The Effect of Expertise on the Quality of Appraisal Services

This article examines the quality of appraisals as a function of expertise. In paraticular, we compare novices (beginning real estate students) to experts (practicing certified and/or designated appraisers) on three performance criteria. First, we examine differences in the values that these two groups attach to various property features. Second, we investigate the variation between their final market value estimates. The last task studied is whether appraisers can reliably provide a range about their market value that includes the actual sale price of the property. The results are based on a controlled experiment involving seventy-two novices and sixty-nine experts, where each participant was asked to determine a fair market value of a single-family home. Findings indicate that experienced appraisers do in fact exhibit less variation in their valuation of property characteristics, hence there is greater agreement in their market value estimates than is the case with novices. However, more experienced decision-makers tend to be overconfident of their ability: they are less likely to specify a range that includes the sale price than are novices.

Simultaneous Range-Velocity Processing and SNR Analysis of AFIT\u27s Random Noise Radar

This paper presents two research objectives aimed at advancing the AFIT RNR signal processing algorithm and modeling capability toward the overarching goal of performing collision avoidance on an autonomous vehicle. In both research efforts, analytical, simulated, and measured results are provided and used to draw research conclusions. The first research effort is aimed at reducing the memory required for 2D processing in the time domain in order to distribute the processing algorithm across hundreds of processors on a GPU. Distributed processing reduces the overall 2D processing time and the feasibility of a near real-time implementation is studied. The second effort consists of improving a Simulink® model of the AFIT RNR. Each component of the AFIT RNR, as well as the target environment, is modeled and compared to measured results. A robust model will provide a useful tool to study the signal-to-noise ratio (SNR) of the RNR at all points within the radar system

Identifying species complexes based on spatial and temporal clustering from joint dynamic species distribution models

Data-limited species are often grouped into a species complex to simplify management. Commonalities between species that may indicate if species can be adequately managed as a complex include the following: shared habitat utilization (e.g., overlapping fine-scale spatial distribution), synchrony in abundance trends, consistent fishing pressure or gear susceptibility, or life history parameters resulting in similar productivity. Using non-target rockfish species in the Gulf of Alaska as a case study, we estimate spatial and temporal similarities among species to develop species complexes using the vector autoregressive spatio-temporal (VAST) model, which is a joint dynamic species distribution model. Species groupings are identified using Ward\u27s hierarchical cluster analysis based on spatial and temporal species correlations. We then compare the spatial and temporal groupings with cluster analysis groupings that use exploitation and life history characteristics data. Based on the results, we conclude that there are some rockfish species that consistently group together, but the arrangement and number of clusters differ slightly depending on the data used. Developing species complexes for fisheries management requires a variety of analytical approaches including species distribution models and cluster analyses, and these can be applied across the full extent of available data sources

tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models

Multivariate spatio-temporal models are widely applicable, but specifying their structure is complicated and may inhibit wider use. We introduce the R package tinyVAST from two viewpoints: the software user and the statistician. From the user viewpoint, tinyVAST adapts a widely used formula interface to specify generalized additive models, and combines this with arguments to specify spatial and spatio-temporal interactions among variables. These interactions are specified using arrow notation (from structural equation models), or an extended arrow-and-lag notation that allows simultaneous, lagged, and recursive dependencies among variables over time. The user also specifies a spatial domain for areal (gridded), continuous (point-count), or stream-network data. From the statistician viewpoint, tinyVAST constructs sparse precision matrices representing multivariate spatio-temporal variation, and parameters are estimated by specifying a generalized linear mixed model (GLMM). This expressive interface encompasses vector autoregressive, empirical orthogonal functions, spatial factor analysis, and ARIMA models. To demonstrate, we fit to data from two survey platforms sampling corals, sponges, rockfishes, and flatfishes in the Gulf of Alaska and Aleutian Islands. We then compare eight alternative model structures using different assumptions about habitat drivers and survey detectability. Model selection suggests that towed-camera and bottom trawl gears have spatial variation in detectability but sample the same underlying density of flatfishes and rockfishes, and that rockfishes are positively associated with sponges while flatfishes are negatively associated with corals. We conclude that tinyVAST can be used to test complicated dependencies representing alternative structural assumptions for research and real-world policy evaluation

Density-dependent changes in effective area occupied for sea-bottom-associated marine fishes

The spatial distribution of marine fishes can change for many reasons including density-dependent distributional shifts. Previous studies show mixed support for either the proportional-density model, PDM (no relationship between abundance and area occupied, supported by ideal-free distribution theory) or the basin model, BM (positive abundance–area relationship, supported by density-dependent habitat selection theory). The BM implies that fishes move towards preferred habitat as the population declines. We estimate the average relationship using bottom trawl data for 92 fish species from six marine regions, to determine whether the BM or PDM provides a better description for sea-bottom-associated fishes. We fit a spatio-temporal model and estimate changes in effective area occupied and abundance, and combine results to estimate the average abundance–area relationship as well as variability among taxa and regions. The average relationship is weak but significant (0.6% increase in area for a 10% increase in abundance), whereas only a small proportion of species–region combinations show a negative relationship (i.e. shrinking area when abundance increases). Approximately one-third of combinations (34.6%) are predicted to increase in area more than 1% for every 10% increase in abundance. We therefore infer that population density generally changes faster than effective area occupied during abundance changes. Gadiforms have the strongest estimated relationship (average 1.0% area increase for every 10% abundance increase) followed by Pleuronectiformes and Scorpaeniformes, and the Eastern Bering Sea shows a strong relationship between abundance and area occupied relative to other regions. We conclude that the BM explains a small but important portion of spatial dynamics for sea-bottom-associated fishes, and that many individual populations merit cautious management during population declines, because a compressed range may increase the efficiency of harvest

Use of Electroshock for Euthanizing and Immobilizing Adult Spring Chinook Salmon in a Hatchery

This study evaluated the use of electroshock as in alternative to traditional techniques for immobilizing and euthanizing hatchery fish. We used a commercially available electroanesthesia unit at the U.S. Fish and Wildlife Service\u27s Carson National Fish Hatchery (Carson, Washington) to euthanize adult spring Chinook salmon Oncorhynchus tshawytscha and to son and collect gametes of fish at maturation. During euthanization by electroshock, the response of each fish was observed, Muscular and vertebral hemorrhaging wits quantified, and electrical settings were optimized accordingly. During gamete collection, fish were either electroshocked or exposed to tricaine methanesulfortate (MS-222); hemorrhaging, egg viability. egg size and quantity, and resultant fry quality were examined for each treatment group. Electroshocked fish had a higher likelihood Of injury during gamete collection than did fish exposed to MS-222. On average, each electroshocked fish had less than two hemorrhages oil both fillets examined. The size of each hemorrhage was less than 0.10% of the fillet surface. Fecundity and egg and fry quality were not affected by either immobilization method. Electroshock was a viable and efficient means of euthanizing adult spring Chinook salmon or sorting the fish and collecting their gametes. However, equipment settings must be optimized based on site-specific (e.g., water conductivity) and species-specific (e.g., fish size and seasonal state of maturation) factors

The performance of model-based indices given alternative sampling strategies in a climate-adaptive survey design

Species-distribution shifts are becoming commonplace due to climate-driven change. Difficult decisions to modify survey extent and frequency are often made due to this change and constraining survey budgets. This often leads to spatially and temporally unbalanced survey coverage. Spatio-temporal models are increasingly used to account for spatially unbalanced sampling data when estimating abundance indices used for stock assessment, but their performance in these contexts has received little research attention. We therefore seek to answer two questions: (1) how well can a spatio-temporal model estimate the proportion of abundance in a new “climate-adaptive” spatial stratum? and (2) when sampling must be reduced, does annual sampling at reduced density or biennial sampling result in better model-based abundance indices? We develop a spatially varying coefficient model in the R package VAST using the eastern Bering Sea (EBS) bottom trawl survey and its northern Bering Sea (NBS) extension to address these questions. We first reduce the spatial extent of survey data for 30 out of 38 years of a real survey in the EBS and fit a spatio-temporal model to four commercially important species using these “data-reduction” scenarios. This shows that a spatio-temporal model generally produces similar trends and density estimates over time when large portions of the sampling domain are not sampled. However, when the central distribution of a population is not sampled the estimates are inaccurate and have higher uncertainty. We also conducted a simulation experiment conditioned upon estimates for walleye pollock (Gadus chalcogrammus) in the EBS and NBS. Many species in this region are experiencing distributional shifts attributable to climate change with species historically centered in the southeastern portion of the survey being increasingly encountered in the NBS. The NBS was occasionally surveyed in the past, but has been surveyed more regularly in recent years to document distributional shifts. Expanding the survey to the NBS is costly and given limited resources the utility of reducing survey frequency versus reducing sampling density to increase survey spatial extent is under debate. To address this question, we simulate survey data from alternative sampling designs that involve (1) annual full sampling, (2) reduced sampling in the NBS every year, or (3) biennial and full sampling in the NBS. Our results show that annual sampling, even with reduced sampling density, provides less biased abundance information than biennial sampling. We therefore conclude that ideally fishery-independent surveys should be conducted annually and spatio-temporal models can help to provide reliable estimates

Lessons to be learned by comparing integrated fisheries stock assessment models (SAMs) with integrated population models (IPMs)

AEP was partially funded by the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA15OAR4320063, Contribution No. 2023-1331.Integrated fisheries stock assessment models (SAMs) and integrated population models (IPMs) are used in biological and ecological systems to estimate abundance and demographic rates. The approaches are fundamentally very similar, but historically have been considered as separate endeavors, resulting in a loss of shared vision, practice and progress. We review the two approaches to identify similarities and differences, with a view to identifying key lessons that would benefit more generally the overarching topic of population ecology. We present a case study for each of SAM (snapper from the west coast of New Zealand) and IPM (woodchat shrikes from Germany) to highlight differences and similarities. The key differences between SAMs and IPMs appear to be the objectives and parameter estimates required to meet these objectives, the size and spatial scale of the populations, and the differing availability of various types of data. In addition, up to now, typical SAMs have been applied in aquatic habitats, while most IPMs stem from terrestrial habitats. SAMs generally aim to assess the level of sustainable exploitation of fish populations, so absolute abundance or biomass must be estimated, although some estimate only relative trends. Relative abundance is often sufficient to understand population dynamics and inform conservation actions, which is the main objective of IPMs. IPMs are often applied to small populations of conservation concern, where demographic uncertainty can be important, which is more conveniently implemented using Bayesian approaches. IPMs are typically applied at small to moderate spatial scales (1 to 104 km2), with the possibility of collecting detailed longitudinal individual data, whereas SAMs are typically applied to large, economically valuable fish stocks at very large spatial scales (104 to 106 km2) with limited possibility of collecting detailed individual data. There is a sense in which a SAM is more data- (or information-) hungry than an IPM because of its goal to estimate absolute biomass or abundance, and data at the individual level to inform demographic rates are more difficult to obtain in the (often marine) systems where most SAMs are applied. SAMs therefore require more 'tuning' or assumptions than IPMs, where the 'data speak for themselves', and consequently techniques such as data weighting and model evaluation are more nuanced for SAMs than for IPMs. SAMs would benefit from being fit to more disaggregated data to quantify spatial and individual variation and allow richer inference on demographic processes. IPMs would benefit from more attempts to estimate absolute abundance, for example by using unconditional models for capture-recapture data.Publisher PDFPeer reviewe

Titmice are a better indicator of bird density in Northern European than in Western European forests

Publisher Copyright: © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.Population sizes of many birds are declining alarmingly and methods for estimating fluctuations in species’ abundances at a large spatial scale are needed. The possibility to derive indicators from the tendency of specific species to co-occur with others has been overlooked. Here, we tested whether the abundance of resident titmice can act as a general ecological indicator of forest bird density in European forests. Titmice species are easily identifiable and have a wide distribution, which makes them potentially useful ecological indicators. Migratory birds often use information on the density of resident birds, such as titmice, as a cue for habitat selection. Thus, the density of residents may potentially affect community dynamics. We examined spatio-temporal variation in titmouse abundance and total bird abundance, each measured as biomass, by using long-term citizen science data on breeding forest birds in Finland and France. We analyzed the variation in observed forest bird density (excluding titmice) in relation to titmouse abundance. In Finland, forest bird density linearly increased with titmouse abundance. In France, forest bird density nonlinearly increased with titmouse abundance, the association weakening toward high titmouse abundance. We then analyzed whether the abundance (measured as biomass) of random species sets could predict forest bird density better than titmouse abundance. Random species sets outperformed titmice as an indicator of forest bird density only in 4.4% and 24.2% of the random draws, in Finland and France, respectively. Overall, the results suggest that titmice could act as an indicator of bird density in Northern European forest bird communities, encouraging the use of titmice observations by even less-experienced observers in citizen science monitoring of general forest bird density.Peer reviewe