Evaluating coyote management strategies using a spatially explicit, individual-based, socially structured population model

Extinction risks and mitigation for a megaherbivore, the giraffe, in a human-influenced landscape under climate change

Megaherbivores play "outsized" roles in ecosystem functioning but are vulnerable to human impacts such as overhunting, land-use changes, and climate extremes. However, such impacts-and combinations of these impacts-on population dynamics are rarely examined using empirical data. To guide effective conservation actions under increasing global-change pressures, we developed a socially structured individual-based model (IBM) using long-term demographic data from female giraffes (Giraffa camelopardalis) in a human-influenced landscape in northern Tanzania, the Tarangire Ecosystem. This unfenced system includes savanna habitats with a wide gradient of anthropogenic pressures, from national parks, a wildlife ranch and community conservation areas, to unprotected village lands. We then simulated and projected over 50 years how realistic environmental and land-use management changes might affect this metapopulation of female giraffes. Scenarios included: (1) anthropogenic land-use changes including roads and agricultural/urban expansion; (2) reduction or improvement in wildlife law enforcement measures; (3) changes in populations of natural predators and migratory alternative prey; and (4) increases in rainfall as predicted for East Africa. The factor causing the greatest risk of rapid declines in female giraffe abundance in our simulations was a reduction in law enforcement leading to more poaching. Other threats decreased abundances of giraffes, but improving law enforcement in both of the study area's protected areas mitigated these impacts: a 0.01 increase in giraffe survival probability from improved law enforcement mitigated a 25% rise in heavy rainfall events by increasing abundance 19%, and mitigated the expansion of towns and blockage of dispersal movements by increasing abundance 22%. Our IBM enabled us to further quantify fine-scale abundance changes among female giraffe social communities, revealing potential source-sink interactions within the metapopulation. This flexible methodology can be adapted to test additional ecological questions in this landscape, or to model populations of giraffes or other species in different ecosystems.

QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm

One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.

New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm

This paper presents a new approach for coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controller. For this purpose, the design problem is considered as an optimization problem whose decision variables are the controllers’ parameters. Due to nonlinearities of large, interconnected power systems, methods capable of handling any nonlinearity of power networks are mostly preferable. In this regard, a nonlinear time domain based objective function is used. Then, the coyote optimization algorithm (COA) is employed for solving this optimization problem. In order to ensure the robustness and performance of the proposed controller (COA-PSS&SVC), the objective function is evaluated for various extreme loading conditions and system configurations. To show the contribution of the coordinated controllers on the improvement of the system stability, PSSs and SVC are optimally designed in individual and coordinated manners. Moreover, the effectiveness of the COA-PSS&SVC is assessed through comparison with other controllers. Nonlinear time domain simulation shows the superiority of the proposed controller and its ability in providing efficient damping of electromechanical oscillations.

An introduction to agent-based models as an accessible surrogate to field-based research and teaching

There are many barriers to fieldwork including cost, time, and physical ability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork in the natural sciences. Travel restrictions, concerns about our carbon footprint, and the global lockdown have extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially among graduate students and early-career researchers. In this paper, we discuss agent-based modeling as an open-source, accessible, and inclusive resource to substitute for lost fieldwork during COVID-19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent-Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent-based models can be applied broadly across life science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the agent-based modeling community in ecology and evolutionary research. Finally, we discuss the challenges facing agent-based modeling and discuss how quantitative ecology can work in tandem with traditional field ecology to improve both methods.

Coal Classification Method Based on Improved Local Receptive Field-Based Extreme Learning Machine Algorithm and Visible-Infrared Spectroscopy

In the process of using coal, if the type of coal cannot be accurately determined, it will have a significant impact on production efficiency, environmental pollution, and economic loss. At present, the traditional classification method of coal mainly relies on technician's experience. This requires a lot of manpower and time, and it is difficult to automate. This paper mainly studies the application of visible-infrared spectroscopy and machine learning methods in coal mine identification and analysis to provide guidance for coal mining and production. This paper explores a fast and high-precision method for coal identification. In this paper, for the characteristics of high dimensionality, strong correlation, and large redundancy of spectral data, the local receptive field (LRF) is used to extract the advanced features of spectral data, which is combined with the extreme learning machine (ELM). We improved the coyote optimization algorithm (COA). The improved coyote optimization algorithm (I-COA) and local receptive field-based extreme learning machine (ELM-LRF) are used to optimize the structure and training parameters of the extreme learning machine network. The experimental results show that the coal classification model based on the network and visible-infrared spectroscopy can effectively identify the coal types through the spectral data. Compared with convolutional neural networks (CNN algorithm) and principal component analysis (PCA algorithm), LRF can extract the spectral characteristics of coal more effectively.

Spatial processes decouple management from objectives in a heterogeneous landscape: predator control as a case study

Predator control is often implemented with the intent of disrupting top-down regulation in sensitive prey populations. However, ambiguity surrounding the efficacy of predator management, as well as the strength of top-down effects of predators in general, is often exacerbated by the spatially implicit analytical approaches used in assessing data with explicit spatial structure. Here, we highlight the importance of considering spatial context in the case of a predator control study in south-central Utah. We assessed the spatial match between aerial removal risk in coyotes (Canis latrans) and mule deer (Odocoileus hemionus) resource selection during parturition using a spatially explicit, multi-level Bayesian model. With our model, we were able to evaluate spatial congruence between management action (i.e., coyote removal) and objective (i.e., parturient deer site selection) at two distinct scales: the level of the management unit and the individual coyote removal. In the case of the former, our results indicated substantial spatial heterogeneity in expected congruence between removal risk and parturient deer site selection across large areas, and is a reflection of logistical constraints acting on the management strategy and differences in space use between the two species. At the level of the individual removal, we demonstrated that the potential management benefits of a removed coyote were highly variable across all individuals removed and in many cases, spatially distinct from parturient deer resource selection. Our methods and results provide a means of evaluating where we might anticipate an impact of predator control, while emphasizing the need to weight individual removals based on spatial proximity to management objectives in any assessment of large-scale predator control. Although we highlight the importance of spatial context in assessments of predator control strategy, we believe our methods are readily generalizable in any management or large-scale experimental framework where spatial context is likely an important driver of outcomes.

Assessing Animal Welfare Impacts in the Management of European Rabbits (Oryctolagus cuniculus), European Moles (Talpa europaea) and Carrion Crows (Corvus corone)

Human-wildlife conflict is a global issue. Attempts to manage this conflict impact upon wild animal welfare, an issue receiving little attention until relatively recently. Where human activities harm animal welfare these effects should be minimised where possible. However, little is known about the welfare impacts of different wildlife management interventions, and opinions on impacts vary widely. Welfare impacts therefore need to be assessed objectively. Our objectives were to: 1) establish whether an existing welfare assessment model could differentiate and rank the impacts of different wildlife management interventions (for decision-making purposes); 2) identify and evaluate any additional benefits of making formal welfare assessments; and 3) illustrate issues raised by application of the model. We applied the welfare assessment model to interventions commonly used with rabbits (Oryctolagus cuniculus), moles (Talpa europaea) and crows (Corvus corone) in the UK. The model ranked interventions for rabbits (least impact first: fencing, head shot, chest shot) and crows (shooting, scaring, live trapping with cervical dislocation). For moles, managing molehills and tunnels scored least impact. Both spring trapping, and live trapping followed by translocation, scored greater impacts, but these could not be compared directly as they scored on different axes of the model. Some rankings appeared counter-intuitive, highlighting the need for objective formal welfare assessments. As well as ranking the humaneness of interventions, the model highlighted future research needs and how Standard Operating Procedures might be improved. The model is a milestone in assessing wildlife management welfare impacts, but our research revealed some limitations of the model and we discuss likely challenges in resolving these. In future, the model might be developed to improve its utility, e.g. by refining the time-scales. It might also be used to reach consensus among stakeholders about relative welfare impacts or to identify ways of improving wildlife management practice in the field.

Dead or alive? Comparing costs and benefits of lethal and non-lethal human–wildlife conflict mitigation on livestock farms

Livestock depredation has implications for conservation and agronomy; it can be costly for farmers and can prompt retaliatory killing of carnivores. Lethal control measures are readily available and are reportedly perceived to be cheaper, more practical and more effective than non-lethal methods. However, the costs and efficacy of lethal vs non-lethal approaches have rarely been compared formally. We conducted a 3-year study on 11 South African livestock farms, examining costs and benefits of lethal and non-lethal conflict mitigation methods. Farmers used existing lethal control in the first year and switched to guardian animals (dogs Canis familiaris and alpacas Lama pacos) or livestock protection collars for the following 2 years. During the first year the mean cost of livestock protection was USD 3.30 per head of stock and the mean cost of depredation was USD 20.11 per head of stock. In the first year of non-lethal control the combined implementation and running costs were similar to those of lethal control (USD 3.08 per head). However, the mean cost of depredation decreased by 69.3%, to USD 6.52 per head. In the second year of non-lethal control the running costs (USD 0.43 per head) were significantly lower than in previous years and depredation costs decreased further, to USD 5.49 per head. Our results suggest that non-lethal methods of human–wildlife conflict mitigation can reduce depredation and can be economically advantageous compared to lethal methods of predator control.

Intense harvesting of eastern wolves facilitated hybridization with coyotes

Despite ethical arguments against lethal control of wildlife populations, culling is routinely used for the management of predators, invasive or pest species, and infectious diseases. Here, we demonstrate that culling of wildlife can have unforeseen impacts that can be detrimental to future conservation efforts. Specifically, we analyzed genetic data from eastern wolves (Canis lycaon) sampled in Algonquin Provincial Park (APP), Ontario, Canada from 1964 to 2007. Research culls in 1964 and 1965 killed the majority of wolves within a study region of APP, accounting for approximately 36% of the park's wolf population at a time when coyotes were colonizing the region. The culls were followed by a significant decrease in an eastern wolf mitochondrial DNA (mtDNA) haplotype (C1) in the Park's wolf population, as well as an increase in coyote mitochondrial and nuclear DNA. The introgression of nuclear DNA from coyotes, however, appears to have been curtailed by legislation that extended wolf protection outside park boundaries in 2001, although eastern wolf mtDNA haplotype C1 continued to decline and is now rare within the park population. We conclude that the wolf culls transformed the genetic composition of this unique eastern wolf population by facilitating coyote introgression. These results demonstrate that intense localized harvest of a seemingly abundant species can lead to unexpected hybridization events that encumber future conservation efforts. Ultimately, researchers need to contemplate not only the ethics of research methods, but also that future implications may be obscured by gaps in our current scientific understanding.