Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.

Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

Fitting Generalized Additive Logistic Regression Model with GAM Procedure

In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.

Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model

In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters

Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine species.The response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and seasons.Therefore,it is necessary to determine the optimal combination of environmental variables in HSI modelling.In this study,generalized additive models(GAMs)were used to determine which environmental variables to be included in the HSI models.Significant variables were retained and weighted in the HSI model according to their relative contribution(%)to the total deviation explained by the boosted regression tree(BRT).The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013–2017.Ontogenetic and seasonal variations in HSI models of mantis shrimp were also examined.Among the four models(non-optimized model,BRT informed HSI model,GAM informed HSI model,and both BRT and GAM informed HSI model),both BRT and GAM informed HSI model showed the best performance.Four environmental variables(bottom temperature,depth,distance offshore and sediment type)were selected in the HSI models for four groups(spring-juvenile,spring-adult,falljuvenile and fall-adult)of mantis shrimp.The distribution of habitat suitability showed similar patterns between juveniles and adults,but obvious seasonal variations were observed.This study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models,and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species.

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

The spatiotemporal distribution and relationship between nominal catch-per-unit-ef fort(CPUE) and environment for the jumbo flying squid( Dosidicus gigas) were examined in of fshore Peruvian waters during 2009–2013. Three typical oceanographic factors aff ecting the squid habitat were investigated in this research, including sea surface temperature(SST), sea surface salinity(SSS) and sea surface height(SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive(SAR) model and a generalized additive model(GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°–82.7°W and 11.9°–17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°–81.2°W and 14.3°–15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°–21.9°C for SST, 35.16–35.32 for SSS and 27.2–31.5 cm for SSH in the areas bounded by 78°–80°W/82–84°W and 15°–18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas off shore Peru, and off er a new SAR modeling method for advancing fishery science.

Linearity extensions of the market model:a case of the top 10 cryptocurrency prices during the pre‑COVID‑19 and COVID‑19 periods

This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods.Two extensions are offered to compare the performance of the linear specification of the market model(LMM),which allows for the measurement of the cryptocurrency price beta risk.The first is the generalized additive model,which permits flexibility in the rigid shape of the linearity of the LMM.The second is the time-varying linearity specification of the LMM(Tv-LMM),which is based on the state space model form via the Kalman filter,allowing for the measurement of the time-varying beta risk of the cryptocurrency price.The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization,using the Crypto Currency Index 30(CCI30)as a market proxy and 1-day and 7-day forward predictions.Such a comparison of cryptocurrency prices has yet to be undertaken in the literature.The empirical findings favor the Tv-LMM,which outperforms the others in terms of modeling and forecasting performance.This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear,especially during the COVID-19 period.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

Modelling the population fluctuation of winter moth and mottled umber moth in central and northern Germany

Background: Winter moth(Operophtera brumata) and mottled umber moth(Erannis defoliaria) are forest Lepidoptera species characterized by periodic high abundance in a 7–11 year cycle. During outbreak years they cause severe defoliation in many forest stands in Europe. In order to better understand the spatio-temporal dynamics and elucidate possible influences of weather, stand and site conditions, a generalized additive mixed model was developed. The investigated data base was derived from glue band catch monitoring stands of both species in Central and North Germany. From the glue bands only female moth individuals are counted and a hazard code is calculated. The model can be employed to predict the exceedance of a warning threshold of this hazard code which indicates a potential severe defoliation of oak stands by winter moth and mottled umber in the coming spring.Results: The developed model accounts for specific temporal structured effects for three large ecoregions and random effects at stand level. During variable selection the negative model effect of pest control and the positive model effects of mean daily minimum temperature in adult stage and precipitation in early pupal stage were identified.Conclusion: The developed model can be used for short-term predictions of potential defoliation risk in Central and North Germany. These predictions are sensitive to weather conditions and the population dynamics. However, a future extension of the data base comprising further outbreak years would allow for deeper investigation of the temporal and regional patterns of the cyclic dynamics and their causal influences on abundance of winter moth and mottled umber.

A spatially-explicit count data regression for modeling the density of forest cockchafer(Melolontha hippocastani) larvae in the Hessian Ried(Germany)

Background： In this paper, a regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region （Germany） is presented. The forest cockchafer, a native biotic pest, is a major cause of damage in forests in this region particularly during the regeneration phase. The model developed in this study is based on a systematic sample inventory of forest cockchafer larvae by excavation across the Hessian Ried. These forest cockchafer larvae data were characterized by excess zeros and overdispersion. Methods： Using specific generalized additive regression models, different discrete distributions, including the Poisson, negative binomial and zero-inflated Poisson distributions, were compared. The methodology employed allowed the simultaneous estimation of non-linear model effects of causal covariates and, to account for spatial autocorrelation, of a 2-dimensional spatial trend function. In the validation of the models, both the Akaike information criterion （AIC） and more detailed graphical procedures based on randomized quantile residuals were used. Results： The negative binomial distribution was superior to the Poisson and the zero-inflated Poisson distributions, providing a near perfect fit to the data, which was proven in an extensive validation process. The causal predictors found to affect the density of larvae significantly were distance to water table and percentage of pure clay layer in the soil to a depth of I m. Model predictions showed that larva density increased with an increase in distance to the water table up to almost 4 m, after which it remained constant, and with a reduction in the percentage of pure clay layer. However this latter correlation was weak and requires further investigation. The 2-dimensional trend function indicated a strong spatial effect, and thus explained by far the highest proportion of variation in larva density. Conclusions： As such the model can be used to support forest practitioners in their decision making for regeneration and forest protection planning in the Hessian predicting future spatial patterns of the larva density is still comparatively weak. Ried. However, the application of the model for somewhat limited because the causal effects are

西北印度洋鸢乌贼(Sthenoteuthis oualaniensis)灯光罩网渔业CPUE标准化研究

单位捕捞努力量渔获量(CPUE)是渔业资源评估的前提和基础,为了更好地评估西北印度洋鸢乌贼(Sthenoteuthis oualaniensis)资源丰度,掌握鸢乌贼资源分布与时空因子及海洋环境因子之间的关系,利用广义线性模型(GLM)和用广义加性模型(GAM),结合时空因子及海洋环境因子对2015~2021年西北印度洋鸢乌贼灯光罩网渔业CPUE进行标准化研究,并评价各因子对CPUE的影响。GLM模型结果显示,6个变量对CPUE影响显著,依次为年份(Year)、月份(Month)、经度(lon)、纬度(lat)、海表面温度(SST)、叶绿素a浓度(chl a);依次加入六个显著变量构建GAM模型,并通过对不同GAM模型的AIC值进行比较,结果显示包含6个变量的GAM模型AIC值最小且模型最优,对CPUE偏差的解释率为37.80%。研究表明,西北印度洋鸢乌贼高CPUE主要出现在9月至翌年3月期间,13°N~20°N、58°E~66°E、SST为26.0~29.0°C、chl a为0.07~0.73 mg/m^(3)的海域内。整体而言,基于GLM模型和GAM模型对西北印度洋鸢乌贼灯光罩网渔业CPUE标准化研究,能够较好地反映鸢乌贼资源的丰度变化情况。

Identification and Estimation of Generalized Additive Partial Linear Models with Nonignorable Missing Response

The generalized additive partial linear models(GAPLM)have been widely used for flexiblemodeling of various types of response.In practice,missing data usually occurs in studies of economics,medicine,and public health.We address the problem of identifying and estimating GAPLM when the response variable is nonignorably missing.Three types of monotone missing data mechanism are assumed,including logistic model,probit model and complementary log-log model.In this situation,likelihood based on observed data may not be identifiable.In this article,we show that the parameters of interest are identifiable under very mild conditions,and then construct the estimators of the unknown parameters and unknown functions based on a likelihood-based approach by expanding the unknown functions as a linear combination of polynomial spline functions.We establish asymptotic normality for the estimators of the parametric components.Simulation studies demonstrate that the proposed inference procedure performs well in many settings.We apply the proposed method to the household income dataset from the Chinese Household Income Project Survey 2013.

The study on fishing ground of neon flying squid, Ommastrephes bartrami,and ocean environment based on remote sensing data in the Northwest Pacific Ocean

The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature （SST）, chlorophyll concentration （Chl-α） and sea surface height （SSH） from NASA, as well as the yields of neon flying squid in the North Pacific Ocean. The results show that the favorable temperature for neon flying squid living is 10℃-22℃ and the favorite temperature is between 15℃-17℃. The Chl-α concentration is 0.1-0.6 mg/m^3. When Chl-α concentration changes to 0.12-0.14 mg/m^3, the probability of forming fishing ground becomes very high. In most fishing grounds, the SSH is higher than the mean SSH. The generalized additive model （GAM） was applied to analyze the correlations between neon flying squid and ocean environmental factors. Every year, squids migrate northward from June to August and return southward during October-November, and the characteristics of the both migrations are very different. When squids migrate to the north, most relationships between the yields and SST are positive. The relationships are negative when squids move to southward. The relationships between the yields and Chl-a concentrations are negative from June to October, and insignificant in November. There is no obvious correlation between the catches of squid and longitude, but good with latitude.

The bacterial abundance and production in the East China Sea: seasonal variations and relationships with the phytoplankton biomass and production

The East China Sea is a productive marginal sea with a wide continental shelf and plays an important role in absorbing atmospheric carbon dioxide and transferring terrigenous organic matter to the open ocean. To investigate the roles of heterotrophic bacteria in the biogeochemical dynamics in the East China Sea, bacterial biomasses （BB） and productions （BP） were measured in four cruises. The spatial distributions of the BB and the BP were highly season-dependent. Affected by the Changjiang River discharge, the BB and the BP were high in shelf waters （bottom depth not deeper than 50 m） and generally decreased offshore in August 2009. In December 2009 to lanuary 2010, and November to December 2010, the BB and the BP were high in waters with medium bottom depth. The onshore-offshore decreasing trends of the BB and the BP also existed in May-June 2011, when the BB was significantly higher than in other cruises in shelf break waters （bottom depth deeper than 50 m but not deeper than 200 m）. The results of generalized additive models （GAM） suggest that the BB increased with the temperature at a range of 8-20~C, increased with the chlorophyll concentration at a range of 0.02-3.00 mg/m3 and then declining, and decreased with the salinity from 28 to 35. The relationship between the temperature and the log-transformed bacterial specific growth rate （SGR） was linear. The estimated temperature coefficient （Q10） of the SGR was similar with that of the phytoplankton growth. The SGR also increased with the chlorophyll concentration. The ratio of the bacterial to phytoplankton production ranged from less than 0.01 to 0.40, being significantly higher in November December 2010 than in May-June 2011. Calculated from the bacterial production and growth efficiency, the bacterial respiration consumed, on average, 59%, 72% and 23% of the primary production in August 2009, November-December 2010, and May-/une 2011, respectively.

Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government's monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.

Evaluating the impact of spatio-temporal scale on CPUE standardization

This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.