scholarly journals Expansion Speed as a Generic Measure of Spread for Alien Species

2019 ◽  
Vol 68 (2) ◽  
pp. 227-252 ◽  
Author(s):  
Hanno Sandvik
Keyword(s):  
Alien Species ◽  
Ecological Impact ◽  
Observation Data ◽  
Spatial Presence ◽  
Real World Data ◽  
Detection Rates ◽  
Area Of Occupancy ◽  
Expansion Speed ◽  
Spatio Temporal ◽  
Anthropogenic Transport

Abstract The ecological impact of alien species is a function of the area colonised. Impact assessments of alien species are thus incomplete unless they take the spatial component of invasion processes into account. This paper describes a measure, termed expansion speed, that quantifies the speed with which a species increases its spatial presence in an assessment area. It is based on the area of occupancy (AOO) and can be estimated from grid occupancies. Expansion speed is defined as the yearly increase in the radius of a coherent circle having the same area as the AOO, irrespective of whether the increase is due to natural dispersal or anthropogenic transport. Two methods for estimating expansion speed are presented: one that requires several years of spatio-temporal observation data and explicitly takes detection rates into account; and one that can be used under a situation with sparse data. Using simulations and real-world data from natural history collections, it is shown that the method provides a good fit to observational datasets. Expansion speed has several valuable properties. Being based on AOO, it is an intuitive measure; as it only requires occupancy data, it is comparatively easy to estimate; and because it is a quantitative and generic measure, it increases the testability and comparability of impact assessments of alien species.

scholarly journals Regional Assessment of the Conservation Status of Snubfin Dolphins (Orcaella heinsohni) in the Kimberley Region, Western Australia

2021 ◽  
Vol 7 ◽  
Author(s):  
Phil J. Bouchet ◽  
Deborah Thiele ◽  
Sarah A. Marley ◽  
Kelly Waples ◽  
Frank Weisenberger ◽  
...  
Keyword(s):  
Western Australia ◽  
Conservation Status ◽  
Regional Scale ◽  
Indigenous Communities ◽  
Multiple Sources ◽  
Marine Animals ◽  
Cetacean Species ◽  
Detection Rates ◽  
Area Of Occupancy ◽  
Kernel Approach

Implementing conservation measures for data-limited species is a fundamental challenge for wildlife managers and policy-makers, and proves difficult for cryptic marine animals occurring in naturally low numbers across remote seascapes. There is currently scant information on the abundance and habitat preferences of Australian snubfin dolphins (Orcaella heinsohni) throughout much of their geographical range, and especially within the Kimberley region of northern Western Australia. Such knowledge gaps curtail rigorous threat assessments on both local and regional scales. To address this and assist future conservation listings, we built the first comprehensive catalog of snubfin dolphin sightings for the Kimberley. We used these data to estimate the species’ extent of occurrence (EOO) and area of occupancy (AOO) along the region’s 7,000 km coastline, following a simple Bootstrap bivariate kernel approach to combine datasets of varying quality and quantify uncertainty. Our catalog consists of 1,597 visual detections of snubfin dolphins made over a period of 17 years (2004–2020) and collated from multiple sources, including online biodiversity repositories, peer-reviewed scientific articles, citizen science programs, as well as dedicated marine wildlife surveys with local Indigenous communities and Ranger groups. Snubfin dolphins were consistently encountered in shallow waters (<21 m depth) close to (<15 km) freshwater inputs, with high detection rates in known hotspots (e.g., Roebuck Bay, Cygnet Bay) as well as in coastal habitats suspected to be suitable (e.g., Prince Regent River and surrounds, King Sound, Doubtful Bay, Napier Broome Bay and the upper Cambridge Gulf). Bootstrap estimates of EOO and AOO were 38,300 (95% CI: 25,451–42,437) km2 and 700 (656–736) km2 respectively, suggesting that snubfin dolphins in the Kimberley are likely Vulnerable under IUCN criteria B2 at a regional scale, in keeping with their global classification. Our study offers insights into the distribution of a vulnerable coastal cetacean species and demonstrates the value of integrating multiple data sources for informing conservation assessments in the face of uncertainty.

scholarly journals Toward location privacy protection in Spatial crowdsourcing

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983056 ◽  
Author(s):  
Hang Ye ◽  
Kai Han ◽  
Chaoting Xu ◽  
Jingxin Xu ◽  
Fei Gui
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Data Sets ◽  
Relative Location ◽  
Mathematical Transformation ◽  
Real World Data ◽  
Spatial Crowdsourcing ◽  
Effectiveness And Efficiency ◽  
Spatio Temporal ◽  
Location Privacy Protection

Spatial crowdsourcing is an emerging outsourcing platform that allocates spatio-temporal tasks to a set of workers. Then, the worker moves to the specified locations to perform the tasks. However, it usually demands workers to upload their location information to the spatial crowdsourcing server, which unavoidably attracts attention to the privacy-preserving of the workers’ locations. In this article, we propose a novel framework that can protect the location privacy of the workers and the requesters when assigning tasks to workers. Our scheme is based on mathematical transformation to the location while providing privacy protection to workers and requesters. Moreover, to further preserve the relative location between workers, we generate a certain amount of noise to interfere the spatial crowdsourcing server. Experimental results on real-world data sets show the effectiveness and efficiency of our proposed framework.

Difference Operators in Simulation Data Warehouses

2017 ◽  
Vol 12 (2) ◽  
pp. 347-354 ◽  
Author(s):  
Jing Zhao ◽  
◽  
Yoshiharu Ishikawa ◽  
Yukiko Wakita ◽  
Kento Sugiura
Keyword(s):  
Difference Operator ◽  
Difference Operators ◽  
Observation Data ◽  
Temporal Data ◽  
Data Warehouses ◽  
Simulation Data ◽  
Tsunami Disaster ◽  
Spatio Temporal ◽  
The Difference ◽  
Mass Evacuation

In analyzing observation data and simulation results, there are frequent demands for comparing more than one data on the same subject to detect any differences between them. For example, comparison of observation data for an object in a certain spatial domain at different times or comparison of spatial simulation data with different parameters. Therefore, this paper proposes the difference operator in spatio-temporal data warehouses, which store temporal and spatial observation data and simulation data. The requirements for the difference operator are summarized, and the approaches to implement them are presented. In addition, the proposed approach is applied to the mass evacuation of simulation data in a tsunami disaster, and its effectiveness is verified. Extensions of the difference operator and their applications are also discussed.

scholarly journals Empty Container Management at Ports Considering Pollution, Repair Options, and Street-Turns

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Norberto Sáinz Bernat ◽  
Frederik Schulte ◽  
Stefan Voß ◽  
Jürgen Böse
Keyword(s):  
Inventory Management ◽  
Real World ◽  
Ecological Impact ◽  
Ecological Problem ◽  
Container Transportation ◽  
Periodic Review ◽  
Real World Data ◽  
Empty Container ◽  
Container Management ◽  
Empty Containers

International trade imbalances make the management of empty containers within shipping networks an important economic and ecological problem. While import-dominated ports accumulate large amounts of empty containers, export-dominated ports need them as transport resources, requiring a repositioning transportation of empty containers on the sea and land side. Acknowledging the importance of the problem, plenty of respective literature has appeared. Since periodic review inventory management systems allow to model the inherent stochasticity of empty container transportation, they have emerged as a major solution approach in the domain. Nevertheless, existing approaches often omit crucial economic and ecological real world conditions determining the success of empty container management. Pollution, repair options, and street-turns are important aspects in this context. In this work, we present new stochastic review policies incorporating a realistic allocation scheme for empty container emissions, realistic maintenance, and repair options as well as street-turns. We analyze the optimality of the proposed polices and evaluate them in a simulation model with metaheuristic parameter search based on extensive real-world data from a major global shipping company operating in Latin America. Results provide insights for academics and practitioners about the economic and ecological impact of the distinct empty container management polices within a shipping network.

Naturalization of the Manila clam (Tapes philippinarum), an alien species, and establishment of a clam fishery within Poole Harbour, Dorset

2004 ◽  
Vol 84 (5) ◽  
pp. 1069-1073 ◽  
Author(s):  
A.C. Jensen ◽  
J. Humphreys ◽  
R.W.G. Caldow ◽  
C. Grisley ◽  
P.E.J. Dyrynda
Keyword(s):  
Alien Species ◽  
Current Distribution ◽  
Ecological Impact ◽  
Manila Clam ◽  
Wading Birds ◽  
Tapes Philippinarum

The first known occurrence of a naturalized population of Manila clam (Tapes philippinarum) in UK waters is reported. Introduced into Poole Harbour for aquaculture in 1988; by 1994 local fishermen and wading birds began to exploit this northernmost naturalized population in Europe. The licensed fishery currently supports 31 local fishers, landing approximately 250 tn. of clams in 2002. The current distribution of the clam in Poole Harbour, the biology of this naturalized population and the ecological impact of its introduction and fishery, is described.

scholarly journals ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 497-503
Author(s):  
S. Busch ◽  
T. Schindler ◽  
T. Klinger ◽  
C. Brenner
Keyword(s):  
Public Transportation ◽  
Autonomous Driving ◽  
Video Data ◽  
Observation Data ◽  
Traffic Patterns ◽  
Traffic Light ◽  
The Road ◽  
Embedded Graph ◽  
Observed Behaviour ◽  
Spatio Temporal

For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.

scholarly journals AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

2018 ◽  
Vol XLII-1 ◽  
pp. 451-456 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
G. Bilgin
Keyword(s):  
Classification Accuracy ◽  
Alpha Angle ◽  
Test Site ◽  
Classification Performance ◽  
Support Vector ◽  
Observation Data ◽  
Data Set ◽  
Crop Classification ◽  
Sar Data ◽  
Spatio Temporal

<p><strong>Abstract.</strong> Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (<span style="text-decoration: overline">&amp;alpha;</span>) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H<span style="text-decoration: overline">&amp;alpha;</span>, (2) H<span style="text-decoration: overline">&amp;alpha;</span>Span, (3) H<span style="text-decoration: overline">&amp;alpha;</span>A, (4) H<span style="text-decoration: overline">&amp;alpha;</span>ASpan and (5) coherency [<i>T</i>] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&amp;alpha;ASpan (91.43<span class="thinspace"></span>% for SVM, 92.25<span class="thinspace"></span>% for RF and 90.55<span class="thinspace"></span>% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25<span class="thinspace"></span>% by RF and H&amp;alpha;ASpan while lowest classification accuracy was obtained as 66.99<span class="thinspace"></span>% by NB and H&amp;alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.</p>

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