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Understanding the mechanisms driving biological diversity remains a central problem in ecology and evolutionary biology. Traditional explanations assume that differences in selection pressures lead to different adaptations in geographically separated locations. This book takes a different approach and explores adaptive diversification--diversification rooted in ecological interactions and frequency-dependent selection. In any ecosystem, birth and death rates of individuals are affected by interactions with other individuals. What is an advantageous phenotype therefore depends on the phenotype of other individuals, and it may often be best to be ecologically different from the majority phenotype. Such rare-type advantage is a hallmark of frequency-dependent selection and opens the scope for processes of diversification that require ecological contact rather than geographical isolation.

Understanding the mechanisms driving biological diversity remains a central problem in ecology and evolutionary biology. Traditional explanations assume that differences in selection pressures lead to different adaptations in geographically separated locations. This book takes a different approach and explores adaptive diversification--diversification rooted in ecological interactions and frequency-dependent selection. In any ecosystem, birth and death rates of individuals are affected by interactions with other individuals. What is an advantageous phenotype therefore depends on the phenotype of other individuals, and it may often be best to be ecologically different from the majority phenotype. Such rare-type advantage is a hallmark of frequency-dependent selection and opens the scope for processes of diversification that require ecological contact rather than geographical isolation. Michael Doebeli investigates adaptive diversification using the mathematical framework of adaptive dynamics. Evolutionary branching is a paradigmatic feature of adaptive dynamics that serves as a basic metaphor for adaptive diversification, and Doebeli explores the scope of evolutionary branching in many different ecological scenarios, including models of coevolution, cooperation, and cultural evolution. He also uses alternative modeling approaches. Stochastic, individual-based models are particularly useful for studying adaptive speciation in sexual populations, and partial differential equation models confirm the pervasiveness of adaptive diversification. Showing that frequency-dependent interactions are an important driver of biological diversity,Adaptive Diversificationprovides a comprehensive theoretical treatment of adaptive diversification.

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

Aim The idea of combining predictions from different models into an ensemble has gained considerable popularity in species distribution modelling, partly due to free and comprehensive software such as the R package BIOMOD. However, despite proliferation of ensemble models, we lack oversight of how and where they are used for modelling distributions, and how well they perform. Here, we present such an overview. Location Global. Methods Since BIOMOD is freely available and widely used by ensemble species distribution modellers, we focused on articles that apply BIOMOD, filtering the initial 852 papers identified in our structured literature search to a relevant final subset of 224 eligible peer‐reviewed journal articles. Results BIOMOD‐based ensembles are used across many taxa and locations, with terrestrial plants being the most represented group of species (n = 72) and Europe being the most represented continent (n = 106). These studies often focus on forecasting distributions in the future (n = 109), and commonly use presence‐only species data (n = 139) and climatic environmental predictors (n = 219). An average of six models are used in ensembles, and approximately half of ensembles weight contributions of models by their cross‐validation performance. However, discussion about choices made in the modelling process and unambiguous information on the performance of ensemble models versus individual models are limited. The use of independent data to validate model performance is particularly uncommon. Main conclusions We document the breadth of ensemble applications, but could not draw strong quantitative conclusions about the predictive performance of ensemble models, due to lack of unambiguous information reported. Understanding how and where ensembles are best used when modelling species distributions is important for enabling best choices for different applications. To enable this objective to be achieved, we provide recommendations for thorough reporting practices in a BIOMOD‐based ensemble workflow.

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity. The collation of biodiversity datasets with broad taxonomic, biogeographic, and spatial extents is necessary to understand historical declines and to project—and hopefully avert—future declines. We describe and make freely available a database of more than 3.2 million biodiversity measurements from 94 countries representing over 47,000 species, collated from 480 existing spatial comparisons of local‐scale biodiversity exposed to different intensities and pressures relating to land use, from terrestrial sites around the world.

Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data.

Biotic interactions play a fundamental role in shaping multitrophic species communities, yet incorporating these interactions into species distribution models (SDMs) remains challenging. With the growing availability of species interaction networks, it is now feasible to integrate these interactions into SDMs for more comprehensive predictions. Here, we propose a novel framework that combines trophic interaction networks with Bayesian structural equation models, enabling each species to be modeled based on its interactions with predators or prey alongside environmental factors. This framework addresses issues of multicollinearity and error propagation, making it possible to predict species distributions in unobserved locations or under future environmental conditions, even when prey or predator distributions are unknown. We tested and validated our framework on realistic simulated communities spanning different theoretical models and ecological setups. scenarios. Our approach significantly improved the estimation of both potential and realized niches compared to single SDMs, with mean performance gains of 8% and 6%, respectively. These improvements were especially notable for species strongly regulated by biotic factors, thereby enhancing model predictive accuracy. Our framework supports integration with various SDM extensions, such as occupancy and integrated models, offering flexibility and adaptability for future developments. While not a universal solution that consistently outperforms single SDMs, our approach provides a valuable new tool for modeling multitrophic community distributions when biotic interactions are known or assumed.

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic-index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random-forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R² ≥ 0.94, mean squared error [MSE] ≤ 170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random-forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R² ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats. El monitoreo acústico pasivo podría ser una manera poderosa de evaluar la biodiversidad en escalas temporales y espaciales grandes. Sin embargo, la extracción de información significativa a partir de grabaciones puede ser inasequible y requerir de mucho tiempo. Los índices acústicos (es decir, un resumen matemático de la energía acústica) proporcionan un método relativamente rápido para procesar los datos acústicos y cada vez se usan más para caracterizar las comunidades biológicas. Examinamos la relación entre los índices acústicos y la diversidad y abundancia de sonidos biológicos en las grabaciones. Revisamos la bibliografía sobre el índice de acústica y encontramos que más de 60 índices han sido aplicados a una gama de objetivos con éxito variante. Usamos 36 de los índices más indicativos para desarrollar un modelo predictivo de la diversidad de sonidos de animales en las grabaciones. Se recolectaron datos acústicos en 43 sitios en habitats terrestres templados y marinos tropicales en todos los Estados Unidos continentales. Para las grabaciones terrestres, los modelos de bosques aleatorios junto con un juego de índices acústicos como covariantes predijeron la diversidad de Shannon, la riqueza y el número total de sonidos biológicos con una certeza elevada (R² ≥ 0.94, error medio al cuadrado [MSE] ≤ 170.2). Entre los índices que se evaluaron, la desigualdad, la actividad acústica y la riqueza acústica fueron los que más contribuyeron a la habilidad predictiva de los modelos. El desempeño de los modelos de índices fue afectado negativamente por sonidos de insectos, del clima y de origen humano. Para las grabaciones marinas, los modelos de bosque aleatorio predijeron pobremente la diversidad de Shannon, la riqueza y el número total de sonidos biológicos (R² ≤ 0.40, MSE ≥ 195). Nuestros resultados sugieren que el uso de una combinación de índices acústicos relevantes dentro de un modelo flexible puede predecir con exactitud la diversidad de los sonidos biológicos en un registro acústico de un habitat terrestre templado. Así, las estrategias acústicas podrían ser una contribución importante para el monitoreo de la biodiversidad en algunos habitats. 被动的声音监测可以跨越较大的时空尺度有效地评估生物多祥性。然而，从录音中提取有意义的信息可 能会非常耗时。声学指标（即对声能的数学总结) 提供了一种相对快速地处理声学数据的方法，正越来越多地 被用于描述生物群落的特征。我们检验了声学指标与录音中生物声音的多祥性和丰度之间的关系。通过对声学 指标文献的综述，我们找到了超过 60 个用于不同目的的声学指标，成效不一。我们选用了 36 个最具指示性的 指标，以建立录音中动物声音多祥性的预测模型。声学数据来自美国大陆的温带陆地和热带海洋生境的 43 个 位点。在陆地的录音中，含有一系列声学指标作为协变量的随机森林模型可以精准地预测香农多样性、丰富度 和生物声音的总数 (R² ≥ 0.94，平均方差 [MSE] ≤ 170.2)。在我们评估的指标中，声音的粗糖度、活动性和丰富 度对模型预测能力贡献最大。指数模型的效果会受到昆虫、天气和人类活动声音的负面影响。对于海洋录音来 说，随机森林模型对香农多祥性、丰富度和生物声音总数的预测结果不佳(R² ≤ 0.40, MSE ≥ 195) 。我们的结 果表明，在模型中灵活运用相关声学指标的组合可以准确预测温带陆地生态系统录音的生物声音多祥性。因此， 声学方法可以为某些生境的生物多祥性监测做出重要贡献。

Aim: There is enormous interest in applying connectivity modelling to resistance surfaces for identifying corridors for conservation action. However, the multiple analytical approaches used to estimate resistance surfaces and predict connectivity across resistance surfaces have not been rigorously compared, and it is unclear what methods provide the best inferences about population connectivity. Using a large empirical data set on puma (Puma concolor), we are the first to compare several of the most common approaches for estimating resistance and modelling connectivity and validate them with dispersal data. Location: Southern California, USA. Methods: We estimate resistance using presence-only data, GPS telemetry data from puma home ranges and genetic data using a variety of analytical methods. We model connectivity with cost distance and circuit theory algorithms. We then measure the ability of each data type and connectivity algorithm to capture GPS telemetry points of dispersing pumas. Results: We found that resource selection functions based on GPS telemetry points and paths outperformed species distribution models when applied using cost distance connectivity algorithms. Point and path selection functions were not statistically different in their performance, but point selection functions were more sensitive to the transformation used to convert relative probability of use to resistance. Point and path selection functions and landscape genetics outperformed other methods when applied with cost distance; no methods outperformed one another with circuit theory. Main conclusions: We conclude that path or point selection functions, or landscape genetic models, should be used to estimate landscape resistance for wildlife. In cases where resource limitations prohibit the collection of GPS collar or genetic data, our results suggest that species distribution models, while weaker, may still be sufficient for resistance estimation. We recommend the use of cost distance-based approaches, such as least-cost corridors and resistant kernels, for estimating connectivity and identifying functional corridors for terrestrial wildlife.