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.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.04617

landscapemetrics: an open‐source R tool to calculate landscape metrics

Habitat loss is the primary driver of biodiversity decline worldwide, but the effects of fragmentation (the spatial arrangement of remaining habitat) are debated. We tested the hypothesis that forest fragmentation sensitivity-affected by avoidance of habitat edges-should be driven by historical exposure to, and therefore species' evolutionary responses to disturbance. Using a database containing 73 datasets collected worldwide (encompassing 4489 animal species), we found that the proportion of fragmentation-sensitive species was nearly three times as high in regions with low rates of historical disturbance compared with regions with high rates of disturbance (i.e., fires, glaciation, hurricanes, and deforestation). These disturbances coincide with a latitudinal gradient in which sensitivity increases sixfold at low versus high latitudes. We conclude that conservation efforts to limit edges created by fragmentation will be most important in the world's tropical forests.

/pdf/extinction-filters-mediate-the-global-effects-of-habitat-4auuhbkubo.pdf

Extinction filters mediate the global effects of habitat fragmentation on animals.

The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera-trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance-estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera-trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision-making.

Abundance estimation of unmarked animals based on camera-trap data.

Spending time in nature is one potential way to cope with the negative physical and psychological health impacts from major stressful life events. In 2020, a large fraction of the global population was impacted by restrictions to contain the spread of the COVID-19 outbreak, a period characterised by marked health risks and behavioural changes. Here we explore whether people responded to this stressor by spending more time in nature and investigate the reasons for any changes.We surveyed 1,002 people in Brisbane, Australia in 2020, to measure the change in use of green space during the restrictions period and benefits people associated with visiting them.About 36% of participants increased their urban green space use, but 26% reduced it, indicating a great deal of flux. Furthermore, 45% of the previous non-users of urban green space began using it for the first time during the restrictions period. Older people were less likely to increase their green space use and those with a backyard were more likely to increase their use of green spaces.Participants' change in use occurred regardless of the amount of green space available in close proximity to their households. In addition, we did not find a relationship between nature-relatedness and change in use.People's reasons for green space use shifted during the pandemic-related restrictions period, with many emphasising improvement of personal well-being rather than consolidating community capital. Most participants indicated an increase in the importance of the psychological and physical benefits obtained from urban green spaces.We conclude that increased urban green space use during moments of stress such as the COVID-19 pandemic has the potential to ameliorate some of the negative effects of the stressor, but that the capacity and desire to spend more time in green space varies markedly across society. Sufficient urban green space provision for all sections of society will maximise the opportunity to employ a nature-based coping mechanism during times of personal or community stress.

Urban green space use during a time of stress: A case study during the COVID-19 pandemic in Brisbane, Australia

https://www.nature.com/articles/s41467-023-39221-x.pdf

Global forest fragmentation change from 2000 to 2020

1. Neutral landscape models (NLMs) simulate landscape patterns based on theoretical distributions and can be used to systematically study the effect of landscape structure on ecological processes. NLMs are commonly used in landscape ecology to enhance the findings of field studies as well as in simulation studies to provide an underlying landscape. However, their creation so far has been limited to software that is platform dependent, does not allow a reproducible workflow or is not embedded in R, the prevailing programming language used by ecologists. 2. Here, we present two complementary R packages NLMR and landscapetools, that allow users to generate, manipulate and analyze NLMs in a single environment. They grant the simulation of the widest collection of NLMs found in any single piece of software thus far while allowing for easy manipulation in a self-contained and reproducible workflow. The combination of both packages should stimulate a wider usage of NLMs in landscape ecology. NLMR is a comprehensive collection of algorithms with which to simulate NLMs. landscapetools provides a utility toolbox which facilitates an easy workflow with simulated neutral landscapes and other raster data. 3. We show two example applications that illustrate potential use cases for NLMR and landscapetools: First, an agent-based simulation study in which the effect of spatial structure on disease persistence was studied. Here, spatial heterogeneity resulted in more variable disease outcomes compared to the common well-mixed host assumption. The second example shows how increases in spatial scaling can introduce biases in calculated landscape metrics. 4. Simplifying the workflow around handling NLMs should encourage an uptake in the usage of NLMs. NLMR and landscapetools are both generic frameworks that can be used in a variety of applications and are a further step to having a unified simulation environment in R for answering spatial research questions.

https://www.biorxiv.org/content/biorxiv/early/2018/04/25/307306.full.pdf

NLMR and landscapetools: An integrated environment for simulating and modifying neutral landscape models in R

Landscape Metrics for Categorical Map Patterns [R package landscapemetrics version 1.5.2]

Biodiversity theories are not very often explicitly consulted in conservation practice, but implicitly many conservation decisions rely on theory. Biodiversity theories can inform important conservation actions such as assessments of species richness and extinction or habitat loss and fragmentation. Popular examples of biodiversity theories are niche theory and island biogeography theory, whereas neutral theory is less known. Here, we review the implications of biodiversity theories for conservation practice, focusing on neutral theory. Neutral theory assumes that the establishment and success of an individual in a community does not depend on its species identity, but is instead predominantly driven by a stochastic process. We found that drift and stochasticity appear much less frequently in conservation studies than selection processes typical of niche theory. This might be because habitat-specificity is not supported by neutral theory, but is common among rare and vulnerable species. Furthermore, neutral theory makes less intuitive assumptions than niche theory and does not consider trophic interactions. However, models based on neutral theory proved to be useful in some biodiversity hotspots. Moreover, some models based on neutral theory subdivide space into local community and metacommunity, which reflects concepts commonly used in conservation science. Neutral approaches have been used in conservation to generate realistic species-abundance distributions and species-area relationships, provide a standard against which to compare species loss, prioritize species protection, model biological invasions, and support protected area design. We propose that neutral theory can serve as a valuable first-order approximation to reduce complexity and by design account for drift and stochasticity. Neutral theory provides the benefits of a community theory whereas niche theory focuses on single species. Ideally, neutral approaches should be used as a starting point for conscious stepwise addition of niche structure. This step-wise approach reflects recent integrative biodiversity theories that combine aspects of neutral and niche theory such as the stochastic niche or emergent neutrality and may provide a promising foundation for future conservation practice.

Applications of biodiversity theories in conservation

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society rOpenSci Resources The software package nlrx, developed as part of this research effort, was extensively reviewed and approved by the rOpenSci proj‐ ect (https ://ropen sci.org). A full record of the review is available at: https :// github.com/ropen sci/nlrx Abstract 1. Agent‐based models find wide application in all fields of science where large‐scale pat‐ terns emerge from properties of individuals. Due to increasing capacities of computing resources it was possible to improve the level of detail and structural realism of next‐ generation models in recent years. However, this is at the expense of increased model complexity, which requires more efficient tools for model exploration, analysis and docu‐ mentation that enable reproducibility, repeatability and parallelization. NetLogo is a widely used environment for agent‐based model development, but it does not provide sufficient built‐in tools for extensive model exploration, such as sensitivity analyses. One tool for controlling NetLogo externally is the r‐package RNetLogo. However, this package is not suited for efficient, reproducible research as it has stability and resource allocation issues, is not straightforward to be setup and used on high performance computing clusters and does not provide utilities, such as storing and exchanging metadata, in an easy way. 2. We present the r‐package nlrx, which overcomes stability and resource allocation issues by running NetLogo simulations via dynamically created XML experiment files. Class objects make setting up experiments more convenient and helper functions provide many parameter exploration approaches, such as Latin Hypercube designs, Sobol sensitivity analyses or optimization approaches. Output is automatically col‐ lected in user‐friendly formats and can be post‐processed with provided utility func‐ tions. nlrx enables reproducibility by storing all relevant information and simulation output of experiments in one r object which can conveniently be archived and shared. 3. We provide a detailed description of the nlrx package functions and the overall workflow. We also present a use case scenario using a NetLogo model, for which we performed a sensitivity analysis and a genetic algorithm optimization. 4. The nlrx package is the first framework for documentation and application of re‐ producible NetLogo simulation model analysis.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.13286

The nlrx r package: A next-generation framework for reproducible NetLogo model analyses

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