Effects of spatial scale and choice of statistical model (linear versus tree-based) on determining species–habitat relationships

2012 ◽  
Vol 69 (12) ◽  
pp. 2095-2111 ◽  
Author(s):  
Sapna Sharma ◽  
Pierre Legendre ◽  
Daniel Boisclair ◽  
Stéphane Gauthier
Keyword(s):  
Spatial Scale ◽  
Habitat Quality ◽  
Spatial Data ◽  
Environmental Conditions ◽  
Habitat Structure ◽  
Spatial Scales ◽  
Fish Habitat ◽  
Habitat Associations ◽  
Environmental Data ◽  
Positioning Systems

The choice of spatial scale and modelling technique used to capture species–habitat relationships needs to be considered when ascertaining environmental determinants of habitat quality for species and communities. Fish densities and environmental data were collected at three Laurentian lakes using underwater surveys by four snorkelers collecting fine spatial data acquired through geographic positioning systems. At both fine (20 m) and broad (100 m) spatial scales, tree-based approaches, which incorporated both linear and nonlinear relationships, explained more variation than their linear counterparts. At the finest spatial scale considered (20 m), local environmental conditions, such as habitat structure and heterogeneity, were important determinants of fish habitat selection. At the broadest spatial scale considered (100 m), fish tended to select habitat based on both local environmental features and riparian development. Moran’s eigenvector maps further revealed that fish–habitat associations were operating at broader spatial scales than the predefined analytical units, which can be partially attributed to the spatial structure of environmental conditions acting at spatial scales greater than 100 m. This study highlights the importance of evaluating statistical approaches at different spatial scales to identify key determinants of habitat quality for species, ultimately to assess the effects of perturbations on ecosystems.

The relative importance of local, lateral, and longitudinal variables on the development of habitat quality models for a river

2008 ◽  
Vol 65 (1) ◽  
pp. 61-73 ◽  
Author(s):  
Judith Bouchard ◽  
Daniel Boisclair
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Habitat Quality ◽  
Environmental Conditions ◽  
Spatial Scales ◽  
Fish Habitat ◽  
Explanatory Power ◽  
Relative Importance ◽  
Quality Models ◽  
The Hierarchical Structure

Fish habitat quality models (FHQM) developed for rivers consist of relationships between indices of habitat quality and environmental conditions prevailing within sites (local variables). Given the hierarchical structure of these ecosystems, modeling habitat quality over complete rivers may require the inclusion of variables in FHQM that represent the processes operating over a more complete range of spatial scales. The objectives of this study were to quantify the relative importance of local, lateral (characteristics of the shores), and longitudinal (attributes along the upstream–downstream axis of the river) variables on a FHQM developed for parr of Atlantic salmon (Salmo salar). Parr densities (an index of habitat quality) and local, lateral, and longitudinal variables were estimated in 32 reaches of 200 m. FHQM were developed using analytical units (AU) of 50, 100, and 200 m (length of AU in the upstream–downstream axis of the river). The structure and the explanatory power of FHQM were affected by the AU size. In the study river, 98% of the explanatory power of FHQM was imputed to the effect of local variables.

Impacts of spatial scales of fisheries and environmental data on catch per unit effort standardisation

2009 ◽  
Vol 60 (12) ◽  
pp. 1273 ◽  
Author(s):  
Siquan Tian ◽  
Yong Chen ◽  
Xinjun Chen ◽  
Liuxiong Xu ◽  
Xiaojie Dai
Keyword(s):  
Spatial Scale ◽  
Stock Assessment ◽  
Spatial Scales ◽  
Additive Models ◽  
Environmental Data ◽  
Catch Per Unit Effort ◽  
The North ◽  
Unit Effort ◽  
Data Collection And Analysis ◽  
North Western

Spatial scale is an important factor that needs to be considered in data collection and analysis in ecological studies. Studies focusing on the quantitative evaluation of impacts of spatial scales are, however, limited in fisheries. Using the Chinese squid-jigging fishery in the north-western Pacific Ocean as an example, we evaluated impacts of spatial scale used in grouping fisheries and environmental data on the standardisation of fisheries catch per unit effort (CPUE). We developed 18 scenarios of different spatial scales with a combination of three latitudinal levels (0.5°, 1° and 2°) and six longitudinal levels (0.5°, 1°, 2°, 3°, 4° and 5°) to aggregate the data. We then applied generalised additive models to analyse the 18 scenarios of data for the CPUE standardisation, and quantified differences among the scenarios. This study shows that longitudinal and latitudinal spatial scale and size of the spatial area for data aggregation can greatly influence the standardisation of CPUE. We recommend that similar studies be undertaken whenever possible to evaluate the roles of spatial scales and to identify the optimal spatial scale for data aggregations in the standardisation of CPUE and fisheries stock assessment.

Quantifying habitat associations in marine fisheries: a generalization of the Kolmogorov–Smirnov statistic using commercial logbook records linked to archived environmental data

2003 ◽  
Vol 60 (4) ◽  
pp. 370-378 ◽  
Author(s):  
Julie A Reynolds
Keyword(s):  
Environmental Variables ◽  
Fish Habitat ◽  
Habitat Preferences ◽  
The United States ◽  
Habitat Associations ◽  
Environmental Data ◽  
Cumulative Distribution ◽  
Test Statistic ◽  
Kolmogorov Smirnov ◽  
Methodological Advance

Understanding species–habitat associations is critical for designing marine reserves, defining essential fish habitat, and predicting the impacts of climate change on fisheries. For many species, however, there is a paucity of fisheries-independent data that simultaneously track abundance and environmental variables, as is the case for widow rockfish (Sebastes entomelas), a commercially important fishery off the west coast of the United States. In this paper, I generalize a previous approach to identifying habitat associations so that fisheries-dependent data can be used. In analyzing Oregon commercial logbook records and archived environmental data from the National Oceanographic Data Center, I found three environmental variables (bottom depth, vertical depth of fish in the water column, and temperature) to be statistically adequate. Using a generalized Kolmogorov–Smirnov test statistic, I compared an empirically derived cumulative distribution function (CDF) of the habitat sampled to a CDF weighted by widow rockfish catch. Results suggest that the significant habitat association for widow rockfish includes bottom depths between 136 and 298 m, vertical depths between 101 and 197 m, and temperatures between 7.1 and 8.1°C. This novel use of commercial logbook data, which links disparate data sources and explicitly accounts for unequal spatial sampling, is a methodological advance that also provides initial insights into widow rockfish habitat preferences.

scholarly journals A Variance-decomposition Approach to Investigating Multiscale Habitat Associations

The Condor ◽  
2006 ◽  
Vol 108 (1) ◽  
pp. 47-58 ◽  
Author(s):  
Joshua J. Lawler ◽  
Thomas C. Edwards
Keyword(s):  
Spatial Scale ◽  
Spatial Scales ◽  
Analytical Techniques ◽  
Variance Decomposition ◽  
Relative Strength ◽  
Habitat Associations ◽  
Alternative Methods ◽  
Decomposition Approach ◽  
Analytical Technique ◽  
Multiple Spatial Scales

Abstract The recognition of the importance of spatial scale in ecology has led many researchers to take multiscale approaches to studying habitat associations. However, few of the studies that investigate habitat associations at multiple spatial scales have considered the potential effects of cross-scale correlations in measured habitat variables. When cross-scale correlations in such studies are strong, conclusions drawn about the relative strength of habitat associations at different spatial scales may be inaccurate. Here we adapt and demonstrate an analytical technique based on variance decomposition for quantifying the influence of cross-scale correlations on multiscale habitat associations. We used the technique to quantify the variation in nest-site locations of Red-naped Sapsuckers (Sphyrapicus nuchalis) and Northern Flickers (Colaptes auratus) associated with habitat descriptors at three spatial scales. We demonstrate how the method can be used to identify components of variation that are associated only with factors at a single spatial scale as well as shared components of variation that represent cross-scale correlations. Despite the fact that no explanatory variables in our models were highly correlated (r < 0.60), we found that shared components of variation reflecting cross-scale correlations accounted for roughly half of the deviance explained by the models. These results highlight the importance of both conducting habitat analyses at multiple spatial scales and of quantifying the effects of cross-scale correlations in such analyses. Given the limits of conventional analytical techniques, we recommend alternative methods, such as the variance-decomposition technique demonstrated here, for analyzing habitat associations at multiple spatial scales.

scholarly journals Experimental dispersal reveals characteristic scales of biodiversity in a natural landscape

2017 ◽  
Vol 114 (17) ◽  
pp. 4447-4452 ◽  
Author(s):  
Rachel M. Germain ◽  
Sharon Y. Strauss ◽  
Benjamin Gilbert
Keyword(s):  
Spatial Scale ◽  
Environmental Conditions ◽  
Biological Diversity ◽  
Spatial Scales ◽  
Dispersal Limitation ◽  
Negative Effects ◽  
Local Diversity ◽  
Large Numbers ◽  
Regional Process ◽  
Regional Processes

Ecological theory posits that dispersal among habitat patches links local communities and is a key “regional” process that maintains biological diversity. However, manipulations required to experimentally test regional processes are infeasible for most systems, and thus more work is needed to detect the scales at which regional processes manifest and their overall effect on diversity. In a Californian grassland, a hotspot for global biodiversity, we used a seed vacuum to increase dispersal at spatial scales varying from 1 m to 10 km while maintaining a realistic spatial structure of species pools and environmental conditions. We found that dispersal limitation has a profound influence on diversity; species richness increased with the spatial scale of seed mixing, doubling in plots that received seed from large (≥5 km) compared with small (≤5 m) scales. This increase in diversity corresponded to an increase in how well species distributions were explained by environmental conditions, from modest at small scales (R2= 0.34) to strong at large scales (R2= 0.52). Responses to the spatial scale of seed mixing were nonlinear, with no differences below 5 m or above 5 km. Nonlinearities were explained by homogeneity of environmental conditions below 5 m and by a lack of additional variation in the species pool above 5 km. Our approach of manipulating natural communities at different spatial scales reveals (i) nonlinear transitions in the importance of environmental sorting and dispersal, and (ii) the negative effects of dispersal limitation on local diversity, consistent with previous research suggesting that large numbers of species are headed toward regional extinction.

Giscience in the Mountain Geography

2010 ◽  
Vol 27 (1-2) ◽  
pp. 81-90
Author(s):  
Krishna Poudel
Keyword(s):  
Spatial Data ◽  
Environmental Data ◽  
Temporal Data ◽  
Observation Systems ◽  
Research Findings ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Mountain Geography ◽  
Different Sources

Mountains have distinct geography and are dynamic in nature compared to the plains. 'Verticality' and 'variation' are two fundamental specificities of the mountain geography. They possess distinct temporal and spatial characteristics in a unique socio-cultural setting. There is an ever increasing need for spatial and temporal data for planning and management activities; and Geo Information (GI) Science (including Geographic Information and Earth Observation Systems). This is being recognized more and more as a common platform for integrating spatial data with social, economic and environmental data and information from different sources. This paper investigates the applicability and challenges of GISscience in the context of mountain geography with ample evidences and observations from the mountain specific publications, empirical research findings and reports. The contextual explanation of mountain geography, mountain specific problems, scientific concerns about the mountain geography, advances in GIScience, the role of GIScience for sustainable development, challenges on application of GIScience in the contexts of mountains are the points of discussion. Finally, conclusion has been made with some specific action oriented recommendations.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Simulations with Australian dragon lizards suggest movement-based signal effectiveness is dependent on display structure and environmental conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xue Bian ◽  
Angela Pinilla ◽  
Tom Chandler ◽  
Richard Peters
Keyword(s):  
Research Methods ◽  
Environmental Conditions ◽  
Comparative Studies ◽  
Habitat Structure ◽  
Signal Transmission ◽  
Lizard Species ◽  
Local Adaptations ◽  
Signal Divergence ◽  
Colour Signals ◽  
Optimal Signal

AbstractHabitat-specific characteristics can affect signal transmission such that different habitats dictate the optimal signal. One way to examine how the environment influences signals is by comparing changes in signal effectiveness in different habitats. Examinations of signal effectiveness between different habitats has helped to explain signal divergence/convergence between populations and species using acoustic and colour signals. Although previous research has provided evidence for local adaptations and signal divergence in many species of lizards, comparative studies in movement-based signals are rare due to technical difficulties in quantifying movements in nature and ethical restrictions in translocating animals between habitats. We demonstrate herein that these issues can be addressed using 3D animations, and compared the relative performance of the displays of four Australian lizard species in the habitats of each species under varying environmental conditions. Our simulations show that habitats differentially affect signal performance, and an interaction between display and habitat structure. Interestingly, our results are consistent with the hypothesis that the signal adapted to the noisier environment does not show an advantage in signal effectiveness, but the noisy habitat was detrimental to the performance of all displays. Our study is one of the first studies for movement-based signals that directly compares signal performance in multiple habitats, and our approach has laid the foundation for future investigations in motion ecology that have been intractable to conventional research methods.

scholarly journals Effects of alternate reef states on coral reef fish habitat associations

2011 ◽  
Vol 94 (2) ◽  
pp. 421-429 ◽  
Author(s):  
David Lecchini ◽  
Laure Carassou ◽  
Bruno Frédérich ◽  
Yohei Nakamura ◽  
Suzanne C. Mills ◽  
...  
Keyword(s):  
Coral Reef ◽  
Reef Fish ◽  
Coral Reef Fish ◽  
Fish Habitat ◽  
Habitat Associations
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