Grass thrips (Anaphothrips obscurus) (Thysanoptera: Thripidae) population dynamics and sampling method comparison in timothy

Sampling studies were conducted on grass thrips, Anaphothrips obscurus (Müller) (Thysanoptera: Thripidae), in timothy, Phleum pratense L. These studies were used to compare the occurrence of brachypterous and macropterous thrips across sampling methods, seasons, and time of day. Information about the population dynamics of this thrips was also revealed. Three absolute and two relative methods were tested at three different dates within a season and three different daily times during four harvest periods. Thrips were counted and different phenotypes were recorded from one of the absolute methods. Absolute methods were the most similar to one another over time of day and within seasonal dates. Relative methods varied in assessing thrips population dynamics over time of day and within seasonal dates. Based on thrips collected from the plant and sticky card counts, macropterous individuals increased in the spring and summer. Thrips aerially dispersed in the summer. An absolute method, the beat cup method (rapping timothy inside a plastic cup), was among the least variable sampling methods and was faster than direct observations. These findings parallel other studies, documenting the commonality of diel and diurnal effects on sampled arthropod abundance and the seasonal effects on population abundance and structure. These studies also demonstrate that estimated population abundance can be markedly affected by temporal patterns as well as shifting adult phenotypes.     

Sampling techniques for thrips (Thysanoptera: Thripidae) in preflowering tomato

Sampling techniques for thrips (Thysanoptera: Thripidae) were compared in preflowering tomato plants at the Coastal Plain Experiment Station in Tifton, GA, in 2000 and 2003, to determine the most effective method of determining abundance of thrips on tomato foliage early in the growing season. Three relative sampling techniques, including a standard insect aspirator, a 946-ml beat cup, and an insect vacuum device, were compared for accuracy to an absolute method and to themselves for precision and efficiency of sampling thrips. Thrips counts of all relative sampling methods were highly correlated (R > 0.92) to the absolute method. The aspirator method was the most accurate compared with the absolute sample according to regression analysis in 2000. In 2003, all sampling methods were considered accurate according to Dunnett's test, but thrips numbers were lower and sample variation was greater than in 2000. In 2000, the beat cup method had the lowest relative variation (RV) or best precision, at 1 and 8 d after transplant (DAT). Only the beat cup method had RV values <25 for all sampling dates. In 2003, the beat cup method had the lowest RV value at 15 and 21 DAT. The beat cup method also was the most efficient method for all sample dates in both years. Frankliniella fusca (Pergande) was the most abundant thrips species on the foliage of preflowering tomato in both years of study at this location. Overall, the best thrips sampling technique tested was the beat cup method in terms of precision and sampling efficiency.     

Sampling methods, dispersion patterns, and fixed precision sequential sampling plans for western flower thrips (Thysanoptera: Thripidae) and cotton fleahoppers (Hemiptera: Miridae) in cotton

A 2-yr field study was conducted to examine the effectiveness of two sampling methods (visual and plant washing techniques) for western flower thrips, Frankliniella occidentalis (Pergande), and five sampling methods (visual, beat bucket, drop cloth, sweep net, and vacuum) for cotton fleahopper, Pseudatomoscelis seriatus (Reuter), in Texas cotton, Gossypium hirsutum (L.), and to develop sequential sampling plans for each pest. The plant washing technique gave similar results to the visual method in detecting adult thrips, but the washing technique detected significantly higher number of thrips larvae compared with the visual sampling. Visual sampling detected the highest number of fleahoppers followed by beat bucket, drop cloth, vacuum, and sweep net sampling, with no significant difference in catch efficiency between vacuum and sweep net methods. However, based on fixed precision cost reliability, the sweep net sampling was the most cost-effective method followed by vacuum, beat bucket, drop cloth, and visual sampling. Taylor's Power Law analysis revealed that the field dispersion patterns of both thrips and fleahoppers were aggregated throughout the crop growing season. For thrips management decision based on visual sampling (0.25 precision), 15 plants were estimated to be the minimum sample size when the estimated population density was one thrips per plant, whereas the minimum sample size was nine plants when thrips density approached 10 thrips per plant. The minimum visual sample size for cotton fleahoppers was 16 plants when the density was one fleahopper per plant, but the sample size decreased rapidly with an increase in fleahopper density, requiring only four plants to be sampled when the density was 10 fleahoppers per plant. Sequential sampling plans were developed and validated with independent data for both thrips and cotton fleahoppers.     

Sequential sampling of Aphis gossypii Glover (Hemiptera: Aphididae) and Frankliniella schultzei Trybom (Thysanoptera: Thripidae) on cotton crop

The goal of this research was to use the sequential probability ratio test to establish a sequential sampling plan for Aphis gossypii Glover and Frankliniella schultzei Trybom infesting cotton. Field work was conducted at the agricultural experimental station of the Universidade Federal da Grande Dourados during the 2003/2004 and 2004/2005 agricultural years. Aphid colonies and individual thrips in the sampling area were counted and their numbers were recorded. The spatial distribution pattern of A. gossypii and F. schultzei in the cotton culture was aggregated. Sequential sampling plans were developed for aphids and thrips with type I and type II errors set at 0.1, common Kc = 0.6081 (aphids) and = 0. 9449 (thrips), and safety and management levels of 20% (aphids) and 40% (thrips) of infested plants. The sampling plans resulted in two decision boundaries for each species, as follows: the upper boundary, indicating when management (population control) is recommended: S1 = 4.6546 + 0.2849n (aphids), and S1 = 3.6514 + 0.1435n (thrips); and the lower boundary, indicating when population control is not necessary: S0 = -4.6546 + 0.2849n (aphids) and S0 = - 3.6514 + 0.1435n (thrips). The highest probability of error when making a decision was 3% for aphids and 2% for thrips, respectively. The maximum number of samples required to reach a decision was 63 for aphids and 95 for thrips.     

Examining the spatial distribution of flower thrips in southern highbush blueberries by utilizing geostatistical methods

Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed "hot spots", in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including "hot spots," can be modeled using either interpolation method. Future studies can use this information to determine if the formation of "hot spots" can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the "hot spots" rather than their entire field.     

Unbiased estimation of greenhouse whitefly, Trialeurodes vaporariorum, mean density using yellow sticky trap in cherry tomato greenhouses

The spatial distribution of the count of adult greenhouse whiteflies, Trialeurodes vaporariorum (Westwood), on yellow sticky traps was analyzed using Taylor's power law and spatial autocorrelation statistics in the cherry tomato greenhouses from 1998–1999. Samples were collected weekly using a cylindrically shaped yellow sticky trap placed in a 5 by 8 grid covering 0.10–0.15 ha in each of five cherry tomato greenhouses. Taylor's (1961) power law indicated that counts of T. vaporariorum on traps were aggregated within greenhouses. Spatial autocorrelation analysis showed that trap catches were similar (positively autocorrelated) to a distance of 12.5 m, and then dissimilar (negatively autocorrelated) at >12.5 m. Autocorrelation-lag plots showed a globally significant spatial relation in 34 of 57 sample-weeks according to Bonferroni's approximation. The presence of this spatial relation was not related to the changes of mean density. Trap counts at the second lag distance (12.5–25 m) showed little spatial autocorrelation and tended to be the most spatially independent. A fixed-precision-level sequential sampling plan was developed using the parameters from Taylor's power law. The presence of spatial dependency in data sets degraded the sampling plan's precision relative to performance in data sets lacking significant spatial autocorrelation. Therefore, to obtain an unbiased mean density of T. vaporariorum per greenhouse, sticky traps should be placed at least >12.5 m apart to ensure that they are spatially independent.     

Sequential sampling plans for western flower thrips (Thysanoptera: Thripidae) on greenhouse cucumbers

The development of cost-effective and reliable sampling programs for the management of western flower thrips, Frankliniella occidentalis (Pergande), on greenhouse cucumbers is important for getting growers to adopt economic injury levels and economic thresholds. The objectives of this study were to develop two sequential sampling plans. A fixed-precision sequential sampling plan was designed for estimating F. occidentalis adult density at a fixed-precision level on cucumber flowers. Also, a sequential sampling plan for classifying thrips population levels as below or above economic thresholds was developed to assist in decision making for the timing of pesticide applications. Both sequential sampling plans were validated using a resampling simulation technique on nine independent data sets ranging in density from 1.25 to 12.95 adults per flower. With the fixed-precision sampling plan, average means obtained in 100 repeated simulation runs were within the 95% CI of the estimated mean for all data sets. Appropriate levels of precision for the different population densities were recommended based on the simulation results. With sequential sampling for classifying the population levels of thrips in terms of an economic threshold, it has the advantage of requiring smaller sample sizes to determine the population status when the population densities differ greatly from the critical density (i.e., economic threshold). However, this plan needs a great number of samples when population density is close to the critical density. In this case, use of a combination of both sampling plans is recommended.     

西花蓟马在黄瓜和架豆上的空间分布型及理论抽样数

西花蓟马Frankliniella occidentalis（Pergande）已在北京部分蔬菜园区成功定居,对蔬菜生产造成危害。为了解该虫在蔬菜田的种群空间分布型,指导田间取样,本文应用几种聚集度指标的计算公式以及Taylor、Iwao的回归方程式,分析和测定了西花蓟马的种群空间分布格局。结果表明：在黄瓜和架豆上西花蓟马的空间分布型一致,均为聚集分布型;该虫的空间分布型不受种群密度的影响,并且也不随取样时间的变化而变化。种群数量动态研究显示西花蓟马成虫和若虫在黄瓜植株的中部叶片分布较多（从顶部向下数第4叶至第17叶片上成、若虫的分布数量多于其他叶片上的数量）,未展开的叶片、嫩叶和下部老叶危害较轻。用Iwao回归法中α、β参数计算出在允许误差范围内的理论抽样数。     

Evaluation of yellow sticky traps for monitoring the population of thrips (Thysanoptera) in a mango orchard

Populations of several thrips species were estimated using yellow sticky traps in an orchard planted with mango, Mangifera indica L. during the dry and wet seasons beginning in late 2008-2009 on Penang Island, Malaysia. To determine the efficacy of using sticky traps to monitor thrips populations, we compared weekly population estimates on yellow sticky traps with thrips population sizes that were determined (using a CO(2) method) directly from mango panicles. Dispersal distance and direction of thrips movement out of the orchard also were studied using yellow sticky traps placed at three distances from the edge of the orchard in four cardinal directions facing into the orchard. The number of thrips associated with the mango panicles was found to be correlated with the number of thrips collected using the sticky trap method. The number of thrips captured by the traps decreased with increasing distance from the mango orchard in all directions. Density of thrips leaving the orchard was related to the surrounding vegetation. Our results demonstrate that sticky traps have the potential to satisfactorily estimate thrips populations in mango orchards and thus they can be effectively employed as a useful tactic for sampling thrips.     

沿黄稻区灰飞虱越冬种群的时空分布及抽样技术研究

2009—2010年间系统调查了河南省境内沿黄稻区灰飞虱Laodelphax striatellus Fallén越冬种群的时空动态变化,并在田间调查的基础上,运用聚集度指标法和改进的Iwao回归分析法对沿黄稻区灰飞虱越冬种群的空间分布格局、理论抽样数及序贯抽样技术进行了研究。结果表明,灰飞虱在沿黄流域主要以少量3、4龄若虫进行越冬,11月下旬进入越冬期,翌年4月上中旬为越冬代种群发生高峰期,成、若虫虫量分别为22头/m2、43头/m2;灰飞虱成虫在5月中旬开始向水稻田迁飞,迁入高峰时虫量在450头/m2以上;灰飞虱越冬代成虫在麦田中空间分布型的聚集性指标大于1,空间分布型为聚集分布型,在此基础上得出灰飞虱田间调查的理论抽样数公式:N=t2/D2(0.324/m+0.086),并且描述了灰飞虱种群序贯抽样的Iwao模型:Tn=25n±15.4n~(1/2)。研究结果为田间灰飞虱的准确抽样调查和有效防治提供了科学依据。     

Seasonal abundance and spatial distribution of larval and adult thrips (Thysanoptera) on weed host plants in mango orchards in Penang,Malaysia

The spatial distribution of larval and adult thrips (Thysanoptera) was studied on mango panicles, Mangifera indica L., on Penang Island, Malaysia, during two consecutive mango flowering seasons from December 2008 to March 2009 and from August to September 2009. Larval and adult thrips were sampled from mango panicles using the carbon dioxide (CO₂) collection technique weekly in treated and untreated orchards. Seasonal abundance and dispersion pattern of thrips were investigated on weed host plants in the treated orchard between June 2008 and March 2009. Spatiotemporal dynamics of larvae and adults was analyzed using Taylor’s Power Law (TPL), Lloyd’s Index (LI), and Green’s Index (GI). Thrips hawaiiensis (Morgan) was the dominant thrips species recovered from mango panicles and weeds in the treated orchard, whereas Scirtothrips dorsalis (Hood) was the most abundant species captured in the untreated orchard. Thrips adults and larvae analyzed via dispersion indices were found to be aggregated in mango panicles in both orchards. The value of the aggregation index (b) of TPL for the total number of adult thrips was significantly higher in the treated orchard than in the untreated orchard, whereas slopes of TPL for the total number of larval thrips did not differ significantly between treated and untreated orchards. All adult thrips species were distributed regularly on the weed plants; however, their larvae were aggregated. It is concluded that pesticide treatment caused adult thrips to become more aggregated on mango panicles, hiding in flower parts that were less exposed to the chemicals.     

Spatiotemporal population dynamics of the banana rind thrips,Elixothrips brevisetis (Bagnall) (Thysanoptera: Thripidae)

The understanding of how environmental factors and agricultural practices affect population dynamics of insect pests is necessary for pest management. Here, we provide insight into the ecology of the banana rind thrips Elixothrips brevisetis (Bagnall) (Thysanoptera: Thripidae) by collecting and analysing a spatiotemporal database of population estimates in Martinique (West French Indies). We assessed the influence of climatic variables (which were rainfall and temperature) and biotic variables (which were banana and three weed species) on the adult thrips abundance for different components of the banana plant (sucker, mother plant and bunch) and evaluated the effect of thrips abundance and standard bunch covers on damages. The abundance of thrips on the sucker, the mother plant, and the bunch was significantly related to the abundance on neighbouring banana plants, and spatial autocorrelation indicated that E. brevisetis dispersed for only short distances. The number of thrips on the mother plant and on the bunch was positively related to the number of thrips on the sucker, suggesting that the thrips may disperse from the sucker to the mother plant and then to the bunch. The abundance of thrips on the sucker increased with sucker height and was positively correlated with the mean daily rainfall during the 17 days before sampling; the length of that period might correspond with the time required for an individual to complete its life cycle. Covered bunches had 98% fewer thrips than non‐covered bunches, and the damage caused by thrips was linearly related to the number of thrips present between the 2nd and 4th week after flowering. Finally, we found that the presence of Alocasia cucullata, Dieffenbachia seguine and Peperomia pellucida is significantly related with a decrease in thrips abundance on banana plants, suggesting the use of these weeds as potential trap plants.     

Assessing abundance and distribution of an invasive thrips Frankliniella schultzei (Thysanoptera: Thripidae) in south Florida

Within-plant and within-field distribution of larvae and adults of an invasive thrips species, Frankliniella schultzei (Trybom) on cucumber, Cucumis sativus L. was studied in 2008 and 2009 in Homestead, Florida. The majority of thrips were found inhabiting flowers of cucumber plants and little or none was found on the other parts of the plant. Thrips were aggregated in the field, as indicated by the two regression models, Taylor's power and Iwao's patchiness regression. Iwao's patchiness regression provided a better fit than Taylor's power law. The distribution was clumped during the initial stages of infestation at the edges of the field and became random thereafter. However, with increase in population density, thrips again formed aggregates in the field. Based on the average pest density per flower in a ～0.25-ha field, minimum sample size (number of flowers) required at the recommended precision level (0.25) was 51. The number of samples required at two levels of predetermined pest density was also calculated, which would help growers in collecting optimum number of samples required to determine the correct threshold level of pest in fields. Results from seasonal abundance indicated that density of thrips peaked during the fifth week of sampling with an average of 25 and 34 adults per ten flowers during autumn 2008 and 2009, respectively. Results from these studies will help growers and extension personnel in understanding the abundance and distribution of F. schultzei in the field, which are important components required in developing a sound management program.     

Damage to nectarines by the western flower thrips (Thysanoptera: Thripidae) in the interior of British Columbia, Canada

The phenology of damage by the western flower thrips, Frankliniella occidentalis (Pergande), on nectarines was investigated using sticky cards and direct sampling of buds between 1993 and 1995 in the interior of British Columbia, the most susceptible period for damage by western flower thrips to nectarines. The life stage responsible for damage and variation in susceptibility to damage of 11 different nectarine varieties were determined. To evaluate the predictive ability of 2 sampling methods, thrips were counted from both buds and sticky cards before petal fall and correlated to damage levels at husk drop. Damage to nectarines was caused almost entirely by larval feeding at petal fall. No predictive relationships between adult or larval densities of western flower thrips and subsequent damage to fruit were apparent. Varieties did not differ in terms of larval densities at petal fall or the subsequent damage to fruit. Female western flower thrips oviposit in nectarine buds from dormant through bloom stages primarily in sepal tissues in the early buds, and in filaments and petals as these become available.     

香蕉园黄胸蓟马成虫种群的活动节律、消长规律与空间分布

为系统明确黄胸蓟马在香蕉园的活动节律、消长规律与空间分布。采用蓝色诱虫板诱集法和田间踏查法,在2016—2018年期间调查了香蕉园黄胸蓟马成虫的活动高度情况、日间节律、以及不同香蕉品种(南天黄、巴西蕉与皇帝蕉)与不同地区(海南澄迈、广西玉林与云南景洪)的种群消长规律,同时分析了其空间分布格局与性比。结果显示:高度与蓟马种群数量密切相关,2—6 m是香蕉园黄胸蓟马的主要活动高度范围;蓟马种群的活动节律在晴、阴与雨天基本一致,日活动高峰时段为12:00—16:00时,夜间和阴雨天均活动少;黄胸蓟马的种群动态不受香蕉作物品种和地理区域的影响,但与香蕉作物的生长期密切相关;年度消长规律呈现单峰型,香蕉进入花蕾期时,蓟马种群数量快速增长,盛花期时达到高峰,其余时期少有发生。聚集指标与Taylor回归法分析共同表明黄胸蓟马成虫在香蕉园的空间分布型为聚集式分布。性比调查发现黄胸蓟马在香蕉花蕾内的雌虫比例约为70%,表明该虫是一个雌性为主的种群。为揭示黄胸蓟马的灾变规律提供了基础数据,同时可为香蕉蓟马的适时与精准化监测预报及防治提供指导依据。     

Spatial distribution and sampling plan of the phytophagous stink bug complex in different soybean production systems

Phytophagous stink bugs are major soybean pests, and knowledge of spatial distribution models of the pest in the crop is fundamental to establishing an appropriate sequential sampling plan, and thus, allowing the correct utilization of control strategies. This work aimed to study the spatial distribution of phytophagous stink bugs in soybean grown in different cropping systems and determine a sequential sampling plan. The experiment was conducted in Maracaju, MS, Brazil, during the agricultural year of 2012/2013. Soybean cultivars BRS 284 and SYN 1163 RR were placed in an experimental area comprising six fields (two soybean cultivars × three cropping systems). Sampling was performed weekly, using a beat cloth per plot and counting the number of stink bugs found, virtually throughout the soybean reproductive period. Concerning the statistical analyses, we used the dispersion indexes (variance‐to‐mean ratio, Morisita's index, exponent k of the negative binomial and Green's coefficient) and probabilistic methods of frequency adjustment (negative binomial and Poisson). Adult and nymph phytophagous stink bugs showed aggregate disposition in the field regardless of the cropping system, and their numbers were adjusted to the negative binomial probability distribution. There was no difference in the behaviour of adult and nymphs considering the tested cultivars. We elaborated a practical sequential sampling plan for phytophagous stink bug complexes, considering crops intended for the production of grains and seeds.     

Evaluation of sampling methodology for determining the phenology, relative density, and dispersion of western flower thrips (Thysanoptera: Thripidae) in nectarine orchards

Western flower thrips, Frankliniella occidentalis (Pergande), cause serious economic damage to nectarines in the Okanagan and Similkameen Valleys, British Columbia, Canada. We evaluated several sampling methods for western flower thrips for their precision and ability to predict general population trends. Beating of branches, flicking of buds, and visual estimation methods were not accurate for estimating numbers of thrips in nectarine buds. Thrips caught on sticky cards indicated general population trends, but were less efficient than collecting nectarine buds and counting thrips. Searching for thrips from buds in the field underestimated the density of both adults and larvae, and for adults, underestimated the proportion of the pale morph of western flower thrips. Dispersion patterns of thrips populations among orchards were either random or aggregated dependent on the development stage of the nectarine buds.     

Horizontal and vertical distribution of flower thrips in southern highbush and rabbiteye blueberry plantings, with notes on a new sampling method for thrips inside blueberry flowers

The dispersal behavior of flower thrips was studied during two field seasons within blueberry (Vaccinium spp.) plantings in Florida and southern Georgia. A "shake and rinse" technique used to extract thrips from inside the blueberry flowers was not significantly different from the conventional dissecting technique, but the time taken to complete the extraction of thrips was significantly shorter. Overall, the highest concentration of thrips was captured inside the canopy of blueberry bushes. Using a grid of traps to monitor the dispersal of thrips during the blueberry flowering season, we analyzed their dispersion with graphical and analytical methods to determine and describe their distribution within blueberry plantings. Thrips began to form "hot-spots" 5-7 d after bloom initiation. A hot-spot is defined as a large number of thrips concentrated in a small area of the field, whereas the rest of the field has a low population. The behavior of the population inside these hot-spots fit a Gaussian tendency and a regression was conducted to describe this tendency. Green's and Standardized Morisita's indices were used to determine thrips level of aggregation. Results showed significantly aggregated populations of thrips in both years. Formation of hot-spots in blueberry plantings seemed to be random. However, the formation of hot-spots was higher in places where more than seven thrips per day were captured on sticky traps, 5 to 7 d after the bloom begins. With these results, producers will be able to monitor thrips populations and locate and manage hot-spots before they become a more serious a problem on blueberry farms.     

Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models

Aim Environmental niche models that utilize presence‐only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for ‘training’ and ‘testing’ purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation. Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana. Methods Two types of niche modelling algorithms – the genetic algorithm for rule‐set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) – were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence‐only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. Results The accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusions The findings indicate that care should be taken when interpreting the results from presence‐only niche models when training and test data have been randomly partitioned but occurrence data were non‐randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.