Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico

Scientific Reports 6, Article number: 22615, Published Online 03 March 2016

By Jason J. Roberts, Benjamin D. Best, Laura Mannocci, Ei Fujioka, Patrick N. Halpin, Debra L. Palka, Lance P. Garrison, Keith D. Mullin, Timothy V. N. Cole, Christin B. Khan, William A. McLellan, D. Ann Pabst, and Gwen G. Lockhart

“Cetaceans are protected worldwide but vulnerable to incidental harm from an expanding array of human activities at sea. Managing potential hazards to these highly-mobile populations increasingly requires a detailed understanding of their seasonal distributions and habitats. Pursuant to the urgent need for this knowledge for the U.S. Atlantic and Gulf of Mexico, we integrated 23 years of aerial and shipboard cetacean surveys, linked them to environmental covariates obtained from remote sensing and ocean models, and built habitat-based density models for 26 species and 3 multi-species guilds using distance sampling methodology.

Predicted mean density of small delphinoids.

Predicted mean density of small delphinoids.

“In the Atlantic, for 11 well-known species, model predictions resembled seasonal movement patterns previously suggested in the literature. For these we produced monthly mean density maps. For lesser-known taxa, and in the Gulf of Mexico, where seasonal movements were less well described, we produced year-round mean density maps. The results revealed high regional differences in small delphinoid densities, confirmed the importance of the continental slope to large delphinoids and of canyons and seamounts to beaked and sperm whales, and quantified seasonal shifts in the densities of migratory baleen whales. The density maps, freely available online, are the first for these regions to be published in the peer-reviewed literature.”

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

PLOS_ONEPLOS One, Published 20 May 2015

By M. Ali Jalali, Daniel Ierodiaconou, Harry Gorfine, Jacquomo Monk, and Alex Rattray
“Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs.
Cumulative hotspot distribution map. Cumulative CPUE hotspot map overlays (based on the number of years that CPUE was clustered) for (A) Julia Bank and (B) Discovery Bay over LiDAR derived hillshade.

Cumulative hotspot distribution map. Cumulative CPUE hotspot map overlays (based on the number of years that CPUE was clustered) for (A) Julia Bank and (B) Discovery Bay over LiDAR derived hillshade.

“Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.”

OGC Requests Comment on LandInfra Conceptual Model

OGC_newThe Open Geospatial Consortium (OGC(R)) membership has issued a Request for Comments on the OGC LandInfra Conceptual Model.

This document, the first public draft of the OGC’s proposed UML conceptual model for land parcels and the built environment, communicates the proposed intent and content of a new candidate OGC standard to be called the OGC InfraGML Encoding Standard. The UML conceptual model establishes a single set of consistent concepts that could be implemented in GML (as InfraGML) or in other encoding mechanisms.

After reviewing the existing LandXML format, the OGC Land and Infrastructure Domain Working Group (LandInfraDWG) decided that a fresh start standard was warranted. The new standard would have a use case driven subset of LandXML functionality, but it would be consistent with the OGC standards baseline, implemented with the OGC Geography Markup Language (GML), and supported by a Unified Model Language (UML) conceptual model. Called InfraGML, this new standard would: be supported by a recognized Standards Developing Organization, OGC align with existing OGC (and TC211 and SQL/MM) standards, including the OGC Modular Specification benefit from functionality already supported by GML, including features, geometry, coordinate reference systems, linear referencing, and surface modeling (TIN) initially focus on alignments/roads, survey, and land parcels, the subject areas for which there are identified needs and committed resources for development using modular extensions, be able to expand into other areas (e.g., “wet” infrastructure pipe networks) as resources become available be use-case driven be based on a UML conceptual model developed prior to any encoding, such as GML have more up-to-date functionality be synchronized with the concurrent efforts by buildingSMART International in their development of Infrastructure-based Industry Foundation Classes (IFCs), and be more easily integrated with TransXML and OGC CityGML.

The work on buildingSMART International’s IFC Alignment Extension has been carried out by their P6 project team in strong collaboration with OGC Land&Infra Group. The use cases and the conceptual model are results of the joint work.

“This cooperation between buildingSMART International and the OGC will make it possible for software to directly map IFC alignment models to InfraGML and vice versa,” explained Richard Petrie, chief executive of buildingSMART International. “This represents an important milestone in reaching our shared goal of vendor-neutral standards that enable integration of geospatial information and information about the built environment.”

Scott Simmons, Executive Director of the OGC Standards Program, said, “The joint coordination of OGC and buildingSMART International in developing this conceptual model is an example of the benefits of proactive engagement between Standards Development Organizations. Our working together will result in a standard better suited to both communities and we’ll accomplish this much more quickly than if we worked separately now and harmonized later.”

The OGC LandInfra Conceptual Model and Request for Comment are available at https://portal.opengeospatial.org/files/61594.

[Source: OGC press release]

A GIS-based Relational Data Model for Multi-dimensional Representation of River Hydrodynamics and Morphodynamics

EMS-S13648152Environmental Modelling & Software, Volume 65, March 2015, Pages 79–93

By Dongsu Kim, Marian Muste, and Venkatesh Merwade

“Highlights

  • Represent river data in a curvilinear coordinate system to support river channel oriented spatial analyses.
  • Represent multidimensional river features through points, lines, polygons, and volumes.
  • Represent simulated gridded data for river channels that can be readily coupled with observed data.
  • Represent spatio-temporal evolution of dynamic river objects using Eulerian or Lagrangian observational frameworks.
  • Efficiently store and retrieve data acquired in-situ along with the ancillary metadata.

“The emerging capabilities of the geo-based information systems to integrate spatial and temporal attributes of in-situ measurements is a long-waited solution to efficiently organize, visualize, and analyze the vast amount of data produced by the new generations of river instruments. This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

“The proposed model, labeled Arc River, is built in close connection with the existing Arc Hydro data model developed for water-related features to ensure the connection of the river characteristics with their floodplains and watersheds. This paper illustrates Arc River data model capabilities in conjunction with Acoustic Doppler Current Profiler measurements to demonstrate that essential river morphodynamics and hydrodynamics aspects can be described using data on the flow and its boundaries.”

Two New Maps that Could Change the World

Maps have long been used by people to help navigate and understand our world. Early maps guided early humans to basic necessities such as food and water.

Today, the world is changing rapidly, and it’s difficult for traditional maps to keep up with the pace of that change. To help us keep pace with our evolving planet, we need something better. We need new, more comprehensive maps.

Esri has developed two new maps—the most detailed population map in the world and the most detailed ecological land unit map in the world—to help address the challenges we face and make our world a better place.


A New Map of World Population

Esri has compiled a human geography database of demographics and statistics about all countries in the world and has mapped this data using a new, innovative methodology.

Advances in technology are changing the type, quantity, quality, and timeliness of information available. The ideal human geography database would include uniform social and demographic information about all human populations on the globe. It would include population, household, housing unit, business, and economic information that would allow determination of societal characteristics at any scale from macro to micro.

pop-new-york_600

Esri has developed the most detailed population map in the world.

Esri’s new world population map takes advantage of this new information to track and estimate populations to support better decision making. This new model of world population will allow comparative studies and accurate depiction of statistics to ad hoc areas. Population is modeled from imagery, road networks, and populated place locations to create an urbanization likelihood score.

“The global model is currently complete for approximately 130 countries, allowing for detailed reporting that will show the demographics for any desired geography such as a watershed, drive-time area, or an area affected by a disaster,” said Earl Nordstrand, Data Product Manager, Esri. “Additionally, the likelihood surface has been used to create a global population map by obtaining the latest census population data for the remaining areas of the world.”


A New Map of World Ecology

The U.S. Geological Survey (USGS) and Esri recently announced the publication of the most detailed global ecological land units (ELUs) map in the world.

“The Global ELUs map portrays a systematic division and classification of the biosphere using ecological and physiographic land surface features,” notes Roger Sayre, Ph.D., Senior Scientist for Ecosystems, USGS.

gelu_img

Esri and USGS have developed the most detailed global ecological land units (ELUs) map in the world.

This exciting new global content provides a science platform for better understanding and accounting of the world’s resources.  Scientists, land managers, conservationists, developers and the public will use this map to improve regional, national and global resource management, planning and decision making.

“The ELUs provide an accounting framework to assess ecosystem services, such as carbon storage, soil formation, as well as risks such as, environmental degradation,” said Randy Vaughan, Manager of Content Engineering, Esri.  “The ELUs also lend themselves to the study of ecological diversity, rarity and evolutionary isolation.  For example we can identify whether the most diverse landscapes in terms of proximity to the most unique ELUs are protected. Understanding diversity can point the way to conservation and preservation planning.”

While ELUs do not definitively characterize ecosystems at multiple scales, they do provide information and pointers to the ecological patterns of the globe.  “They will be useful for constructing research agendas and for understanding global processes such as climate change,” added Sayre. “For example, the data will be important to the study of environmental change.  The automated approach to the objective classification of ELUs means that the mapping can be updated as better or more current input layers become available.”


Working Together

Separately, these two maps are important, and can be used in a variety of ways to address important local, regional, and global issues. Used together, these two new maps can give us an even better picture of the links between the human and natural components of our evolving world. “Population density and distributions are important indicator of both the demands and impacts on landscape,” said Vaughan.  “As such, population data can be used as another parameter to infer and understand the environmental processes expressed in the ecological land units.”

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How can you get access to the Global population map?

  1. You can access the map here http://pm.maps.arcgis.com/home/item.html?id=ac0401d78fa24a10a9151ffe50f35afe

How can you get access to the Global ELUs map?

  1. Introductory Story Map to the ecological land units: esriurl.com/elu
  2. Explore the online application: esriurl.com/EcoTapestry
  3. Learn more about ecological land units: www.aag.org/global_ecosystems
  4. Get started using this content in ArcGIS: ArcGIS Online Landscape Layers Group

SDM Toolbox: A Python-based GIS Toolkit for Landscape Genetic, Biogeographic, and Species Distribution Model Analyses

MEE_CoverMethods in Ecology and Evolution 2014, 5, 694–700

By Jason L. Brown

“1. Species distribution models (SDMs) are broadly used in ecological and evolutionary studies. Almost all SDM methods require extensive data preparation in a geographic information system (GIS) prior to model building. Often, this step is cumbersome and, if not properly done, can lead to poorly parameterized models or in some cases, if too difficult, prevents the realization of SDMs. Further, for many studies, the creation of SDMs is not the final result and the post-modelling processing can be equally arduous as other steps.

Illustrative overview of SDMtoolbox. Basic Tools. SDMtoolbox contains 19 basic tools for converting and batch processing shapefile and raster data.

Illustrative overview of SDMtoolbox. Basic Tools. SDMtoolbox contains 19 basic tools for converting and batch processing shapefile and raster data.

2. SDMtoolbox is designed to facilitate many complicated pre- and post-processing steps commonly required for species distribution modelling and other geospatial analyses. SDMtoolbox consists of 59 Python script-based GIS tools developed and compiled into a single interface.

3. A large set of the tools were created to complement SDMs generated inMaxent or to improve the predictive performance of SDMs created inMaxent. However, SDMtoolbox is not limited to analyses of Maxent models, andmany tools are also available for additional analyses or general geospatial processing: for example, assessing landscape connectivity of haplotype networks (using least-cost corridors or least-cost paths); correcting SDM over-prediction; quantifying distributional changes between current and future SDMs; or for calculating several biodiversity metrics, such as corrected weighted endemism.

4. SDMtoolbox is a free comprehensive python-based toolbox for macroecology, landscape genetic and evolutionary studies to be used in ArcGIS 10.1 (or higher) with the Spatial Analyst extension. The toolkit simplifies many GIS analyses required for species distribution modelling and other analyses, alleviating the need for repetitive and time-consuming climate data pre-processing and post-SDManalyses.”

GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 523-539

By Christos Chalkias, Maria Ferentinou, and Christos Polykretis

“The main aim of this paper is landslide susceptibility assessment using fuzzy expert-based modeling. Factors that influence landslide occurrence, such as elevation, slope, aspect, lithology, land cover, precipitation and seismicity were considered. Expert-based fuzzy weighting (EFW) approach was used to combine these factors for landslide susceptibility mapping (Peloponnese, Greece). This method produced a landslide susceptibility map of the investigated area. The landslides under investigation have more or less same characteristics: lateral based and downslope shallow movement of soils or rocks.

ijgi-03-00523-ag

“The validation of the model reveals, that predicted susceptibility levels are found to be in good agreement with the past landslide occurrences. Hence, the obtained landslide susceptibility map could be acceptable, for landslide hazard prevention and mitigation at regional scale. ”