A tree-based approach to identify indispensable foods in minimum-cost food baskets.

Introduction: Optimization techniques, such as linear programming, can be used to estimate the minimum cost of a nutrient-adequate food basket, to determine if individuals or households can afford nutritious diets. These cost estimates typically account for seasonal fluctuations but often overlook significant disruptions in the availability of affordable nutritious food that can severely impact food and nutrition security. Methods: This paper proposes a tree-based method, the binary search tree, to assess the resilience of the cost estimate of the minimum-cost food basket. In particular, this method aims to identify indispensable foods in these baskets - those whose unavailability would lead to a substantial cost increase. The binary search tree operates by iteratively excluding essential food items while ensuring the construction of minimum-cost nutritious baskets. It considers all relevant combinations of foods up to a specified size and avoids unnecessary optimizations, thereby saving computation time. We describe how the resulting tree can be evaluated and condensed to capture only the necessary information for decision makers. The construction and evaluation of the binary search tree are independent of the (dietary) restrictions or type of optimization model (i.e., linear, non-linear or integer) included. Results: In general, the binary search tree can identify all (combinations of) foods whose exclusion leads to a significant cost increase of a nutritious food basket. Furthermore, it can detect possible substitute effects between foods and identify key limiting nutrients. A case study is presented in which the binary search tree is applied to data from Ebonyi, Nigeria, modeled using linear programming. We report all combinations of up to five foods that, when unavailable, can impact food and nutrition security in Ebonyi. Conclusion: The BST can provide insights into local food and nutrition security when facing drastic disruptions in access to nutritious foods by identifying indispensable foods. Its results can be used to inform and design interventions in the context of humanitarian operations.

Strategy investments in zero-sum games.

We propose an extension of two-player zero-sum games, where one player may select available actions for themselves and the opponent, subject to a budget constraint. We present a mixed-integer linear programming (MILP) formulation for the problem, provide analytical results regarding its solution, and discuss applications in the security and advertising domains. Our computational experiments demonstrate that heuristic approaches, on average, yield suboptimal solutions with at least a 20% relative gap with those obtained by the MILP formulation.

Robust Optimal Metabolic Factories.

Perhaps the most fundamental model in synthetic and systems biology for inferring pathways in metabolic reaction networks is a metabolic factory: a system of reactions that starts from a set of source compounds and produces a set of target molecules, while conserving or not depleting intermediate metabolites. Finding a shortest factory-that minimizes a sum of real-valued weights on its reactions to infer the most likely pathway-is NP-complete. The current state-of-the-art for shortest factories solves a mixed-integer linear program with a major drawback: it requires the user to set a critical parameter, where too large a value can make optimal solutions infeasible, while too small a value can yield degenerate solutions due to numerical error. We present the first robust algorithm for optimal factories that is both parameter-free (relieving the user from determining a parameter setting) and degeneracy-free (guaranteeing it finds an optimal nondegenerate solution). We also give for the first time a complete characterization of the graph-theoretic structure of shortest factories, that reveals an important class of degenerate solutions which was overlooked and potentially output by the prior state-of-the-art. We show degeneracy is precisely due to invalid stoichiometries in reactions, and provide an efficient algorithm for identifying all such misannotations in a metabolic network. In addition we settle the relationship between the two established pathway models of hyperpaths and factories by proving hyperpaths actually comprise a subclass of factories. Comprehensive experiments over all instances from the standard metabolic reaction databases in the literature demonstrate our parameter-free exact algorithm is fast in practice, quickly finding optimal factories in large real-world networks containing thousands of reactions. A preliminary implementation of our robust algorithm for shortest factories in a new tool called Freeia is available free for research use at http://freeia.cs.arizona.edu.

Food-based dietary guidelines for optimizing calcium intakes for reproductive-aged women in Ethiopia using local foods.

Increasing dietary calcium intakes of Ethiopian women of reproductive age (WRA) is a public health priority for reducing pre-eclampsia in pregnancy. Using linear programming, we determined whether locally available foods consumed by WRA in nine regions (urban and rural) and two administrative cities of Ethiopia could provide 1000 mg/day of dietary calcium, and we identified food-based recommendations (FBRs) to improve dietary calcium adequacy in each region. Results showed that diets providing 1000 mg/day of calcium were feasible in eight regions (40%) of the target populations examined. It would, however, require marked changes for most populations (90%), increasing the number of servings per week of several food groups to levels close to those of high consumers in each population. The selected calcium-specific FBRs integrate well into the 2022 Ethiopian Dietary Guidelines, requiring additional messages to consume green leafy vegetables, milk, root crops, or teff (Eragrostis tef) or to consume a higher number of servings of vegetables than currently recommended, depending on the population. In conclusion, these analyses show that a food-based approach can be used to achieve dietary calcium adequacy among WRA in 40% of the populations examined. For the other populations, food-based interventions alone may be inadequate and other interventions are likely needed.

HASCH - A high-throughput amplicon-based SNP-platform for medicinal cannabis and industrial hemp genotyping applications.

BACKGROUND: Cannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa. RESULTS: The High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings. CONCLUSION: HASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding.

Diet affordability: a key dimension in the assessment of sustainable food systems and healthy diets.

A promulgated global shift toward a plant-based diet is largely in response to a perceived negative environmental impact of animal food production, but the nutritional adequacy and economic implications of plant-sourced sustainable healthy dietary patterns need to be considered. This paper reviews recent modeling studies using Linear Programming to determine the respective roles of animal- and plant-sourced foods in developing a least-cost diet in the United States and New Zealand. In both economies, least-cost diets were found to include animal-based foods, such as milk, eggs, fish, and seafood, to meet the energy and nutrient requirements of healthy adults at the lowest retail cost. To model a solely plant-based least-cost diet, the prevailing costs of all animal-sourced foods had to be increased by 1.1 to 11.5 times their original retail prices. This led to the inclusion of fortified plant-based foods, such as fortified soymilk, and a plant-based diet that was considerably (34-45%) more costly. The first-limiting essential nutrients were mostly the vitamins and minerals, with special focus on pantothenic acid, zinc, and vitamin B-12, when transitioning from an animal- and plant-containing least-cost diet to a plant-only based least-cost diet. Modeled least-cost diets based on contemporary food costs include animal-sourced foods, at least for developed high-income US and NZ food economies, and potentially for developing low- and middle-income countries, such as Indonesia. Modeling of least-cost diets that consist exclusively of plant-based foods is feasible, but at a higher daily diet cost, and these diets are often close to limiting for several key nutrients. Diet affordability, as a key dimension of sustainable healthy diets, and the respective economic roles of animal- and plant-sourced foods need to be considered.

An Optimization Tool to Formulate Diets within a Supplementary Nutrition Program for Children.

Background: In large supplementary feeding programs for children, it is challenging to create and sustain contextual, acceptable, nutritionally complete, and diverse supplemental foods. For example, the Indian Supplementary Nutrition Program (SNP) supplements the dietary intake of children, pregnant and lactating women, and severely acutely malnourished (SAM) children by offering dry take home rations (THRs) or hot cooked meals (HCMs) across India, but an optimization tool is necessary to create local contextual recipes for acceptable and nutritionally adequate products. Objectives: This study aimed to create a linear programming (LP) model to optimize diverse food provisions for a SNP to meet its program guidelines, using locally available foods, within budgetary allocations. Methods: A LP algorithm with appropriate constraints was used to generate an optimal THR based on raw foods, or an optimal weekly HCM menu comprised of a lunch meal with mid-morning snacks, based on user choices of foods and recipes. The database of foods used was created by a prospective survey conducted across all states of India for this purpose, such that the recipe and food optimization was diverse and specific to the guidelines for each beneficiary group. Results: An interactive web-based app, which can optimize feeding programs at any population level, was developed for use by program implementers and is hosted at https://www.datatools.sjri.res.in/SNP/. In the Indian example analyzed here, the recommended optimized diets met the guidelines for diversified and nutritionally complete SNP provision but at a cost that was almost 25% higher than the present Indian budget allocation. Conclusions: The optimization model developed demonstrates that contextual SNP diets can be created to meet macronutrient and most essential micronutrient needs of large-scale feeding programs, but appropriate diversification entails additional costs.

Route Planning for Intra-Hospital Patient Transportation Using Metaheuristics and Mixed Integer Linear Programming.

Healthcare processes are complex and involve uncertainties to influence the service quality and health of patients. Patient transportation takes place between the hospitals or between the departments within the hospital (i.e., Inter- or Intra-Hospital Transportation respectively). The focus of our paper is route planning for transporting patients within the hospital. The route planning task is complex due to multiple factors such as regulations, fairness considerations (i.e., balanced workload amongst transporters), and other dynamic factors (i.e., transport delays, wait times). Transporters perform the physical transportation of patients within the hospital. In principle, each job allocation respects the transition time between the subsequent jobs. The primary objective was to determine the feasible number of transporters, and then generate the route plan for all determined transporters by distributing all transport jobs (i.e., from retrospective data) within each shift. Secondary objectives are to minimize the sum of total travel time and sum of total idle time of all transporters and minimize the deviations in total travel time amongst transporters. Our method used multi-staged Local Search Metaheuristics to attain the primary objective. Metaheuristics incorporate Mixed Integer Linear Programming to allocate fairly the transport jobs by formulating optimization constraints with bounds for satisfying the secondary objectives. The obtained results using formulated optimization constraints represent better efficacy in multi-objective route planning of Intra-Hospital Transportation of patients.

Immunity to Increasing Condition Numbers of Linear Superiorization versus Linear Programming.

Given a family of linear constraints and a linear objective function one can consider whether to apply a Linear Programming (LP) algorithm or use a Linear Superiorization (LinSup) algorithm on this data. In the LP methodology one aims at finding a point that fulfills the constraints and has the minimal value of the objective function over these constraints. The Linear Superiorization approach considers the same data as linear programming problems but instead of attempting to solve those with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward feasible points with reduced (not necessarily minimal) objective function values. Previous studies compared LP and LinSup in terms of their respective outputs and the resources they use. In this paper we investigate these two approaches in terms of their sensitivity to condition numbers of the system of linear constraints. Condition numbers are a measure for the impact of deviations in the input data on the output of a problem and, in particular, they describe the factor of error propagation when given wrong or erroneous data. Therefore, the ability of LP and LinSup to cope with increased condition numbers, thus with ill-posed problems, is an important matter to consider which was not studied until now. We investigate experimentally the advantages and disadvantages of both LP and LinSup on examplary problems of linear programming with multiple condition numbers and different problem dimensions.

Applying mixed-integer linear programming to the non-coplanar beam angle optimization of intensity-modulated radiotherapy for liver cancer.

Background: Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach. The goal of this study is to develop a mixed-integer linear programming (MILP) approach for the beam angle optimization (BAO) of non-coplanar IMRT for liver cancer. Methods: MILP models for the BAO of both coplanar and non-coplanar IMRT treatment plans were developed. The beam angles of the IMRT plans were first selected by the MILP model built using mathematical optimization software. Next, the IMRT plans with the selected beam angles was created in a commercial treatment planning system. Finally, the fluence map and dose distribution of the IMRT plans were generated under pre-defined dose-volume constraints. The IMRT plans of 10 liver cancer patients previously treated at our institute were used to assessed the proposed MILP models. For each patient, both coplanar and non-coplanar IMRT plans with beam angles optimized by the MILP models were compared with the IMRT plan clinically approved by physicians. Results: The MILP model-guided IMRT plans showed reduced doses for most of the organs at risk (OARs). Compared with the IMRT plans clinically approved by physicians, the doses for the spinal cord (28.5 vs. 36.1, P=0.001<0.05) and liver (27.6 vs. 29.1, P=0.005<0.05) decreased significantly in the IMRT plans with non-coplanar beams selected by the MILP models. Conclusions: The MILP model is an effective tool for the BAO in coplanar and non-coplanar IMRT treatment planning. It facilitates the automation of IMRT treatment planning for current high-precision radiotherapy.

RIBAP: a comprehensive bacterial core genome annotation pipeline for pangenome calculation beyond the species level.

Microbial pangenome analysis identifies present or absent genes in prokaryotic genomes. However, current tools are limited when analyzing species with higher sequence diversity or higher taxonomic orders such as genera or families. The Roary ILP Bacterial core Annotation Pipeline (RIBAP) uses an integer linear programming approach to refine gene clusters predicted by Roary for identifying core genes. RIBAP successfully handles the complexity and diversity of Chlamydia, Klebsiella, Brucella, and Enterococcus genomes, outperforming other established and recent pangenome tools for identifying all-encompassing core genes at the genus level. RIBAP is a freely available Nextflow pipeline at github.com/hoelzer-lab/ribap and zenodo.org/doi/10.5281/zenodo.10890871.

Urban drone stations siting optimization based on hybrid algorithm of MILP and machine learning.

Urban environments, characterized by high population density and intricate infrastructures, are susceptible to a range of emergencies such as fires and traffic accidents. Optimal placement and distribution of fire stations and ambulance centers are thus imperative for safeguarding both life and property. An investigation into the distribution inefficiencies of emergency service facilities in selected districts of Chengdu reveals that imbalanced distribution of these facilities results in suboptimal response times during critical incidents. To address this challenge, a two-stage clustering method, incorporating X-means and K-means algorithms, is employed to identify optimal number and locations for Unmanned Aerial Vehicle (UAV) fire stations and drone ambulance centers. A Mixed-Integer Linear Programming (MILP) model is subsequently constructed and solved using the Gurobi optimization platform. Bayesian optimization-a machine learning technique-is exploited to elucidate the interplay between response speed and service capacity of these UAV-based emergency service stations under an optimized layout. Results affirm that integration of MILP and machine learning provides a robust framework for solving complex problems related to the siting and allocation of emergency service facilities. The proposed hybrid algorithm demonstrates substantial potential for enhancing emergency preparedness and response in urban settings.

Optimising design and operation of sewage source heat pump: techno-economic and environmental assessment.

Heat pump can be used to recover abundant thermal energy contained in the discharge of municipal wastewater treatment plants. While there are some design standards for common heat pump systems, the design of a sewage source heat pump (SSHP) system is still often based on a fixed heat load and neglects the interdependencies between the equipment sizing and operating parameters. To address the issue that previous design methods have not balanced investment and operational costs well from a global optimisation perspective, this work formulates the simultaneous optimisation of SSHP design and operation as a non-linear programming problem. The proposed model features the consideration of multiple working conditions caused by the impact of ambient temperature variation on the heat load of the SSHP system. The feasibility and potential benefits of the optimised SSHP system are also evaluated by incorporating techno-economic performances and environmental impact analyses into the mathematical framework. A case study is carried out to demonstrate the effectiveness of the proposed methodology. The results show that the total annual cost of the optimally designed and operated SSHP in Harbin could be 9% lower than in Beijing and 39% lower than in Shanghai, suggesting that constructing and running the SSHP system in severe cold regions with great heating demands might be more economical than in less cold regions. The CO2, SO2, and NOx emissions of the SSHP could be approximately 50% less than that of coal-fired boiler heating, and 80% less than that of direct electric heating with coal-fired electricity.

Beyond profit margins: Orchestrating social, economic, and environmental sustainability within the Norwegian Salmon Food Supply Chain.

Food Supply Chains (FSCs) have become increasingly complex with the average distance between producers and consumers rising considerably in the past two decades. Consequently, FSCs are a major source of carbon emissions and reducing transportation costs a major challenge for businesses. To address this, we present a mathematical model to promote the three core dimensions of sustainability (economic, environmental, and social), based on the Mixed-Integer Linear Programming (MILP) method. The model addresses the environmental dimension by intending to decrease the carbon emissions of different transport modes involved in the logistics network. Several supply chain network characteristics are incorporated and evaluated, with a consideration of social sustainability (job generation from operating various facilities). The mathematical model's robustness is demonstrated by testing and deploying it to a variety of problem instances. A real-life case study (Norwegian salmon supply chain) helps to comprehend the model's applicability. To understand the importance of optimizing food supply networks holistically, the paper investigates the impact of multiple supply chain permutations on total cost, demand fluctuations and carbon emissions. To address fluctuations in retail demand, we undertook sensitivity analysis for variations in demand, enabling the proposed model to revamp Norway's salmon supply chain network. Subsequently, the results are thoroughly examined to identify managerial implications.

Optimizing spot intensity with lower bound constraints for IMPT: Exposing shortcomings and introducing an enhanced strategy.

BACKGROUND: Intensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and efficiency. PURPOSE: This study introduces an innovative Two-Stage Mixed Integer Linear Programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing strategies. METHODS: Our approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three-phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L∞ strategies. The method's efficacy was tested in eight study cases, using Dose-Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established methods. RESULTS: The proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high-quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB constraints. CONCLUSIONS: Our Two-Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for improved patient outcomes through more precise and efficient radiation therapy plans.

Effects of different resistance training programmings on the relative strength, body composition, anthropometric variables, and metabolic risk of elderly women.

The purpose of this study was to analyze the effect of two different programming models of resistance training (RT) on metabolic risk, anthropometric variables, and relative strength in elderly women. The research was a prospective and comparative longitudinal study with a non-probabilistic random sample. Twenty-two elderly women (64 ± 3 years) was divided into two experimental groups being the Linear programming (LP, n = 12) and Daily undulatory programming (DUP, n = 10), with 3 sessions/week for 12 weeks. Submaximal strength (10RM) was evaluated in the horizontal leg press (HL), pulldown (PD), leg curl (LC), vertical bench press (BP), and leg extension (LE). Anthropometric variables, food intake (R24h) and submaximal strength (10RM) was analyzed. Participants were initially classified as overweight or obese evaluated by body mass index (BMI) and percentual of fat mass (%FM) and with moderate to high risk to develop metabolic diseases evaluated by hip-waist ratio (HWR), waist-height ratio (WHR) and waist circumference (WC). There is no change for metabolic risk and anthropometric variables after the intervention period. There was a significant improvement for relative strength accessed by 10RM and body weight (10RM/BW), and lean body mass (10RM/LBM) (p < 0.05), with large or medium effect size for most of variables after 12 weeks of RT. As a conclusion, both programmings increased relative strength after 12 weeks of RT with attenuated change in body composition and metabolic risk in elderly women in both programming groups and all those strategies can be used in elderly women to improve strength.

Integrated operations planning model for the automotive wiring industry.

An integrated operations planning model for automotive wiring companies is studied to improve synchronization between production activities and inventory flows. These combined factors are growing in significance as they drive the need to take proactive steps in manufacturing and distributing wiring materials within the supply chain. This involves anticipating the requirements of different automotive manufacturers and thereby guaranteeing a consistent, uninterrupted, and punctual provision of raw wiring materials. This support is vital for sustaining the ongoing manufacturing operations in the automotive sector. For this push flow system, the proposed operational model is based on integer linear programming, considering capacity and bill of materials constraints to determine production quantities, inventory levels, and machine sizing. Real-life data from the automotive wiring industry validates the effectiveness of coordinated production and inventory activities, resulting in significant lead time reductions of up to 60 %. These findings provide compelling reasons for automotive wiring partners to engage in joint operations planning.

Combination Chemotherapy Optimization with Discrete Dosing.

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer.

Minimizing Computation and Communication Costs of Two-Sided Secure Distributed Matrix Multiplication under Arbitrary Collusion Pattern.

This paper studies the problem of minimizing the total cost, including computation cost and communication cost, in the system of two-sided secure distributed matrix multiplication (SDMM) under an arbitrary collusion pattern. In order to perform SDMM, the two input matrices are split into some blocks, blocks of random matrices are appended to protect the security of the two input matrices, and encoded copies of the blocks are distributed to all computing nodes for matrix multiplication calculation. Our aim is to minimize the total cost, overall matrix splitting factors, number of appended random matrices, and distribution vector, while satisfying the security constraint of the two input matrices, the decodability constraint of the desired result of the multiplication, the storage capacity of the computing nodes, and the delay constraint. First, a strategy of appending zeros to the input matrices is proposed to overcome the divisibility problem of matrix splitting. Next, the optimization problem is divided into two subproblems with the aid of alternating optimization (AO), where a feasible solution can be obtained. In addition, some necessary conditions for the problem to be feasible are provided. Simulation results demonstrate the superiority of our proposed scheme compared to the scheme without appending zeros and the scheme with no alternating optimization.

Managing Forests for Biodiversity Conservation and Climate Change Mitigation.

We include biodiversity impacts in forest management decision making by incorporating the countryside species area relationship model into the partial equilibrium model GLOBIOM-Forest. We tested three forest management intensities (low, medium, and high) and limited biodiversity loss via an additional constraint on regional species loss. We analyzed two scenarios for climate change mitigation. RCP1.9, the higher mitigation scenario, has more biodiversity loss than the reference RCP7.0, suggesting a trade-off between climate change mitigation, with increased bioenergy use, and biodiversity conservation in forests. This trade-off can be alleviated with biodiversity-conscious forest management by (1) shifting biomass production destined to bioenergy from forests to energy crops, (2) increasing areas under unmanaged secondary forest, (3) reducing forest management intensity, and (4) reallocating biomass production between and within regions. With these mechanisms, it is possible to reduce potential global biodiversity loss by 10% with minor changes in economic outcomes. The global aggregated reduction in biodiversity impacts does not imply that biodiversity impacts are reduced in each ecoregion. We exemplify how to connect an ecologic and an economic model to identify trade-offs, challenges, and possibilities for improved decisions. We acknowledge the limitations of this approach, especially of measuring and projecting biodiversity loss.