scholarly journals A theory for ecological survey methods to map individual distributions

2017 ◽  
Author(s):  
Nao Takashina ◽  
Maria Beger ◽  
Buntarou Kusumoto ◽  
Suren Rathnayake ◽  
Hugh P. Possingham
Keyword(s):  
Point Processes ◽  
Spatial Scales ◽  
Survey Methods ◽  
Survey Method ◽  
Management Decision ◽  
Spatially Explicit ◽  
Mapping Unit ◽  
Ecological Survey ◽  
Multiple Spatial Scales ◽  
Management Actions

AbstractSpatially-explicit approaches are widely recommended for ecosystem management. The quality of the data, such as presence/absence or habitat maps, affects the management actions recommended, and is, therefore, key to management success. However, available data are often biased and incomplete. Previous studies have advanced ways to resolve data bias and missing data, but questions remain about how we design ecological surveys to develop a dataset through field surveys. Ecological surveys may have multiple spatial scales, including the spatial extent of the target ecosystem (observation window), the resolution for mapping individual distributions (mapping unit), and the survey area within each mapping unit (sampling unit). We developed an ecological survey method for mapping individual distributions by applying spatially-explicit stochastic models. We used spatial point processes to describe individual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and individual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total individuals covered by the presence mapped patches. Tradeoffs were found between these quantities and the map resolution, associated with equivalent asymptotic behaviors for both metrics at sufficiently small and large mapping unit scales. Our approach enabled consideration of the effect of multiple spatial scales in surveys, and estimation of the survey outcomes such as the presence mapped fraction and the number of individuals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering.

scholarly journals 21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1084
Author(s):  
John Hogland ◽  
Christopher J. Dunn ◽  
James D. Johnston
Keyword(s):  
Decision Making ◽  
Expert Knowledge ◽  
Risk Mitigation ◽  
Spatial Scales ◽  
Sustainable Use ◽  
Management Decision ◽  
Spatially Explicit ◽  
Wildfire Risk ◽  
Natural Systems ◽  
Desired Future Conditions

Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape.

Inhibition of coral settlement at multiple spatial scales by a pervasive algal competitor

2019 ◽  
Vol 612 ◽  
pp. 29-42 ◽  
Author(s):  
NR Evensen ◽  
C Doropoulos ◽  
KM Morrow ◽  
CA Motti ◽  
PJ Mumby
Keyword(s):  
Spatial Scales ◽  
Coral Settlement ◽  
Multiple Spatial Scales

scholarly journals Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2508
Author(s):  
Loredana Oreti ◽  
Diego Giuliarelli ◽  
Antonio Tomao ◽  
Anna Barbati
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Parametric Method ◽  
Mixed Forest ◽  
Mixed Stands ◽  
Mapping Unit ◽  
Mixed Forests ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

Assessment of clogging in constructed wetlands by saturated hydraulic conductivity measurements

2019 ◽  
Vol 79 (2) ◽  
pp. 314-322 ◽  
Author(s):  
F. Licciardello ◽  
R. Aiello ◽  
V. Alagna ◽  
M. Iovino ◽  
D. Ventura ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Hydraulic Conductivity ◽  
Constructed Wetlands ◽  
Spatial Scales ◽  
Pilot Scale ◽  
Laboratory Scale ◽  
Test Method ◽  
Conductivity Measurements ◽  
Multiple Spatial Scales ◽  
Ks Values

Abstract This study aims at defining a methodology to evaluate Ks reductions of gravel material constituting constructed wetland (CW) bed matrices. Several schemes and equations for the Lefranc's test were compared by using different gravel sizes and at multiple spatial scales. The falling-head test method was implemented by using two steel permeameters: one impervious (IMP) and one pervious (P) on one side. At laboratory scale, mean K values for a small size gravel (8–15 × 10−2 m) measured by the IMP and the P permeameters were equal to 19,466 m/d and 30,662 m/d, respectively. Mean Ks values for a big size gravel (10–25 × 10−2 m) measured by the IMP and the P permeameters were equal to 12,135 m/d and 20,866 m/d, respectively. Comparison of Ks values obtained by the two permeameters at laboratory scale as well as a sensitivity analysis and a calibration, lead to the modification of the standpipe equation, to evaluate also the temporal variation of the horizontal Ks. In particular, both permeameters allow the evaluation of the Ks decreasing after 4 years-operation and 1–1.5 years' operation of the plants at full scale (filled with the small size gravel) and at pilot scale (filled with the big size gravel), respectively.

scholarly journals FORMULATION OF THE HEAT CONDUCTION EQUATION FOR HETEROGENEOUS MEDIA WITH MULTIPLE SPATIAL SCALES USING REITERATED HOMOGENIZATION

2016 ◽  
Vol 15 (1) ◽  
pp. 96
Author(s):  
E. Iglesias-Rodríguez ◽  
M. E. Cruz ◽  
J. Bravo-Castillero ◽  
R. Guinovart-Díaz ◽  
R. Rodríguez-Ramos ◽  
...  
Keyword(s):  
Heat Conduction ◽  
Small Parameter ◽  
Heat Conduction Equation ◽  
Heterogeneous Media ◽  
Spatial Scales ◽  
Conduction Equation ◽  
Small Scale ◽  
Multiple Spatial Scales ◽  
Effective Coefficients ◽  
Reiterated Homogenization

Heterogeneous media with multiple spatial scales are finding increased importance in engineering. An example might be a large scale, otherwise homogeneous medium filled with dispersed small-scale particles that form aggregate structures at an intermediate scale. The objective in this paper is to formulate the strong-form Fourier heat conduction equation for such media using the method of reiterated homogenization. The phases are assumed to have a perfect thermal contact at the interface. The ratio of two successive length scales of the medium is a constant small parameter ε. The method is an up-scaling procedure that writes the temperature field as an asymptotic multiple-scale expansion in powers of the small parameter ε . The technique leads to two pairs of local and homogenized equations, linked by effective coefficients. In this manner the medium behavior at the smallest scales is seen to affect the macroscale behavior, which is the main interest in engineering. To facilitate the physical understanding of the formulation, an analytical solution is obtained for the heat conduction equation in a functionally graded material (FGM). The approach presented here may serve as a basis for future efforts to numerically compute effective properties of heterogeneous media with multiple spatial scales.

Length Variation in Age-0 Westslope Cutthroat Trout at Multiple Spatial Scales

2008 ◽  
Vol 28 (5) ◽  
pp. 1529-1540 ◽  
Author(s):  
Kathleen E. McGrath ◽  
J. Michael Scott ◽  
Bruce E. Rieman
Keyword(s):  
Spatial Scales ◽  
Cutthroat Trout ◽  
Length Variation ◽  
Westslope Cutthroat Trout ◽  
Multiple Spatial Scales

scholarly journals Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys

2016 ◽  
Vol 3 (10) ◽  
pp. 160368 ◽  
Author(s):  
Campbell Murn ◽  
Graham J. Holloway
Keyword(s):  
Detection Probability ◽  
Spatial Scales ◽  
Survey Methods ◽  
A Priori ◽  
Low Density ◽  
Occupancy Models ◽  
Imperfect Detection ◽  
Factors Affecting ◽  
Sources Of Variation ◽  
Time Of Year

Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.

Applying systematic conservation planning to improve the allocation of restoration actions at multiple spatial scales

2021 ◽  
Author(s):  
Ben L. Gilby ◽  
Andrew D. Olds ◽  
Christopher J. Brown ◽  
Rod M. Connolly ◽  
Christopher J. Henderson ◽  
...  
Keyword(s):  
Conservation Planning ◽  
Spatial Scales ◽  
Systematic Conservation Planning ◽  
Multiple Spatial Scales

Assessing habitat selection by foraging egrets in salt marshes at multiple spatial scales

Wetlands ◽  
2006 ◽  
Vol 26 (2) ◽  
pp. 307-312 ◽  
Author(s):  
Carol Lynn Trocki ◽  
Peter W. C. Paton
Keyword(s):  
Habitat Selection ◽  
Salt Marshes ◽  
Spatial Scales ◽  
Multiple Spatial Scales
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