295 research outputs found
Code and data for "Within reach? Habitat availability as a function of individual mobility and spatial structuring"
No full textAn R project and associated code and data files covering the functions, illustrations, simulations and data analyses of the publication "Within reach? Habitat availability as a function of individual mobility and spatial structuring" by Matthiopoulos et al.</p
Code and data for "Within reach? Habitat availability as a function of individual mobility and spatial structuring"
No full text<p>An R project and associated code and data files covering the functions, illustrations, simulations and data analyses of the publication "Within reach? Habitat availability as a function of individual mobility and spatial structuring" by Matthiopoulos et al.</p>
Environmental constraints on the foraging behaviour, spatial usage and population sizes of albatrosses
No full textSatellite-tracking of wide-ranging, apex marine predators, combined with remote-sensing, can be used to test ecological hypotheses and to estimate spatial abundance. I
used this approach to quantify the habitat usage of central place foraging black-browed
albatrosses (BBA) from nine colonies, modelling population-level distribution as a
function of habitat accessibility, habitat preference and conspecific competition.
Throughout breeding, BBA preferred neritic waters, steeper bathymetry, and, during
incubation, warmer sea surface temperatures. BBA from South Georgia also preferred
highly dynamic oceanic waters. Foraging areas were partially spatially segregated with
respect to colony and region, presumably to reduce intraspecific competition. Although
such competition is often invoked to explain observed colony sizes, by accounting for
travel costs, I demonstrate a strong relationship between the sizes of regional
populations and the availability, accessibility and productivity of neritic waters,
supporting the hypothesis that seabird populations are constrained by breeding season
food availability. In response to this constraint, albatrosses have evolved to exploit
energetically efficient gliding flight, allowing them to access prey 100-1000s of km
from their colonies. Hence, I used satellite tracking and activity data to quantify the
effects of relative wind speed on the flight speed of four albatross species.
Groundspeed was linearly related to the wind speed in the direction of flight, its effect
being greatest on wandering albatrosses, followed by BBA, light-mantled and grey-
headed albatrosses, and airspeeds were higher in males than females. Commuting birds
tended to encounter headwinds during outward trips and tailwinds on their return, such
that return trips were faster. This supports the hypothesis that foraging upwind of the
colony is more efficient but could also result from wind climate and the relative
location of prey. The ability to use tracking data to estimate spatial usage is timely
given the acute threat currently posed to albatrosses by incidental fisheries mortality
Julia codes for "Communal and efficient movement routines can develop spontaneously through public information use", Behav Ecol.
No full textHere are provided all the Julia codes (v. 0.5.1) used to run the model presented
in: Riotte-Lambert, L. and J. Matthiopoulos. 2018. Communal and efficient movement routines can develop spontaneously through public information use. Behavioral Ecology.Use "exampleRun.jl" to run a simulation with default parameters.All details on the inputs and outputs of the functions are given as comments in the Julia scripts.</i
How to be a quantitative ecologist: the a to r of green mathematics and statistics/ Matthiopoulos
No full textxix, 467 hal.: ill.; tab.: 25 cm
Multi-species state-space modelling of the hen harrier (Circus cyaneus) and red grouse (Lagopus lagopus scoticus) in Scotland
No full textState-space modelling is a powerful tool to study ecological systems. The direct inclusion of uncertainty, unification of models and data, and ability to model unobserved, hidden states increases our knowledge about the environment and provides
new ecological insights. I extend the state-space framework to create multi-species
models, showing that the ability to model ecosystem interactions is limited only by data availability. State-space models are fit using both Bayesian and Frequentist methods, making them independent of a statistical school of thought. Bayesian approaches can have the advantage in their ability to account for missing data and fit hierarchical structures
and models with many parameters to limited data; often the case in ecological studies.
I have taken a Bayesian model fitting approach in this thesis.
The predator-prey interactions between the hen harrier (Circus cyaneus) and red grouse (Lagopus lagopus scoticus) are used to demonstrate state-space modelling’s
capabilities. The harrier data are believed to be known without error, while missing
data make the cyclic dynamics of the grouse harder to model. The grouse-harrier interactions are modelled in a multi-species state-space model, rather than including
one species as a covariate in the other’s model. Finally, models are included for the
harriers’ alternate prey.
The single- and multi-species state-space models for the predator-prey interactions
provide insight into the species’ management. The models investigate aspects of the species’ behaviour, from the mechanisms behind grouse cycles to what motivates harrier immigration. The inferences drawn from these models are applicable to management, suggesting actions to halt grouse cycles or mitigate the grouse-harrier conflict. Overall, the multi-species models suggest that two popular ideas for grouse-harrier management, diversionary feeding and habitat manipulation to reduce alternate prey densities, will not have the desired effect, and in the case of reducing prey densities, may even increase the harriers’ impact on grouse chicks
R Code and Output Supporting: Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models
No full textEach example.html file (“example_”) uses various uhcplot functions (html files with description “code for function”). The example with the MN moose data (example_4_moose.html) also uses data for one Minnesota moose from 2013 and 2014 (in moose12687.csv). The tables (tables.html) are created with regression output files created and saved with the codes provided in the example.html files. The zipped folder (UHCPlotsPaper_R.zip) contains all of the Program R files (.R extension) for each of the html files. MNmoose_Arrowhead.pdf details the location of the moose from dataset. See the readme.txt for more information.Species distribution models (SDMs) are one of a variety of statistical methods that link individuals, populations, and species to the habitats they occupy. In Fieberg et al. "Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models", we introduce a new method for model calibration, which we call Used-Habitat Calibration plots (UHC plots) that can be applied across the entire spectrum of SDMs. Here, we share the Program R code and data necessary to replicate all three of the examples from the manuscript that together demonstrate how UHC plots can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity.This work was funded in part by the University of Minnesota-Twin Cities and the Minnesota Environment and Natural Resources Trust Fund. The Minnesota Department of Natural Resources assisted with collaring and monitoring of the moose.Fieberg, John R; Forester, James D; Street, Garrett M; Johnson, Douglas H; ArchMiller, Althea A; Matthiopoulos, Jason. (2016). R Code and Output Supporting: Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models. Retrieved from the University Digital Conservancy, http://doi.org/10.13020/D6T590
Distribution and abundance estimation of sperm whales (Physeter macrocephalus) along the Hellenic Trench in eastern Mediterranean
No full textSperm whales (Physeter macrocephalus) of the Hellenic Trench, Mediterranean Sea, illustrate a constant summer distribution and abundance. The sperm whale population of the Mediterranean Sea has been characterized as “Endangered” by the IUCN (2012) although areas of high occurrence should be under a wider conservation planning. Here, I modelled sperm whale distribution in the Hellenic Trench in order to quantify the distribution of the sperm whale along the Hellenic Trench. To do this a combined method of GAMs-GEEs were used to account for the autocorrelation existed in the data. Social groups and solitary or loosely aggregated males varied significantly in the habitat use within the study area, with males using habitat closer to the shore and social groups to present an affinity for higher Sea Surface Temperature (SST) and Sea Level Anomaly (SLA) values. The covariates remained in the model for the combined dataset (social groups-males) are depth, seabed steepness and distance from the shore, distance from 1km depth contour, SST and SLA. Point transects sampling was used for the abundance estimation of the summer sperm whale population from a combined acoustic and visual survey and an estimate of 27 [19.7, 32.08] individuals was derived with 95% CI. An acoustic detection function was modelled with a Generalized Linear Model (GLMs) with data derived from an experimental dataset. The detectability of sperm whales was influenced by group size, so stratification sampling was applied to take into account the bias introduced by the number of individuals in each group. An acoustic effective range of 13 – 21 km was derived, with bigger sized groups being detected at greater distances than the smaller ones. The Hellenic Trench presents apparently an important area for the sperm whale sub-population of the Mediterranean Sea. The Hellenic Trench has been recommended to be an MPA for the protection of the sperm whale by ACCOBAMs (Agreement on the Conservation of Cetaceans of the Black Sea, Mediterranean Sea and contiguous Atlantic Area)
Modelling space-use and habitat preference from wildlife telemetry data
No full textManagement and conservation of populations of animals requires information on where they are, why they are there, and where else they
could be. These objectives are typically approached by collecting data on the
animals’ use of space, relating these to prevailing environmental conditions
and employing these relations to predict usage at other geographical regions.
Technical advances in wildlife telemetry have accomplished manifold
increases in the amount and quality of available data, creating the need for a
statistical framework that can use them to make population-level inferences
for habitat preference and space-use. This has been slow-in-coming because
wildlife telemetry data are, by definition, spatio-temporally autocorrelated,
unbalanced, presence-only observations of behaviorally complex animals,
responding to a multitude of cross-correlated environmental variables.
I review the evolution of techniques for the analysis of space-use and
habitat preference, from simple hypothesis tests to modern modeling
techniques and outline the essential features of a framework that emerges
naturally from these foundations. Within this framework, I discuss eight
challenges, inherent in the spatial analysis of telemetry data and, for each, I
propose solutions that can work in tandem. Specifically, I propose a logistic,
mixed-effects approach that uses generalized additive transformations of the
environmental covariates and is fitted to a response data-set comprising the
telemetry and simulated observations, under a case-control design.
I apply this framework to non-trivial case-studies using data from
satellite-tagged grey seals (Halichoerus grypus) foraging off the east and
west coast of Scotland, and northern gannets (Morus Bassanus) from Bass
Rock. I find that sea bottom depth and sediment type explain little of the
variation in gannet usage, but grey seals from different regions strongly
prefer coarse sediment types, the ideal burrowing habitat of sandeels, their
preferred prey. The results also suggest that prey aggregation within the
water column might be as important as horizontal heterogeneity. More
importantly, I conclude that, despite the complex behavior of the study
species, flexible empirical models can capture the environmental
relationships that shape population distributions
Migration quantified: Constructing models and linking them with data
No full textThis chapter discusses how models, combined with modern data sources and statistical methods, can be used to test different hypotheses about the causes of migration. It presents mathematical formulations for migration and discusses the ecological mechanisms that could spontaneously have given rise to migration-like patterns of space use from the interaction within and between groups of animals and their environment. This highlights that migration is best seen as lying on a continuum from sedentary to nomadic movement patterns and not as a clearly distinct movement behaviour. Given the multitude of potential processes leading to migration, and the constraints imposed by data collection methods, it may be difficult to observe and identify the original cause. With this caveat in mind, the use of inferential methods to detect, quantify, and identify the underlying mechanisms of migration is discussed, and the links between models, data, and inference are illustrated using three case studies.Fil: Borger, Luca. Swansea University; Reino UnidoFil: Matthiopoulos, Jason. University of Glasgow; Reino UnidoFil: Holdo, Ricardo. University of Georgia; Estados UnidosFil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Couzin, Iain. Max Planck Institute of Animal Behavior; AlemaniaFil: McCaukey, Edward. University of Oxford; Reino Unid
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