible to identify clearly the best time aggregation that can capture at the best the maximum relations between the variables at different spatial scales. Aside this research proposes a new combination of multiple attractors. Few analyses have been made to evaluate the existence of synchronicity between two or more attractors. And less analysis has considered the chaotic behaviour in attractors derived from climatic time series at different spatial scales.&amp;#160;&lt;/p&gt;&lt;p&gt;Thus, we evaluate general synchronization between multiple attractors of various climate time series. The Mutual False Nearest Neighbours parameter (MFNN) is used to test the &amp;#8220;Synchronicity Level&amp;#8221; (existence of any type of synchronization) between two different attractors. Two climatic variables were selected for the analysis: Precipitation and Temperature. Likewise, two information sources are used: At the basin scale, local climatic-gauge stations with daily data and at global scale, the output of the MPI-ESM-MR model with a spatial resolution of 1.875&amp;#176;x1.875&amp;#176; for both climatic variables (1850-2005). In the downscaling process, two RCP (Representative Concentration Pathways)&amp;#160; scenarios are used, RCP 4.5 and RCP 8.5.&lt;/p&gt;&lt;p&gt;For the attractor&amp;#8217;s reconstruction, the time-delay is obtained through the&amp;#160; Autocorrelation and the Mutual Information functions. The False Nearest Neighbors method (FNN) allowed finding the embedding dimension to unfold the attractor. This information was used to identify deterministic chaos at different times (e.g. 1, 2, 3 and 5 days) and spatial scales using the Lyapunov exponents. These results were used to test the synchronicity between the various chaotic attractor&amp;#8217;s sets using the MFNN method and time-delay relations. An optimization function was used to find the attractor&amp;#8217;s distance relation that increases the synchronicity between the attractors.&amp;#160; These results provided the potential of synchronicity in chaotic attractors to improve rainfall and temperature downscaling results at aggregated daily-time steps. Knowledge of loss information related to multiple reconstructed attractors can provide a better construction of downscaling models. This is new information for the downscaling process. Furthermore, synchronicity can improve the selection of neighbours for nearest-neighbours methods looking at the behaviour of synchronized attractors. This analysis can also allow the classification of unique patterns and relationships between climatic variables at different temporal and spatial scales.&lt;/p&gt;


2018 ◽  
Vol 10 (12) ◽  
pp. 1881 ◽  
Author(s):  
Yueyuan Zhang ◽  
Yungang Li ◽  
Xuan Ji ◽  
Xian Luo ◽  
Xue Li

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.


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