Journal of Statistical Software

Leeuw, Jan de

doi:10.1002/wics.10

Cited by 85 publications

(56 citation statements)

References 35 publications

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“…Each detected animal was recorded, and its distance and azimuth from the sampling point was measured using a laser range finder and magnetic compass. Perpendicular distances were calculated, and distance break classes were set to 50 m. The maximum observation distance was 366 m. We used the package unmarked (Fiske & Chandler, 2011) in R (R Core Team, 2018). We selected best-approximating models of abundance and detection probability, using a model with no covariates for a null estimate considering three detection functions (half-normal, hazard rate, and exponential) and two abundance distributions (Poisson and negative binomial) for each group.…”

Section: Model Specificationsmentioning

confidence: 99%

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Jímenez

Chandler

Tobajas

et al. 2019

Ecology and Evolution

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The estimation of abundance of wildlife populations is an essential part of ecological research and monitoring. Spatially explicit capture–recapture (SCR) models are widely used for abundance and density estimation, frequently through individual identification of target species using camera‐trap sampling. Generalized spatial mark–resight (Gen‐SMR) is a recently developed SCR extension that allows for abundance estimation when only a subset of the population is recognizable by artificial or natural marks. However, in many cases, it is not possible to read the marks in camera‐trap pictures, even though individuals can be recognized as marked. We present a new extension of Gen‐SMR that allows for this type of incomplete identification. We used simulation to assess how the number of marked individuals and the individual identification rate influenced bias and precision. We demonstrate the model's performance in estimating red fox ( Vulpes vulpes ) density with two empirical datasets characterized by contrasting densities and rates of identification of marked individuals. According to the simulations, accuracy increases with the number of marked individuals ( m ), but is less sensitive to changes in individual identification rate (δ). In our case studies of red fox density estimation, we obtained a posterior mean of 1.60 (standard deviation SD: 0.32) and 0.28 ( SD : 0.06) individuals/km 2 , in high and low density, with an identification rate of 0.21 and 0.91, respectively. This extension of Gen‐SMR is broadly applicable as it addresses the common problem of incomplete identification of marked individuals during resighting surveys.

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Section: Model Specificationsmentioning

confidence: 99%

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Jímenez

Chandler

Tobajas

et al. 2019

Ecology and Evolution

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“…Following the protocol described in Guidi et al (2016), we used partial least square regression, which is a dimensionality-reduction method that aims to determine predictor combinations with maximum covariance with the response variable. The predictors were ranked according to their value importance in projection (VIP) using the R package pls (Mevik & Wehrens, 2007). For each eukaryotic IAA, their relative contribution to each sample was estimated by computing the first eigen value.…”

Section: Network Analysis and Correlations With Ironmentioning

confidence: 99%

Community‐Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Caputi

Carradec

Eveillard

et al. 2019

Global Biogeochemical Cycles

100

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Predicting responses of plankton to variations in essential nutrients is hampered by limited in situ measurements, a poor understanding of community composition, and the lack of reference gene catalogs for key taxa. Iron is a key driver of plankton dynamics and, therefore, of global biogeochemical cycles and climate. To assess the impact of iron availability on plankton communities, we explored the comprehensive bio‐oceanographic and bio‐omics data sets from Tara Oceans in the context of the iron products from two state‐of‐the‐art global scale biogeochemical models. We obtained novel information about adaptation and acclimation toward iron in a range of phytoplankton, including picocyanobacteria and diatoms, and identified whole subcommunities covarying with iron. Many of the observed global patterns were recapitulated in the Marquesas archipelago, where frequent plankton blooms are believed to be caused by natural iron fertilization, although they are not captured in large‐scale biogeochemical models. This work provides a proof of concept that integrative analyses, spanning from genes to ecosystems and viruses to zooplankton, can disentangle the complexity of plankton communities and can lead to more accurate formulations of resource bioavailability in biogeochemical models, thus improving our understanding of plankton resilience in a changing environment.

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“…We compared models using AIC, and we estimated occupancy profiles across the range of elevations sampled by model-averaging all models with ΔAIC <2.0. All models were fitted using the package Unmarked in R (Fiske & Chandler, 2011).…”

Section: Sampling Level-detectability (P)mentioning

confidence: 99%

Have bird distributions shifted along an elevational gradient on a tropical mountain?

Campos‐Cerqueira

Arendt

Wunderle

et al. 2017

Ecology and Evolution

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An upward shift in elevation is one of the most conspicuous species responses to climate change. Nevertheless, downward shifts and, apparently, the absences of response have also been recently reported. Given the growing evidence of multiple responses of species distributions due to climate change and the paucity of studies in the tropics, we evaluated the response of a montane bird community to climate change, without the confounding effects of land‐use change. To test for elevational shifts, we compared the distribution of 21 avian species in 1998 and 2015 using occupancy models. The historical data set was based on point counts, whereas the contemporary data set was based on acoustic monitoring. We detected a similar number of species in historical (36) and contemporary data sets (33). We show an overall pattern of no significant change in range limits for most species, although there was a significant shift in the range limit of eight species (38%). Elevation limits shifted mostly upward, and this pattern was more common for upper than lower limits. Our results highlight the variability of species responses to climate change and illustrate how acoustic monitoring provides an easy and powerful way to monitor animal populations along elevational gradients.

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Journal of Statistical Software

Cited by 85 publications

References 35 publications

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

Community‐Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Have bird distributions shifted along an elevational gradient on a tropical mountain?

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