Probability Of Catch Research Articles

Catch uncertainty and recreational fishing attraction: Propositions and future research directions

AbstractWhy do people fish for recreation? Social science literature suggests that both catch (e.g., number or sizes of fish) and non‐catch dimensions (e.g., nature experience, temporary escape) play a role. After reviewing the literature from environmental psychology, neuroscience, anthropology, recreational fisher motivation research as well as popular fishing books, I find that the opposite of catching fish, more specifically the uncertainty of the catch, maybe another, perhaps fundamental force that explains the attraction of the activity to millions of people. There appears to be strong utility in the gaming nature of the activity. This quality may contribute to explain various patterns that are well known, e.g., the overinvestment of time and money by recreational fishers that drastically exceed the market value of fish, the lack of self‐regulation of a local recreational fishery in terms of effort being spent also on low stock sizes, the disutility associated with providing certain catch probability information, diminishing marginal utility return for increasing catch rates, management regulations that make fishing harder than necessary, suboptimal satisfaction despite rising catch rates, and finally the dominance of men among populations of recreational fishers. I present a serious of testable propositions and call for a novel research focus that seeks to better understand what makes catch ambiguity attractive psychologically and emotionally.

Neural network approach for detecting spatial changes in catch probability of Engraulis ringens during El Niño‐Southern Oscillation events in northern Chile

AbstractEngraulis ringens (anchovy) is a small pelagic fish of the Family Engraulidae that inhabits the neretic‐coastal zone from northern Peru to south‐central Chile. It is the main resource species of industrial fishing of northern Chile, representing 80% of the annual landings of the purse seine fleet. The history of this fishery (1985–2023) shows a strong decrease in annual industrial landings, especially during extreme El Niño–Southern Oscillation (ENSO) events. The greatest decrease in landings in two decades occurred in 2020, coinciding with a cold La Niña event, which had not been observed in previous La Niña events. We evaluated whether the current decrease in annual landings of E. ringens is associated with oceanographic changes in northern Chile during El Niño or La Niña events. We applied a neuronal network model to identify the spatial and temporal distribution of E. ringens using the catch probability of each boat of the industrial purse seine fleet. The selected oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height and currents) for the 2003–2020 period were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS program) and used as predictor variables of the monthly landings of E. ringens. The neural network model explained 97% of the monthly variability of catch probability of E. ringens by the industrial purse seine fleet. The spatial distribution of catch probability of E. ringens was analyzed independently for El Niño (2015), La Niña (2007, 2013, and 2020), and Neutral (2004) years. We found that catch probability extended further west during La Niña events (except for 2020), occupying a greater area, but were limited to a 10 nautical mile coastal strip during the El Niño event. The spatial distribution of catch probability in the Neutral condition was near the coast, although not as restricted as during the El Niño event. The higher catch probabilities in the La Niña event of 2020 were near the coast, in contrast to the previous La Niña events of 2007 and 2013, due to the restriction of the optimal habitat of E. ringens in response to changes in oceanographic conditions. The application of the results of this study will allow understanding and probably anticipating the consequences that extreme ENSO events could have on the catch probability of the industrial anchovy purse seine fleet in northern Chile.

Identifying the ecologically significant habitats of Yellow-fin tuna (Thunnus albacares, Bonnaterre, 1788) of Iranian purse seine fishery in the Gulf of Oman and Indian Ocean: An approach using satellite imagery and fishery data

Ecological fluctuations are influential factors affecting marine fish populations. The main primary objective of this study was to determine the catch probability of yellowfin tuna (Thunnus albacares, Bonnaterre, 1788) in the Indian Ocean and the Gulf of Oman, as well as its environmental preferences. This study employs generalized additive models (GAMs) to examine the spatiotemporal pattern of yellowfin tuna purse seiners, placing particular emphasis on the analysis of satellite imagery and environmental data. The results derived from the GAMs showed that the presence of yellowfin tuna was related to factors including sea surface height (SSH), salinity (SAL), latitude, and sea surface current velocities (velocity). Based on the findings of this research, it can be concluded that Iranian waters of the Gulf of Oman provide suitable habitats for yellowfin tuna during the autumn season, while in the Indian Ocean, these conditions are observed during the summer. Furthermore, the probability of successfully catching yellowfin tuna is higher in the Gulf of Oman than in the studied region of the Indian Ocean, with the catch probability predicted to be higher between 57°E and 69°E and for latitudes ranging from 5°N to 5°S and 20°−25°N. The study also found that areas with a sea surface temperature (SST) of 23–33 °C, SSH of 151–223 cm, and chlorophyll-a (Chl-a) concentration of 0.08–0.6 mg/m3 achieve higher catches than those with low SST, SSH, and Chl-a concentration. The model trained with data from all seasons was used to examine the seasonal variation in potential fishing grounds. Yellowfin tuna abundance varies seasonally and from year to year, depending on sea conditions and their behavior.

The influence of bait position on the catch of target and non-target sharks in a SMART drumline bather protection program

There is increasing support for shark bite mitigation measures, such as SMART (Shark-Management-Alert-in-Real-Time) drumlines that minimise impacts on target sharks and other marine animals. On the east coast of Australia, SMART drumlines are used in a shark management program to catch and relocate white (Carcharodon carcharias), tiger (Galeocerdo cuvier), and bull sharks (Carcharhinus leucas; herein referred to as target sharks). This study examines the effect of bait position relative to the seabed on SMART drumline catches in eastern Australian waters, aiming to optimise catches of target sharks while reducing bycatch. Over 17 months, SMART drumlines were deployed at Ballina and Evans Head, New South Wales. Trace extensions were attached to 3.2 m standard traces to test the effect of bait height above the seabed on shark catch in an experimental design that alternated bait position every fortnight. White and tiger shark catches accounted for 67% of the total catch, whereas bull sharks were infrequently caught (3%). Bait position above the seabed did not significantly influence catch probability of white and tiger sharks. However, catches of Critically Endangered grey nurse sharks (Carcharias taurus) and false alarm events significantly increased when baits were closer to the seabed. Catches of white and tiger sharks varied throughout the year and were linked to seasonal water temperature changes. The standard traces effectively caught target shark species whilst reducing catches of grey nurse sharks and false alarm events, highlighting that the trace length currently used for NSW SMART drumline deployments is optimal.

How catch uncertainty and harvest regulations drive anglers’ choice for pike (Esox lucius) fishing in the Baltic Sea

In the brackish lagoons located in coastal inshore areas around the German island of Rügen, pike Esox lucius is a highly sought after target species for recreational fishers. Yet, the pike stock is currently in decline, and conflicts with other user groups (specifically commercial fishers) have emerged. To inform future fisheries management, an improved understanding of the human dimensions of recreational fishers, including their preferences for catch outcomes and regulations, is relevant. Our objective was to better understand how German recreational pike fishers react to different types of harvest regulations in trade-off with risky or ambiguous catch outcomes for pike and other predators in the lagoon fishery. We conducted an online survey including choice experiments for angling experiences that differed in seven attributes. Data were analyzed using a latent class model. We found four distinct angler types differentiating resident and tourists and dependency on pike. Specifically, we identified a highly involved pike tourist (42 % average probability), a consumptive, generalist tourist angler type (18 %), a highly specialized local trophy pike angler type that readily engaged in catch-and-release (12 %), and a local generalist angler that also benefitted from the catch of other predators than pike (18 %). Despite heterogeneity in preferences, all four angler types generally accepted more restrictive regulations for pike removals than currently in place, and dispreferred low catch probabilities compared to catch ambiguity. Most angler types also revealed diminishing marginal utility returns for catch rates, where the first three pike in a day provide utility, while the marginal benefits of catch rate decrease to zero for more than three pike per day. Angler types varied more in their preferences for trophy pike, the general attractiveness of the pike as a target and the utility derived from catching fish other than pike. Particularly the more specialized angler types focused on size rather than catch rate. Despite preference heterogeneity, focusing management attention on maintaining a sufficiently high catch rate and increasing the number of trophy pike in the stock seems to satisfy the expectations of most angler types. To achieve such outcomes, the anglers of all types would overwhelmingly support implementing harvest slots of 50–85 cm that allow both small and large fish to be released (rather than the current minimum-length limit of 50 cm), and accept reductions in the daily bag limit to 1 pike per day (relative to the current of 3 pike per day). Implementing these policy measures would unlikely generate substantial conflict with anglers represented in our survey.

Effect of hook eye alignment on deep hooking rates for stream‐dwelling trout caught with baited circle and J hooks

It has been well documented that circle hooks generally reduce deep hooking of bait-caught fish. However, for decades there has been speculation that the hook eye must be straight relative to the shank for circle hooks to function properly, yet this aspect of hook configuration has rarely been investigated. Using a passive hook set when strikes were detected, we compared deep hooking rates and catch probability for stream-dwelling trout (Yellowstone cutthroat trout [Oncorhynchus clarkii bouvieri Jordan & Gilbert], rainbow trout [O. mykiss Walbaum], and cutthroat trout × rainbow trout hybrids) caught using baited circle and J hooks with the eye either straight with or turned-up from the shank. Landed fish averaged 26 cm in total length and ranged from 11 to 46 cm. Most fish (83%) were hooked either in the upper or lower jaw, but 16% were hooked deeply (i.e., either in the esophagus or the gills). As expected, the deep hooking rate was lower for circle hooks (10%) than for J hooks (24%). Logistic regression model results indicated that hook eye orientation had no effect on deep-hooking, with deep hooking rates of 11.0% and 8.2% for circle hooks with angled-up and straight eyes, respectively, compared to 21.7% and 25.3% for J hooks with angled-up and straight eyes. Model results also revealed that deep hooking probability differed among anglers but did not differ between species and was not related to fish length. Catch probability (i.e., the proportion of fish strikes that resulted in a landed fish) did not differ between hooks or hook eye orientations. Contrary to some perceptions, our results suggest that changing the angle of the hook eye does not affect deep hooking rates when anglers use baited circle hooks to land stream-dwelling trout.

Comparative analysis of three bait types in deep-set pelagic longline gear in the Equatorial Atlantic Ocean

The choice of bait is one of the fisheries tactics used to increase selectivity for particular target species. The performance of three bait types (mackerel, sardine, squid) was evaluated with a commercial vessel operating in the Equatorial Atlantic Ocean using the deep-set pelagic longline deployment method to target large yellowfin and bigeye tunas. The effect of different factors and covariates on the Capture per Effort Unit - CPUE was evaluated through Generalized Linear Models (GLM). In 121 experimental sets using three bait types, 2385 individuals of the two target species were captured, 1166 yellowfin tuna and 1219 bigeye tuna. The results suggest a preference between bait types for each target species, with the yellowfin tuna being mostly caught by the hooks using squid and bigeye tuna with fish bait mackerel. Stratifying the results for three depth ranges of the hooks, the combination of bait and depth for yellowfin tuna resulted in an increase of catch probability in the intermediary depth layer using mackerel. For bigeye tuna, using mackerel in the intermediary layer resulted in a reduction in the catch rate. Bycatch represented around 11.15% of total captures. These results will provide important information to choosing the most efficient bait for the pelagic longline fishing operation and will help future decisions of fisheries management.

Lagrangian characteristics in the western North Pacific help to explain variability in Pacific saury fishery

A new model for estimation of daily probability for the Pacific saury (Cololabis saira) encounter was proposed. The model performance was tested for the period of 2004–2018 (August–November) using the data from the Russian vessel monitoring system. The following physical oceanographic variables were used for encounter probability prediction: the absolute values and gradients (∇) of speed (V) of passive particles, imitating water parcels, and Lagrangian indicators. The positive effects on the encounter probability of saury were found for V, ∇V, and for the gradient of the finite-time Lyapunov exponent (∇Λ), while the effect of particle path length was negative. That means that saury preferred places close to the boundaries of the oceanographic features, where Lagrangian fronts are situated, but not inside the features themselves, because Λ is small in regular flows and large at Lagrangiam fronts. The model did not include information about years and volume of saury catches, but its monthly mean of catch probability in September had the highest correlation with Russian annual catches outside the national waters between Russia and Japan (r = 0.76, p = 0.001) and total annual catches there (r = 0.73, p = 0.002). Timeseries analysis of principle components (PC) from daily predictions of saury catch probabilities has also shown that the third PC correlated highly with the annual biomass of saury (r ≥ 0.8, p < 0.05). The model seems to be useful to manage Russian fishery and may help to explain the reasons for the saury biomass decline. The latter is very important to take into account for development of the stock assessment models.

Correcting for size bias in electrofishing removal samples

Correcting for sampling biases and the misleading results they generate is an important but sometimes overlooked aspect of ecological research. Removal sampling, a common method used across survey protocols to estimate abundance, usually assumes constant capture probability across all individuals in a population. However, differences in body size, which are widespread in fish assemblages, are likely to violate this assumption. To correct for potential biases in removal sampling, we used hierarchical Bayesian models that estimate capture probability as a function of body size and common covariates associated with electrofishing of fish in streams. Using a fish-out data set (n= 78) from Alberta, Canada, that includes true abundances, we demonstrate that capture probability increased with body size and the model accurately estimated abundance for the majority of the reaches (83%). Furthermore, model performance improved when site width and conductivity, and their interactions with body size, were included as covariates in a stream fish dataset from Ontario (n= 56). The inclusion of site covariates in the model indicates that these size-based effects on capture probability can be dependent on the environment. The consequence of ignoring size-dependent catch probability is to underestimate biomass when the actual biomass is low and to overestimate biomass when it is high. This removal model allows us to estimate capture probability for multiple pass samples and consequently improves our ability to obtain more reliable abundance estimates. This modeling framework is presented using electrofishing, yet it is flexible enough to be used with a variety of other sampling techniques.

Modeling Mackerel Tuna (Euthynnus affinis) Habitat in Southern Coast of Java: Influence of Seasonal Upwelling and Negative IOD

We used fishery catch data from Cilacap Fishing Port and Copernicus data set in July 2016-December 2017 to investigate the impacts of Indian Ocean Dipole (IOD) on upwelling and mackerel tuna distribution in the southern coast of Java. This study implemented a Generalized Additive Model (GAM) for habitat prediction of mackerel tuna in the waters. The present study showed that the extreme negative IOD in 2016 caused a weaker southeasterly wind and even a reversal to the northwesterly wind, as seen off Sumatra in September 2016. The situation produced vertically mixed layer thickening and no upwelling during the southeast monsoon event 2016, consequently resulted in warmer temperature and fewer Chlorophyll-a (Chl-a) compared to the southeast monsoon event 2017. The mackerel tuna production significantly dropped in 2016 and rose in 2017, particularly during the upwelling event. The high Habitat Suitability Index (HSI) was found in southern Central Java in July 2017, expanded bigger in August 2017, and decreased in September 2017. During July and August 2016, the high HSI covered only a less area in the region and disappeared in September 2017. The high HSI indicates that the oceanographic factor is consistent with the catch probability of mackerel tuna.

Effect of hook type and hook setting method on deep‐hooking rates when bait fishing for trout in lentic waters

AbstractFishing with circle hooks along with prescribed hook‐setting methods can reduce deep‐hooking rates in some fisheries, but baited circle hooks have not been evaluated in stillwater trout fisheries. Deep‐hooking rates and catch probabilities were compared for cutthroat trout Oncorhynchus clarkii​ (Richardson), rainbow trout Oncorhyncus mykiss (Walbaum), cutthroat × rainbow trout hybrids, and brook trout Salvelinus fontinalis (MItchill) caught using three types of baited hooks (circle, intermediate circle and traditional J) and two hook‐setting methods (active and passive) in several lakes containing naturally reared trout. Hook type had a negligible effect on deep‐hooking rates, which averaged 20%. Actively setting the hook and fishing with a bobber both reduced deep‐hooking rates, regardless of hook type. Larger fish were deeply hooked more frequently regardless of hook type. Catch probabilities were higher when active rather than passive hook‐setting was used, particularly in combination with intermediate circle hooks. The present results, which indicate that circle hook use did not reduce deep hooking in lentic settings, contrast earlier studies done in lotic waters. Therefore, before proposing hook‐type regulations intended to reduce deep hooking, fisheries managers should consider waterbody type in addition to fishing methods, fish size and species.

Quantifying the Outfield Shift Using K-Means Clustering

Sports teams constantly search for a competitive advantage (e.g. bidding for free agents or scouting nontraditional markets). As popularized by Moneyball, we focus on advanced analytics in baseball. These sabermetrics are employed to provide objective information to management and coaches to support player management and in-game strategy decisions. Though widely used at the professional level, analytics use in college baseball is limited. Air Force Academy Baseball has been one win short of qualifying for the Mountain West tournament three straight years, resulting in the loss of potential income from media payouts and exposure for future recruiting efforts. Using a K-means clustering method for defensive shifting, we calculate an overall catch probability increase of 7.4% with a shifted outfield in a one-game case study. Based on our analysis, we provide evidence that Air Force Baseball can benefit from an outfield defensive shifting scheme that drives a competitive advantage and additional wins.

Catch-effort model used as a management tool in exploited populations: Wild boar as a case study

For sustainable management of exploited populations, it is required to have good knowledge on temporal trends in population density to adapt the harvest. In this regard, hunting statistics are often collected routinely by government agencies and associations. These data are used to assess demographic trends through the development of indices, which are in turn used to manage exploited populations in a sustainable way. However, these population indices depend on features of the hunting process (e.g. hunting effort, hunting conditions, probability of catch). In this study, we show how to use hunting logs to assess demographic trends in exploited populations while accounting for the components of the hunting process. In particular, we developed a catch-effort model to study how the hunting effort leads to mortality rate – hunting pressure – within a given habitat type and during a given period. We illustrated the usefulness of this approach using exploited wild boar (Sus scrofa) populations as a case study. We used a large hunting logs dataset to perform our study, with several hundreds of thousands hunting events for more than 10 years in two French departments in France, including information about the number of hunters, of wild boars culled and the date of the hunt. We showed that catchability is a key parameter to assess hunting pressure at a given time and place. This parameter varies both within the hunting season and between habitat types. Once this variation in catchability was accounted for, our catch-effort model allowed us to obtain estimates of relative densities of wild boar populations over the study period at the management unit scale. Thus, catch-effort models are powerful tools to assess population density and to understand the underlying hunting process. Our study offers straightforward and reproducible conceptual framework that can be applied routinely by wildlife managers on exploited populations and practitioners from hunting statistics logs.

Offshore snapper and shark distributions are predicted by prey and area of nearby estuarine environments in the Gulf of Mexico, USA

Seascape ecology has demonstrated that marine fishes are associated with multiscale habitat characteristics; however, most species distribution models focus on only a few predictors (e.g. depth, temperature), and this limits knowledge of essential fish habitat characteristics. Our objectives were to (1) determine habitat associations of offshore predatory marine fishes using a comprehensive suite of predictors, including area of nearby estuarine environments, (2) assess variable influence, and (3) model the spatial distribution of selected fishes in the families Carcharhinidae and Lutjanidae. We hypothesized that the concept of coastal outwelling would be evidenced by species associations with areas of nearby estuarine environments, and prey abundance would correlate with predator distributions. Species distribution models were developed for 2 snapper and 3 shark species in the northern Gulf of Mexico, USA. We used 34 multiscale predictors to evaluate how fish probability of presence or catch per unit effort (CPUE) were associated with oceanography, geography, substrate, area of nearby wetlands and estuaries, and prey abundance. Boosted regression trees, a machine-learning technique, modeled the most influential variables and predicted distributions. Model validation showed an overall accuracy of 79-86%, and CPUE models explained &gt;40% of model deviance. Oceanographic variables, particularly mixed layer depth, were most influential and most frequently selected. As hypothesized, predatory fish distributions were predicted by prey abundances, and shark distributions were predicted by area of nearby coastal wetlands and estuaries. Our findings suggest that spatial models can provide novel insights into prey associations and linkages of marine species with nearby wetlands and estuaries.

Distance and Visual Angle of Line-of-Sight of a Small Drone

Determining the distance of the line-of-sight (LOS) of a small drone is essential in both regulating drone operation and drone operator training considering public safety. A field experiment was conducted to determine the LOS distance and visual angle of a small drone. Human participants were requested to observe a drone in one of the predetermined locations in the air. They responded whether they could see and hear the drone using a five-point scale. It was found that auditory signals were insignificant in drone detection because most of the participants could not hear the drone while they could still see the drone in most of the test locations. Logistic regression analyses were conducted to predict the probability of catching the drone visually. Two models were built considering the “definitely yes” and “definitely or probably yes” criteria of visual detection. These models may be used to estimate the LOS distance and visual angle. Assuming a 50% probability of visual catching and the “definitely or probably yes” criterion, the distance and visual angle of the LOS for the Mavic Air drone without a protector were approximately 307 m and 0.065°, respectively.

Sharpening the Precision of Pest Management Decisions: Assessing Variability Inherent in Catch Number and Absolute Density Estimates Derived from Pheromone-Baited Traps Monitoring Insects Moving Randomly.

During a trapping study interval, each target insect is either caught or not caught. Therefore, the current analysis treats trapping as a binomial process. Data from a binomial calculator, along with computer simulations of random walkers, documented that the inherent variance associated with estimates of absolute population density generated by a single catch number in a pheromone-baited monitoring trap becomes very high when catch probability averaged across the trap's sampling area falls below 0.02, as is the case for most insect trapping systems operating in the open field. The imprecision associated with interpretations of single catch numbers renders many current pest management decisions risky and unsatisfactory. Here we reinforce how single-trap, multiple-release experiments can and should be used to measure catch probability, plume reach, and trap sampling area. When catch probability lies in the danger zone below 0.02, steps are suggested for how multiple traps might be deployed to raise composite catch probability to a level where estimates of absolute pest density become reliable. Heat transfer is offered as an appropriate conceptual model for the mechanics of trapping. A call is made for a radical rethinking in the designs of insect monitoring traps in light of their significant current deficits highlighted by this study.

Multisatellite-Based Feeding Habitat Suitability Modeling of Albacore Tuna in the Southern Atlantic Ocean

Decision strategies in fisheries management are often directed by the geographic distribution and habitat preferences of target species. This study used remote sensing data to identify the optimal feeding habitat of albacore tuna in the Southern Atlantic Ocean (SAO) using an empirical habitat suitability model applying longline fisheries data during 2009–2015. An arithmetic mean model with sea surface temperature (SST) and sea surface chlorophyll-a concentration (SSC) was determined to be suitable for defining the albacore habitat in the SAO. The optimal ranges of SST and SSC for the habitat were approximately 16.5 °C–19.5 °C and 0.11–0.33 mg/m3, respectively. The study revealed a considerable positive trend between the suitable habitat area and standardized catch per unit effort (r = 0.97; p &lt; 0.05); due to the west-to-east and northward development of the suitable habitat, albacore schools moved to the northeast of the SAO, thus increasing catch probability in April to August in that region. Overall, the frontal structure of SST and SSC plays an essential role in the formation of potential albacore habitats in the SAO. Our findings could contribute to the establishment of regional ecosystem-based fisheries management in the SAO.

Expected hypothetical completion probability

AbstractUsing high-resolution player tracking data made available by the National Football League (NFL) for their 2019 Big Data Bowl competition, we introduce the Expected Hypothetical Completion Probability (EHCP), a objective framework for evaluating plays. At the heart of EHCP is the question “on a given passing play, did the quarterback throw the pass to the receiver who was most likely to catch it?” To answer this question, we first built a Bayesian non-parametric catch probability model that automatically accounts for complex interactions between inputs like the receiver’s speed and distances to the ball and nearest defender. While building such a model is, in principle, straightforward, using it to reason about a hypothetical pass is challenging because many of the model inputs corresponding to a hypothetical are necessarily unobserved. To wit, it is impossible to observe how close an un-targeted receiver would be to his nearest defender had the pass been thrown to him instead of the receiver who was actually targeted. To overcome this fundamental difficulty, we propose imputing the unobservable inputs and averaging our model predictions across these imputations to derive EHCP. In this way, EHCP can track how the completion probability evolves for each receiver over the course of a play in a way that accounts for the uncertainty about missing inputs.

EVALUATION OF THE WORKING RADIUS OF PHEROMONE PREPARATIONS FOR THE BROWN MARMORATED STINK BUG HALYOMORPHA HALYS

This article presents the trial results aimed to evaluate the working radius of the pheromone formulation for the adults of the brown marmorated stink bug Halyomorpha halys under the field conditions in 2019. The cumulative plastic traps of “pyramid” type with a synthetic pheromone preparation manufactured by a Russian producer were used. The chemical composition of the preparation included a mixture of two stereoisomers: (3S, 6S, 7R, 10S)-10.11-epoxy-1-bisabolene-3-ol and (3R,6S,7R,10S)-10.11-epoxy-1-bisabolene-3-ol, and the substance – synergist-methyl-(E,E,Z)-2,4,6-decatrienoate. Previously, this composition had been proved to be effective in catching nymphs and adults of the brown marmorated stink bug during the field trials at the dosage of 12 mg/trap. The results of the 2019 trials showed that when labeled bugs were released in fruit orchards from a distance of 10 m (corresponding to an area of 0.03 ha), the probability of adults’ catching in traps was 9 %. The bug catching probability from the distance of 20 m (an area of 0.1 ha) was lower, reaching only 3 %. The total number of bugs caught in the trap in the hazelnut plantations was half of that in fruit orchards, and the probability of catching of labeled adults released from 10 m (0.03 ha) was 2.5 %.

Potential distribution and population trends of the smalltail shark Carcharhinus porosus inferred from species distribution models and historical catch data

Abstract Updated distribution ranges are crucial for conservation status assessments. Comprehensive analyses combining published literature and available data on historical catches and species distribution models (SDMs) are effective tools that could improve the prediction of more realistic scenarios for some species, especially those with limited information available and facing multiple threats. The present study aimed at generating an updated distribution for the smalltail shark Carcharhinus porosus, one of the most threatened and understudied shark species of the western Atlantic Ocean. Estimates of the key areas for this species conservation based on the SDMs, and trends in catch probabilities throughout its distribution range are provided. Four algorithms (BIOCLIM, Domain, Mahalanobis, and Maximum Entropy) were used to model the distribution of C. porosus and calculate its habitat suitability based on marine environmental variables. To assess historical catch probability trends, we built a generalized linear model from published and grey literature data. This analysis was used to estimate catch probability as an indication of population trends. SDMs suggest that the northern coast of South America (NCSA) harbours the most suitable habitats for C. porosus in the world, which was expected given its historically high catch rate in this region. In addition, there was a continuously declining catch probability trend starting in the 1970s. However, the decline was smaller for the NCSA as compared with the Gulf of Mexico and the eastern South America coast. Results indicate that the NCSA should be considered the currently most important area in the world for this species conservation. Furthermore, the lack of data throughout Central and South American marine regions hampers the evaluation of extinction risk throughout its updated distribution. Thus, research in these areas is urgently required for a more comprehensive conservation status assessment.