Skip header and navigation

4 records – page 1 of 1.

Quantifying Migration Behaviour Using Net Squared Displacement Approach: Clarifications and Caveats.

https://arctichealth.org/en/permalink/ahliterature275164
Source
PLoS One. 2016;11(3):e0149594
Publication Type
Article
Date
2016
Author
Navinder J Singh
Andrew M Allen
Göran Ericsson
Source
PLoS One. 2016;11(3):e0149594
Date
2016
Language
English
Publication Type
Article
Keywords
Animal Migration
Animals
Deer - physiology
Ecology - statistics & numerical data
Ecosystem
Seasons
Sweden
Abstract
Estimating migration parameters of individuals and populations is vital for their conservation and management. Studies on animal movements and migration often depend upon location data from tracked animals and it is important that such data are appropriately analyzed for reliable estimates of migration and effective management of moving animals. The Net Squared Displacement (NSD) approach for modelling animal movement is being increasingly used as it can objectively quantify migration characteristics and separate different types of movements from migration. However, the ability of NSD to properly classify the movement patterns of individuals has been criticized and issues related to study design arise with respect to starting locations of the data/animals, data sampling regime and extent of movement of species. We address the issues raised over NSD using tracking data from 319 moose (Alces alces) in Sweden. Moose is an ideal species to test this approach, as it can be sedentary, nomadic, dispersing or migratory and individuals vary in their extent, timing and duration of migration. We propose a two-step process of using the NSD approach by first classifying movement modes using mean squared displacement (MSD) instead of NSD and then estimating the extent, duration and timing of migration using NSD. We show that the NSD approach is robust to the choice of starting dates except when the start date occurs during the migratory phase. We also show that the starting location of the animal has a marginal influence on the correct quantification of migration characteristics. The number of locations per day (1-48) did not significantly affect the performance of non-linear mixed effects models, which correctly distinguished migration from other movement types, however, high-resolution data had a significant negative influence on estimates for the timing of migrations. The extent of movement, however, had an effect on the classification of movements, and individuals undertaking short- distance migrations can be misclassified as other movements such as sedentary or nomadic. Our study raises important considerations for designing, analysing and interpreting movement ecology studies, and how these should be determined by the biology of the species and the ecological and conservation questions in focus.
Notes
Cites: Ecol Lett. 2009 May;12(5):395-40819379134
Cites: Ecol Lett. 2008 Jan;11(1):63-7717897327
Cites: Ecol Appl. 2009 Dec;19(8):2016-2520014575
Cites: Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2157-6220566493
Cites: Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2303-1220566506
Cites: PLoS One. 2011;6(1):e1637021283536
Cites: J Anim Ecol. 2011 Mar;80(2):466-7621105872
Cites: PLoS One. 2012;7(5):e3652722570722
Cites: Ecol Appl. 2012 Oct;22(7):2007-2023210316
Cites: PLoS One. 2013;8(5):e6454823691246
Cites: Ecology. 2013 Jun;94(6):1245-5623923485
Cites: PLoS One. 2013;8(10):e7567324130732
Cites: Nat Commun. 2013;4:268824162104
Cites: Ecology. 2014 Jan;95(1):225-3724649661
Cites: PLoS One. 2014;9(4):e9475024722396
Cites: Am Nat. 2014 May;183(5):E154-6724739204
Cites: Biol Lett. 2014 Jun;10(6). pii: 20140379. doi: 10.1098/rsbl.2014.037924942710
Cites: PLoS One. 2015;10(4):e012475425905640
Cites: Science. 2015 Jun 12;348(6240):aaa247826068858
Cites: Science. 2015 Jun 12;348(6240):125564226068859
Cites: Ecol Appl. 2007 Mar;17(2):628-3817489266
Cites: Trends Ecol Evol. 2008 Feb;23(2):87-9418191283
Cites: Proc Natl Acad Sci U S A. 2008 Dec 9;105(49):19114-919060190
Cites: J Anim Ecol. 2006 Sep;75(5):1046-5716922840
Cites: Oecologia. 2005 Mar;143(2):179-8815657759
Cites: Ecology. 2009 Oct;90(10):2956-6219886504
PubMed ID
26938257 View in PubMed
Less detail

Vegetation productivity summarized by the Dynamic Habitat Indices explains broad-scale patterns of moose abundance across Russia.

https://arctichealth.org/en/permalink/ahliterature307223
Source
Sci Rep. 2020 01 21; 10(1):836
Publication Type
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Date
01-21-2020
Author
Elena Razenkova
Volker C Radeloff
Maxim Dubinin
Eugenia V Bragina
Andrew M Allen
Murray K Clayton
Anna M Pidgeon
Leonid M Baskin
Nicholas C Coops
Martina L Hobi
Author Affiliation
SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA. razenkova@wisc.edu.
Source
Sci Rep. 2020 01 21; 10(1):836
Date
01-21-2020
Language
English
Publication Type
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Keywords
Animals
Deer
Ecosystem
Population Density
Russia
Time Factors
Abstract
Identifying the factors that determine habitat suitability and hence patterns of wildlife abundances over broad spatial scales is important for conservation. Ecosystem productivity is a key aspect of habitat suitability, especially for large mammals. Our goals were to a) explain patterns of moose (Alces alces) abundance across Russia based on remotely sensed measures of vegetation productivity using Dynamic Habitat Indices (DHIs), and b) examine if patterns of moose abundance and productivity differed before and after the collapse of the Soviet Union. We evaluated the utility of the DHIs using multiple regression models predicting moose abundance by administrative regions. Univariate models of the individual DHIs had lower predictive power than all three combined. The three DHIs together with environmental variables, explained 79% of variation in moose abundance. Interestingly, the predictive power of the models was highest for the 1980s, and decreased for the two subsequent decades. We speculate that the lower predictive power of our environmental variables in the later decades may be due to increasing human influence on moose densities. Overall, we were able to explain patterns in moose abundance in Russia well, which can inform wildlife managers on the long-term patterns of habitat use of the species.
PubMed ID
31964926 View in PubMed
Less detail

Vulnerability of Subarctic and Arctic breeding birds.

https://arctichealth.org/en/permalink/ahliterature278879
Source
Ecol Appl. 2017 Jan;27(1):219-234
Publication Type
Article
Date
Jan-2017
Author
Anouschka R Hof
Genoveva Rodríguez-Castañeda
Andrew M Allen
Roland Jansson
Christer Nilsson
Source
Ecol Appl. 2017 Jan;27(1):219-234
Date
Jan-2017
Language
English
Publication Type
Article
Abstract
Recent research predicts that future climate change will result in substantial biodiversity loss associated with loss of habitat for species. However, the magnitude of the anticipated biodiversity impacts are less well known. Studies of species vulnerability to climate change through species distribution models are often limited to assessing the extent of species' exposure to the consequences of climate change to their local environment, neglecting species sensitivity to global change. The likelihood that species or populations will decline or go extinct due to climate change also depends on the general sensitivity and adaptive capacity of species. Hence, analyses should also obtain more accurate assessments of their vulnerability. We addressed this by constructing a vulnerability matrix for 180 bird species currently breeding in Subarctic and Arctic Europe that integrates a climatic exposure-based vulnerability index and a natural-history trait-based vulnerability index. Species that may need extra conservation attention based on our matrix include the Great Snipe (Gallinago media), the Rough-legged Buzzard (Buteo lagopus), the Red-throated Pipit (Anthus cervinus), the Common Swift (Apus apus), the Horned Lark (Eremophila alpestris), and the Bar-tailed Godwit (Limosa lapponica). Our vulnerability matrix stresses the importance of looking beyond exposure to climate change when species conservation is the aim. For the species that scored high in our matrix the future in the region looks grim and targeted conservation actions, incorporating macroecological and global perspectives, may be needed to alleviate severe population declines. We further demonstrate that climate change is predicted to significantly reduce the current breeding range of species adapted to cold climates in Subarctic and Arctic Europe. The number of incubation days and whether the species was a habitat specialist or not were also among the variables most strongly related to predicted contraction or expansion of species' breeding ranges. This approach may aid the identification of vulnerable bird species worldwide.
PubMed ID
28052503 View in PubMed
Less detail

Vulnerability of Subarctic and Arctic breeding birds.

https://arctichealth.org/en/permalink/ahliterature301984
Source
Ecol Appl. 2017 01; 27(1):219-234
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
01-2017
Author
Anouschka R Hof
Genoveva Rodríguez-Castañeda
Andrew M Allen
Roland Jansson
Christer Nilsson
Author Affiliation
Landscape Ecology Group, Department of Ecology and Environmental Science, Umeå University, Umeå, SE-901 87, Sweden.
Source
Ecol Appl. 2017 01; 27(1):219-234
Date
01-2017
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Animal Distribution
Animals
Arctic Regions
Biodiversity
Birds - physiology
Climate change
Conservation of Natural Resources
Finland
Norway
Population Dynamics
Sweden
Abstract
Recent research predicts that future climate change will result in substantial biodiversity loss associated with loss of habitat for species. However, the magnitude of the anticipated biodiversity impacts are less well known. Studies of species vulnerability to climate change through species distribution models are often limited to assessing the extent of species' exposure to the consequences of climate change to their local environment, neglecting species sensitivity to global change. The likelihood that species or populations will decline or go extinct due to climate change also depends on the general sensitivity and adaptive capacity of species. Hence, analyses should also obtain more accurate assessments of their vulnerability. We addressed this by constructing a vulnerability matrix for 180 bird species currently breeding in Subarctic and Arctic Europe that integrates a climatic exposure-based vulnerability index and a natural-history trait-based vulnerability index. Species that may need extra conservation attention based on our matrix include the Great Snipe (Gallinago media), the Rough-legged Buzzard (Buteo lagopus), the Red-throated Pipit (Anthus cervinus), the Common Swift (Apus apus), the Horned Lark (Eremophila alpestris), and the Bar-tailed Godwit (Limosa lapponica). Our vulnerability matrix stresses the importance of looking beyond exposure to climate change when species conservation is the aim. For the species that scored high in our matrix the future in the region looks grim and targeted conservation actions, incorporating macroecological and global perspectives, may be needed to alleviate severe population declines. We further demonstrate that climate change is predicted to significantly reduce the current breeding range of species adapted to cold climates in Subarctic and Arctic Europe. The number of incubation days and whether the species was a habitat specialist or not were also among the variables most strongly related to predicted contraction or expansion of species' breeding ranges. This approach may aid the identification of vulnerable bird species worldwide.
PubMed ID
28052503 View in PubMed
Less detail