A new method for correcting locational error from aerial surveys improves habitat model performance

Selectivity is a key parameter in stock assessments that describes how fisheries interact with different ages and sizes of fish. It is usually confounded with other processes (e.g., natural mortality and recruitment) in stock assessments and the assumption of selectivity can strongly affect stock assessment outcome. Here, we introduce a new semi-parametric selectivity method, which we implement and test in Stock Synthesis. This selectivity method includes a parametric component and an autocorrelated nonparametric component consisting of deviations from the parametric component. We explore the new selectivity method using two simulation experiments, which show that the two autocorrelation parameters for selectivity deviations of data-rich fisheries are estimable using either mixed-effect or simpler sample-based algorithms. When selectivity deviations of a data-rich fishery are highly autocorrelated, using the new method to estimate the two autocorrelation parameters leads to more precise estimations of spawning biomass and fully selected fishing mortality. However, this new method fails to improve model performance in low data quality cases where measurement error in the data overwhelms the pattern caused by the autocorrelated process. Finally, we use a case study involving North Sea herring (Clupea harengus) to show that our new method substantially reduces autocorrelations in the Pearson residuals in fit to age composition data.

Download Full-text

Regional Climate Modeling over the Maritime Continent. Part II: New Parameterization for Autoconversion of Convective Rainfall

Journal of Climate ◽

10.1175/jcli-d-13-00171.1 ◽

2014 ◽

Vol 27 (4) ◽

pp. 1504-1523 ◽

Cited By ~ 25

Author(s):

Rebecca L. Gianotti ◽

Elfatih A. B. Eltahir

Keyword(s):

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Model Performance ◽

Convective Cloud ◽

Cloud Water ◽

New Method ◽

Convection Scheme ◽

Convective Rainfall ◽

Maritime Continent

Abstract This paper describes a new method for parameterizing the conversion of convective cloud liquid water to rainfall (“autoconversion”) that can be used within large-scale climate models, and evaluates the new method using the Regional Climate Model, version 3 (RegCM3), coupled to the land surface scheme Integrated Biosphere Simulator (IBIS). The new method is derived from observed distributions of cloud water content and is constrained by observations of cloud droplet characteristics and climatological rainfall intensity. This new method explicitly accounts for subgrid variability with respect to cloud water density and is independent of model resolution, making it generally applicable for large-scale climate models. This work builds on the development of a new parameterization method for convective cloud fraction, which was described in Part I. Simulations over the Maritime Continent using the Emanuel convection scheme show significant improvement in model performance, not only with respect to convective rainfall but also in shortwave radiation, net radiation, and turbulent surface fluxes of latent and sensible heat, without any additional modifications made to the simulation of those variables. Model improvements are demonstrated over a 19-yr validation period as well as a shorter 4-yr evaluation. Model performance with the Grell convection scheme is not similarly improved and reasons for this outcome are discussed. This work illustrates the importance of representing observed subgrid-scale variability in diurnally varying convective processes for simulations of the Maritime Continent region.

Download Full-text

Noise ceiling on the crossvalidated performance of reweighted models of representational dissimilarity: Addendum to Khaligh-Razavi & Kriegeskorte (2014)

10.1101/2020.03.23.003046 ◽

2020 ◽

Cited By ~ 1

Author(s):

Katherine R. Storrs ◽

Seyed-Mahdi Khaligh-Razavi ◽

Nikolaus Kriegeskorte

Keyword(s):

Lower Bound ◽

Model Performance ◽

Upper Bounds ◽

New Method ◽

Human Data ◽

Human Participants ◽

Weighted Combination ◽

Made In

AbstractAn error was made in including noise ceilings for human data in Khaligh-Razavi and Kriegeskorte (2014). For comparability with the macaque data, human data were averaged across participants before analysis. Therefore the noise ceilings indicating variability across human participants do not accurately depict the upper bounds of possible model performance and should not have been shown. Creating noise ceilings appropriate for the fitted models is not trivial. Below we present a method for doing this, and the results obtained with this new method. The corrected results differ from the original results in that the best-performing model (weighted combination of AlexNet layers and category readouts) does not reach the lower bound of the noise ceiling. However, the best-performing model is not significantly below the lower bound of the noise ceiling. The claim that the model “fully explains” the human IT data appears overstated. All other claims of the paper are unaffected.

Download Full-text

GIS-based habitat model to predict potential areas for the upcoming occurrences of an alien invasive plant, Mimosa pigra L.

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/fsmu-2019-0003 ◽

2019 ◽

Vol 70 (1) ◽

pp. 31-43

Author(s):

Thai Son Le ◽

Pham Thi Kim Thoa ◽

Nguyen Van Tuan

Keyword(s):

Invasive Species ◽

High Risk ◽

Invasive Plant ◽

Model Performance ◽

Null Model ◽

Habitat Model ◽

Alien Invasive Species ◽

Multivariate Statistic ◽

Mimosa Pigra ◽

Risk Areas

Abstract Incursions of Mimosa pigra L., a super-invasive plant, were detected in Hoa Vang district, Da Nang city, Vietnam. This invasive species posed threats to the local agricultural and natural areas, especially to Ba Na - Nui Chua Nature Reserve located in the district. In this study, a habitat model was developed to predict potential areas for the upcoming occurrences of the plant. Detected locations of the species were analyzed in association with seven environmental layers (15 m spatial resolution), which characterized the habitat conditions facilitating the plant incursion, to calculate a multivariate statistic, Mahalanobis distance (D2). Mimosa occurrences were divided into subsets of modelling (for model construction) and validating data (for selecting the best model from replicate runs). The model performance was tested using a null model of 1,000 random points and indicated a significant relationship between D2 values and mimosa occurrence. The D2 model performed markedly better than the random model. The null model in combination with the entire dataset of mimosa locations was also used to identify the threshold D2 value. Using that threshold value, 99.5% of existing mimosa locations were detected and 20.3% of the study area was determined as high-risk areas for mimosa occurrence. These identified high risk areas would make an important contribution to the local alien invasive species management. Given the potential threats to these species from illegal harvesting, that information may serve as an important benchmark for future habitat and population assessments. The spatial modelling techniques in this study can easily be applied to other species and areas.

Download Full-text

Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models

Movement Ecology ◽

10.1186/s40462-021-00240-2 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Elliott L. Hazen ◽

Briana Abrahms ◽

Stephanie Brodie ◽

Gemma Carroll ◽

Heather Welch ◽

...

Keyword(s):

Random Walks ◽

Mixed Models ◽

Explanatory Power ◽

Model Performance ◽

Habitat Model ◽

Boosted Regression Trees ◽

Habitat Models ◽

Predictive Skill ◽

Correlated Random Walks ◽

Blue Whales

Abstract Background Habitat suitability models give insight into the ecological drivers of species distributions and are increasingly common in management and conservation planning. Telemetry data can be used in habitat models to describe where animals were present, however this requires the use of presence-only modeling approaches or the generation of ‘pseudo-absences’ to simulate locations where animals did not go. To highlight considerations for generating pseudo-absences for telemetry-based habitat models, we explored how different methods of pseudo-absence generation affect model performance across species’ movement strategies, model types, and environments. Methods We built habitat models for marine and terrestrial case studies, Northeast Pacific blue whales (Balaenoptera musculus) and African elephants (Loxodonta africana). We tested four pseudo-absence generation methods commonly used in telemetry-based habitat models: (1) background sampling; (2) sampling within a buffer zone around presence locations; (3) correlated random walks beginning at the tag release location; (4) reverse correlated random walks beginning at the last tag location. Habitat models were built using generalised linear mixed models, generalised additive mixed models, and boosted regression trees. Results We found that the separation in environmental niche space between presences and pseudo-absences was the single most important driver of model explanatory power and predictive skill. This result was consistent across marine and terrestrial habitats, two species with vastly different movement syndromes, and three different model types. The best-performing pseudo-absence method depended on which created the greatest environmental separation: background sampling for blue whales and reverse correlated random walks for elephants. However, despite the fact that models with greater environmental separation performed better according to traditional predictive skill metrics, they did not always produce biologically realistic spatial predictions relative to known distributions. Conclusions Habitat model performance may be positively biased in cases where pseudo-absences are sampled from environments that are dissimilar to presences. This emphasizes the need to carefully consider spatial extent of the sampling domain and environmental heterogeneity of pseudo-absence samples when developing habitat models, and highlights the importance of scrutinizing spatial predictions to ensure that habitat models are biologically realistic and fit for modeling objectives.

Download Full-text

Selective Sampling and Orientation of Non-Homogeneous Tissues

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100100238 ◽

1968 ◽

Vol 26 ◽

pp. 98-99

Author(s):

C. C. Clawson ◽

L. W. Anderson ◽

R. A. Good

Keyword(s):

Electron Microscopy ◽

Electron Microscope ◽

Light Microscopy ◽

Random Sampling ◽

New Method ◽

Microscope Examination ◽

Small Areas ◽

Selective Sampling ◽

Large Numbers ◽

Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

Download Full-text

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

American Journal of Audiology ◽

10.1044/2019_aja-heal18-18-0163 ◽

2019 ◽

Vol 28 (3S) ◽

pp. 802-805 ◽

Cited By ~ 1

Author(s):

Marieke Pronk ◽

Janine F. J. Meijerink ◽

Sophia E. Kramer ◽

Martijn W. Heymans ◽

Jana Besser

Keyword(s):

Help Seeking ◽

Hearing Aid ◽

Model Performance ◽

Area Under The Curve ◽

Secondary Data ◽

Support Program ◽

Evaluation Period ◽

A Prospective Study ◽

Study Participants ◽

Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Download Full-text