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Interorganizational coalitions or collaboratives in healthcare are essential to address the health challenges of local communities, particularly during crises such as the Covid-19 pandemic. However, few studies use large-scale data to systematically assess the network structure of these collaboratives and understand their potential to be resilient or fragment in the face of structural changes. This paper analyzes data collected in 2009–2017 about 817 organizations (nodes) in 42 healthcare collaboratives (networks) throughout Florida, the third-largest U.S. state by population, including information about interorganizational ties and organizations' resource contributions to their coalitions. Social network methods are used to characterize the resilience of these collaboratives, including identification of key players through various centrality metrics, analyses of fragmentation centrality and core/periphery structure, and Exponential Random Graph Models to examine how resource contributions facilitate interorganizational ties. Results show that the most significant resource contributions are made by key players identified through fragmentation centrality and by members of the network core. Departure or removal of these organizations would both strongly disrupt network structure and sever essential resource contributions, undermining the overall resilience of a collaborative. Furthermore, one-third of collaboratives are highly susceptible to disruption if any fragmentation-central organization is removed. More fragmented networks are also associated with poorer health-system outcomes in domains such as education, health policy, and services. ERGMs reveal that two types of resource contributions – community connections and in-kind resource sharing – are especially important to facilitate the formation of interorganizational ties in these coalitions. • Collaborative healthcare resilience was assessed by analyzing key players, core-periphery structure, and fragmentation centrality. • ERGMs were estimated to examine the link between resource contributions by organizations and inter-organizational tie formation. • Florida healthcare collaboratives showed high vulnerability to fragmentation through structural changes. • Key players identified via fragmentation centrality were crucial providers of resources to healthcare coalitions. • Higher fragmentation centrality of coalition structure was associated with achievement of fewer health systems outcomes by the coalition. • Contributions of community connections and in-kind resources were most strongly associated with inter-organizational tie formation. [ABSTRACT FROM AUTHOR]

Deer surveys play an important role in the estimation of local ecological balance. In the Chitwan National Park of Nepal, the dense tree canopies and tall vegetation often obscure the presence of wild deer, which has a negative effect on the accurate population surveys of wild deer. UAVs equipped with infrared sensors have been increasingly used to monitor wild deer by capturing a lot of images. How to automatically recognize and obtain the number of deer objects from thermal images is becoming an important research topic. Due to the difference between thermal images and true-color images, as well as the variations in deer object sizes in these two types of images, current ready-to-use object detection models, designed for true-color imagery, are ill-suited for the task of detecting small deer objects within thermal imagery. In this paper, an enhanced Faster R-CNN was constructed to detect small deer objects from thermal images, in which a Feature Pyramid Network (FPN) based on a residual network is used to improve feature extraction for small deer objects and multi-scale feature map constrution for the subsequent region proposals searching, bounding box regression, and regions of interest (RoIs) classification. In addition, small-scaled anchor boxes and a multi-scale feature map selection criterion are devised to improve the detection accuracy of small objects. Finally, based on Faster R-CNN, FPN, and different residual networks including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152, we constructed five object detection models, and evaluated their detection performance by using COCO evaluation matrix. Under the condition of IoU ≥ 0.5 , the integration of Faster R-CNN, FPN, and ResNet18 demonstrated to perform better than others. Specifically, The COCO evaluation results revealed an Average Precision (AP) score of 91.6% for all deer objects. Small deer objects (area ≤ 200 pixels) achieved an AP score of 73.6%, medium deer objects (200 < area ≤ 400 pixels) demonstrated an AP score of 93.4%, and large deer objects (area > 400 pixels) achieved the highest AP score of 94.3%. Our research is helpful for effective wild deer monitoring and conservation and can be a valuable reference for the exploration of small object detection from low-resolution thermal images. • Drones with infrared sensors capture thermal images for wild deer surveys in areas covered by dense canopies. • The objects within thermal imagery are small and less than 30 × 30 pixels. • Existing object detection models, trained on true-color images, struggle with small objects detection from thermal imagery. • Feature Pyramid Network fuses the feature information extracted by residual networks to construct multi-scale feature maps. • Enhanced Faster R-CNN with customized anchor boxes and multi-scale feature maps achieves an average precision. [ABSTRACT FROM AUTHOR]

Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone. • The largest snow leopard dataset for deep learning research was compiled from zoo and field data. • PIE and Hotspotter run together increase snow leopard image reidentification to 85% Rank-1. • Background subtraction and left-right mirroring decreased overfitting with the PIE algorithm. • Whiskerbook included PIE and Hotspotter deep learning processing along with data curation tools. [ABSTRACT FROM AUTHOR]

• Effects of stand age and tree density on single tree and overstory biomass. • Variation of understory biomass with overstory biomass. • Optimal canopy density to balance overstory biomass and understory biomass. • Proper tree densities at different ages under optimal canopy density. • Practical tradeoff approach for the multifunctional management of larch plantation. Understory vegetation biomass (UVB) is necessary for forest stability and supply of numerous forest ecosystem services (FES). However, UVB is often ignored in traditionally managed overly dense plantations. A tradeoff approach between the overstory biomass (OB) and UVB, based on the quantitative evaluation of overstory stand structure effects on UVB, was examined using 82 sample plots of larch (Larix Principis-rupprechetii) plantation in the Liupan Mountains (LPM), northwest China. Results showed that with rising tree density at given age, the single tree biomass decreased, but the OB increased. The UVB decreased significantly with rising OB. The optimal canopy density of 0.7 was suggested to balance OB and UVB. The corresponding proper tree densities calculated using fitted model were about 2600, 2000, 1600, 1250, 1000 trees/ha for the ages of 15, 20, 25, 30, 35 years, respectively. These proper tree densities lead to an increase of single tree biomass by 0.8%, 4.1%, 3.9%, 4.1%, 5.5%, an increase of UVB by 0.9%, 10.1%, 16.1%, 32.2%, 88.5%, but a decrease of OB by 2.9%, 13.26%, 12.5%, 13.3%, 18.5%, compared with the stands having the maximum tree densities of 2700, 2400, 1900, 1500 and 1300 trees/ha. The study can help to guide the multifunctional forest management and to uplift FES of degraded plantation. [ABSTRACT FROM AUTHOR]

[Display omitted] • The carbon density of larch plantation ecosystems varies with tree age. • Tree density and canopy density affect the overstory and understory carbon density. • Canopy density and overstory tree biomass affect the humus and soil carbon density. • The tradeoff among carbon density components in overstory, understory, humus and soil layers can be realized through the optimization of stand structure. • Optimum canopy density is required for multifunctional management of larch plantation. Information about the variation of carbon density components of larch plantation (LP) with changing overstory stand structure is lacking, which limits multifunctional forest management. This study sought to address these components in the Liupan Mountains of northwest China. It showed that stand age, tree density, tree biomass, and canopy density are important stand structure parameters influencing the amplitude and composition of carbon density. The results showed that, with rising stand age, the carbon density of both overstory tree and humus layers increased, the carbon density of the understory vegetation layer (shrubs and herbs) decreased, and the carbon density of soil layer (0–100 cm) first increased and then slightly decreased. However, the total ecosystem carbon density increased with rising stand age. With rising tree density and canopy density, the overstory carbon density increased, up to the thresholds of about 1800 trees/ha and 0.7 respectively, but after that increased very slowly; while the understory vegetation carbon density decreased continuously with rising tree density, but decreased with rising canopy density, first very slowly up to the range of 0.6–0.7 and after that very quickly. With rising overstory tree biomass, the humus layer carbon density increased, while soil layer carbon density first increased to a certain threshold, then leveled off or slowly decreased. With rising canopy density, the humus and soil carbon density first increased to the threshold of 0.7 and 0.67, respectively, then decreased. Therefore, the optimum canopy density of 0.7 maintains the highest total ecosystem carbon density with its highest or relatively higher components. Such a tradeoff among the carbon components informs multifunctional forest management since every carbon component is related to specific functions/services of the LP ecosystems. [ABSTRACT FROM AUTHOR]