Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems.

Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm's global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications.

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cutting-edge neural network that effectively captures complex features and spatial relationships within images. To improve the CapsNet's performance, a Modified Version of Osprey Optimization Algorithm (MOA) has been utilized. Thesuggested approach has been tested on the ALL-IDB database, a widely recognized dataset for leukemia image classification. Comparative analysis with various machine learning techniques, including Combined combine MobilenetV2 and ResNet18 (MBV2/Res) network, Depth-wise convolution model, a hybrid model that combines a genetic algorithm with ResNet-50V2 (ResNet/GA), and SVM/JAYA demonstrated the superiority of our method in different terms. As a result, the proposed method is a robust and powerful tool for diagnosing leukemia from medical images.

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks.

One of the critical technologies to ensure cyberspace security is network traffic anomaly detection, which detects malicious attacks by analyzing and identifying network traffic behavior. The rapid development of the network has led to explosive growth in network traffic, which seriously impacts the user's information security. Researchers have delved into intrusion detection as an active defense technology to address this challenge. However, traditional machine learning methods struggle to capture complex threats and attack patterns when dealing with large-scale network data. In contrast, deep learning methods have the advantages of automatically extracting features from network traffic data and strong generalization capabilities. Aiming to enhance the ability of network anomaly traffic detection, this paper proposes a network traffic anomaly detection based on Deep Residual Shrinkage Network (DRSN), namely "GSOOA-1DDRSN". This method uses an improved Osprey optimization algorithm to select the most relevant and essential features in network traffic, reducing the features' dimensionality. For better detection performance of network traffic anomalies, a one-dimensional deep residual shrinkage network (1DDRSN) is designed as a classifier. Validation is performed using the NSL-KDD and UNSW-NB15 datasets and compared with other methods. The experimental results show that GSOOA-1DDRSN has improved multi-classification accuracy, precision, recall, and F1 Score by approximately 2 % and 3 %, respectively, compared to the 1DDRSN model on two datasets. Additionally, it reduces the time computation costs by 20 % and 30 % on these datasets. Furthermore, compared to other models, GSOOA-1DDRSN offers superior classification accuracy and effectively reduces the number of features.

DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors.

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems.

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things.

The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network's lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

TAXONOMIC REASSESSMENT AND MORPHOLOGICAL REDESCRIPTION OF NEMATOSTRIGEA SERPENS ANNULATA (DIGENEA: STRIGEIDAE) FROM OSPREY IN NORTH AMERICA.

Digenean trematodes in the strigeid genus Nematostrigea are parasites of various birds, most often ospreys (Pandion haliaetus). Nematostrigea currently contains 2 species, Nematostrigea serpens and Nematostigea hepatica. Nematostrigea serpens is divided into 2 subspecies: N. serpens serpens from the Palearctic and Indomalayan realms and N. serpens annulata from the Nearctic realm. In the present work, we studied the type material of N. serpens annulata and collected new specimens from an osprey in Wisconsin close to the type locality. The original description and illustration of N. serpens annulata were incomplete. Herein we provide a detailed redescription and new illustrations of N. serpens annulata on the basis of the type material as well as newly collected and sequenced specimens. Nematostrigea serpens annulata has a distinct tegumental thickening near the mid-length of the genital cone, which is absent in its congeners. Partial sequences of the large ribosomal subunit (28S) DNA and cytochrome c oxidase subunit 1 (COI) mitochondrial DNA gene sequences were generated from our specimens of N. serpens annulata. Comparisons with previously published sequences of N. serpens serpens from the Palearctic demonstrated 2.2% difference in 28S and 3.7% in COI. On the basis of morphological and molecular comparisons, we elevate N. serpens annulata to species status and recognize it as Nematostrigea annulata Dubois and Rausch, 1948.

Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design.

Broadly neutralizing antibodies (bNAbs) against HIV can reduce viral transmission in humans, but an effective therapeutic will require unusually high breadth and potency of neutralization. We employ the OSPREY computational protein design software to engineer variants of two apex-directed bNAbs, PGT145 and PG9RSH, resulting in increases in potency of over 100-fold against some viruses. The top designed variants improve neutralization breadth from 39% to 54% at clinically relevant concentrations (IC80 < 1 µg/mL) and improve median potency (IC80) by up to 4-fold over a cross-clade panel of 208 strains. To investigate the mechanisms of improvement, we determine cryoelectron microscopy structures of each variant in complex with the HIV envelope trimer. Surprisingly, we find the largest increases in breadth to be a result of optimizing side-chain interactions with highly variable epitope residues. These results provide insight into mechanisms of neutralization breadth and inform strategies for antibody design and improvement.

Continuous Automated Analysis Workflow for MRS Studies.

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

Image acquisition and interpretation of 18F-DCFPyL (piflufolastat F 18) PET/CT: How we do it.

Prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET) is rapidly becoming widely accepted as the standard-of-care for imaging of men with prostate cancer. Labeled indications for regulatoryapproved agents include primary staging and recurrent disease in men at risk of metastases. The first commercial PSMA PET agent to become available was 18F-DCFPyL (piflufolastat F 18), a radiofluorinated small molecule with high-affinity for PSMA. The regulatory approval of 18F-DCFPyL hinged upon two key, multi-center, registration trials, OSPREY (patient population: highrisk primary staging) and CONDOR (patient population: biochemical recurrence). In this manuscript, we will (1) review key findings from the OSPREY and CONDOR trials, (2) discuss the clinical acquisition protocol we use for 18F-DCFPyL PET scanning, (3) present information on important pearls and pitfalls, (4) provide an overview of the PSMA reporting and data system (PSMA-RADS) interpretive framework, and (5) posit important future directions for research in PSMA PET. Our overall goal is to provide a brief introduction for practices and academic groups that are adopting 18F-DCFPyL PET scans for use in their patients with prostate cancer.

Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures.

Resistance to pharmacological treatments is a major public health challenge. Here, we introduce Resistor-a structure- and sequence-based algorithm that prospectively predicts resistance mutations for drug design. Resistor computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity (ΔKa) of the (1) drug and (2) endogenous ligand upon a protein's mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. For validation, we applied Resistor to EGFR and BRAF kinase inhibitors treating lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the erlotinib and gefitinib "gatekeeper" T790M mutation and five known osimertinib resistance mutations. Furthermore, Resistor predictions are consistent with BRAF inhibitor sensitivity data from both retrospective and prospective experiments using KinCon biosensors. Resistor is available in the open-source protein design software OSPREY.

RESISTOR: A New OSPREY Module to Predict Resistance Mutations.

Computational, in silico prediction of resistance-conferring escape mutations could accelerate the design of therapeutics less prone to resistance. This article describes how to use the Resistor algorithm to predict escape mutations. Resistor employs Pareto optimization on four resistance-conferring criteria-positive and negative design, mutational probability, and hotspot cardinality-to assign a Pareto rank to each prospective mutant. It also predicts the mechanism of resistance, that is, whether a mutant ablates binding to a drug, strengthens binding to the endogenous ligand, or a combination of these two factors, and provides structural models of the mutants. Resistor is part of the free and open-source computational protein design software OSPREY.

Importance of Linear Combination Modeling for Quantification of Glutathione and γ-Aminobutyric Acid Levels Using Hadamard-Edited Magnetic Resonance Spectroscopy.

Background: J-difference-edited 1H-MR spectra require modeling to quantify signals of low-concentration metabolites. Two main approaches are used for this spectral modeling: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. Purpose: The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach would improve quantification of Hadamard-edited data compared with simple peak fitting. Methods: A test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE = 80 ms for HERMES and TE = 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variation (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate. Results: The reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0-9.9%. For MEGA-PRESS GSH data, reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3 and 8.8%. GABA + CVs from HERMES were 16.7 and 15.2%, respectively for the two models. Conclusion: LCM with simulated basis functions substantially improved the reproducibility of GSH quantification for HERMES data.

Disseminated Toxoplasma gondii Infection in an Adult Osprey (Pandion haliaetus).

An adult female osprey (Pandion haliaetus) was found weak and unable to fly in Auburn, Alabama in August 2019. The bird was captured and submitted to the Southeastern Raptor Center of the Auburn University College of Veterinary Medicine for evaluation. On presentation, the bird was thin with a body condition score of approximately 1.5 out of 5. The bird died during the examination and was submitted for necropsy. At the necropsy, there was a severe loss of muscle mass over the body, and the keel was prominent. The liver and spleen were moderately enlarged with pale tan to red foci randomly scattered throughout the parenchyma. A histopathologic observation revealed multifocal to coalescing areas of necrosis and hemorrhage with intralesional protozoans in the liver, spleen, lungs, kidney, sciatic nerve, esophagus, cerebrum, heart, and proventriculus. Immunohistochemistry using anti-Toxoplasma gondii-specific antibodies showed a strong positive labeling of the parasite. Semi-nested PCR, specific for the B1 gene of T. gondii, successfully identified T. gondii. This is the first confirmed case of T. gondii infection in an osprey.

Red Spot on the European Green Map: Will the Extra Catastrophic Phenomenon Take the Polish Poaching-Pressured Ospreys to the Brink of Extinction?

Poland is the only European country where the Osprey population is declining due to the mortality of adult birds from poaching, which impacts not only single breeding attempts but also the Lifetime Reproductive Success (LRS) of specimens. However, what if there came an extra mortality factor in the moment of the lowest numbers of Osprey, already vulnerable in the country? In the years 2018-2020, we installed 22 trail cameras and five digital cameras (live online video feeds) on the nests. The total failure level observed in cameras (18.5%) was high. We observed, using these cameras, the extra mortality of chicks (10.7% of potentially fledged chicks) and even adult birds by unexpected predation by Northern Goshawk and White-tailed Eagle. This phenomenon is also common in the national population, as we found a total of ten cases of total losses by predators (eight or nine of them were birds of prey), including nests not covered by camera monitoring. The extra adult-predation by Goshawks means an extra drop in LRS. Those adult and chick predations are an example of exceptional catastrophic phenomena, which have been described as the direct cause of the extinction of animal populations throughout history. Only active conservation and stop poaching of the Polish population could stop the decline and save the Polish Ospreys.

Microplastic accumulation in the gastrointestinal tracts in birds of prey in central Florida, USA.

A study was conducted to quantify the abundance of plastic pollution in the gastrointestinal tracts in birds of prey. Data was collected from all birds retrieved from the Audubon Center for Birds of Prey in central Florida, USA from January to May 2018. Individuals were either dead prior to reaching the Center or died within 24 h of arrival with no food consumed during captivity. Sixty-three individuals representing eight species were dissected to extract the gastrointestinal (GI) tract from the esophagus to the large intestine. Microplastics were found in the GI tracts in all examined species and in all individual birds. The overall mean number (±S.E.) of microplastics for species of bird of prey in central Florida was 11.9 (±2.8), and the overall mean number of microplastics per gram of GI tract tissue was 0.3 (±0.1). A total of 1197 pieces of plastic were recorded. Microfibers accounted for 86% of total plastics followed by microfragments (13%), macroplastics (0.7%) and microbeads (0.3%). Most fibers were either clear or royal blue in color. Micro-Fourier-transform infrared spectroscopy (µ-FTIR) found that processed cellulose was the most common polymer identified in birds (37%), followed by polyethylene terephthalate (16%) and a polymer blend (4:1) of polyamide-6 and poly(ethylene-co-polypropylene) (11%). Two bird species, Buteo lineatus (red-shouldered hawk, n = 28) and Pandion haliaetus (osprey, n = 16), were sufficiently abundant to enable statistical analyses. Microplastics were significantly more abundant per gram in the gastrointestinal tract tissue of B. lineatus, that consumes small mammals, snakes, and amphibians, than in fish-feeding P. haliaetus (ANOVA: p = 0.013). If raptors in terrestrial food webs have higher densities of microplastics than aquatic top predators, then it potentially could be due to a combination of direct intake of plastics and indirect consumption via trophic transfer.

Long-range transport of legacy organic pollutants affects alpine fish eaten by ospreys in western Canada.

Persistent organic pollutants (POPs) contaminate pristine, alpine environments through long-range transport in the atmosphere and glacier trapping. To study variation in POPs levels in western Canada, we measured levels in the prey (fish) of osprey (Pandion haliaetus) during 1999-2004, and compared those to levels in eggs and chicks. Values in fish muscle (representing human consumption) correlated with whole carcasses (wildlife consumption) for all POPs, except toxaphene, allowing us to pool data. Biomagnification factors for osprey eggs were much higher than published values from Oregon, reflecting differences in local diet. We factored baseline-corrected food chain variation by using amino acid-specific analysis of osprey eggs, illustrating how top predators (ospreys) can indicate both ecosystem-wide baselines and contamination. Given that our biomagnification factors were so different from those for the same species from a nearby site, we argue that trophic magnification factors derived from baseline-corrected δ15N are likely a more accurate method for estimating contamination. Dichlorodiphenyltrichloroethane (ΣDDT) concentrations were greatest in rainbow trout from a small lake at 1800 m, and those levels exceeded wildlife and human health guidelines. Indeed, once sites with known agricultural inputs were eliminated, elevation, percent lipids and baseline-corrected δ15N (from amino acid specific isotope values) best predicted ΣDDT. Baseline-corrected, but not bulk, δ15N was the main predictor of polychlorinated biphenyls (ΣPCB). Total toxaphene was consistently the major contaminant after ΣPCB and ΣDDT in osprey eggs, and was present in many fish samples. We concluded that toxaphene arrived from long range deposition due to high proportions of Parlar 40-50 congeners. The only exception was Paul Lake, where toxaphene was used as a piscicide, with a high concentrations of the Hex-Sed and Hep-Sed congeners at that site. We conclude that long-range transport and trophic position, not melting glaciers, were important determinants of some legacy POPs in fish and wildlife in alpine Canada.

Minimization-Aware Recursive K*: A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape.

Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE-A*-K*, computes provable ɛ-approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations, and using the A* enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A*-K* runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE-A*-K* is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K*. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K* (MARK*), which tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE-A*-K* versus MARK* by running the Branch and Bound over K* (BBK*) algorithm, which provably returns sequences in order of decreasing K* score, using either iMinDEE-A*-K* or MARK* to approximate partition functions. We show on 200 design problems that MARK* not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs up to 2 orders of magnitude faster. Finally, we show that MARK* not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK* is the first algorithm to do so. We use MARK* to analyze the change in energy landscape of the bound and unbound states of an HIV-1 capsid protein C-terminal domain in complex with a camelid VHH, and measure the change in conformational entropy induced by binding. Thus, MARK* both accelerates existing designs and offers new capabilities not possible with previous algorithms.

Top-down effects of repatriating bald eagles hinder jointly recovering competitors.

The recovery of piscivorous birds around the world is touted as one of the great conservation successes of the 21st century, but for some species, this success was short-lived. Bald eagles, ospreys and great blue herons began repatriating Voyageurs National Park, USA, in the mid-20th century. However, after 1990, only eagles continued their recovery, while osprey and heron recovery failed for unknown reasons. We aimed to evaluate whether top-down effects of bald eagles and bottom-up effects of inclement weather, habitat quality and fish resources contributed to the failed recovery of ospreys and herons in a protected area. We quantified the relative influence of top-down and bottom-up factors on nest colonization, persistence (i.e., nest reuse) and success for ospreys, and occurrence and size of heronries using 26 years (1986-2012) of spatially explicit monitoring data coupled with multi-response hierarchical models and Bayesian variable selection approaches. Bald eagles were previously shown to recover faster due to intensive nest protection and management. Increased numbers of eagles were associated with a reduction in the numbers of osprey nests, their nesting success and heronry size, while higher local densities of nesting eagles deterred heronries nearby. We found little evidence of bottom-up limitations on the failed recovery of herons and ospreys. We present a conservation conundrum: bald eagles are top predators and a flagship species of conservation that have benefited from intensive protection, but this likely hindered the recovery of ospreys and herons. Returning top predators, or rewilding, is widely promoted as a conservation strategy for top-down ecosystem recovery, but managing top predators in isolation of jointly recovering species can halt or reverse ecosystem recovery. Previous studies warn of the potential consequences of ignoring biotic interactions amongst recovering species, but we go further by quantifying how these interactions contributed to failed recoveries via impacts on the nesting demography of jointly recovering species. Multi-species management is paramount to realizing the ecosystem benefits of top predator recovery.

The role of age of first breeding in modeling raptor reintroductions.

The present biodiversity crisis has led to an increasing number of reintroduction programs, and this conservation method is likely to be increasingly used in the future, especially in the face of climate change. Many fundamental questions in population ecology are focused on the mechanisms through which populations escape extinction.Population viability analysis (PVA) is the most common procedure for analyzing extinction risk. In the use of PVA to model the trajectories of reintroduced populations, demographic values are sometimes taken from other existing wild populations or even from individuals in captivity.Density dependence in productivity is usually considered in viability models, but density-dependent variation in age of first breeding is usually ignored. Nevertheless, age of first breeding has a buffering effect on population fluctuations and in consequence on population persistence.We simulated the viability of Spanish Imperial Eagle (Aquila adalberti) and Osprey (Pandion haliaetus) populations using data from established and reintroduced populations in southern Spain.Our results show that reduction in the age of first breeding is critical in the success of reintroductions of such long-lived birds. Additionally, increases in productivity allow populations to growth at maximum rate. However, without considering variation in age of breeding, and the associated increasing overall productivity, reintroduced populations seem nonviable.To ignore density dependence in age of breeding in PVA means that we are seriously limiting the potential of the model population to respond to fluctuations in density, thereby reducing its resilience and viability. Variation in age of first breeding is an important factor that must be considered and included in any simulation model involving long-lived birds with deferred maturity.