91. Neutral landscape models (NLMs) simulate landscape patterns based 10 on theoretical distributions and can be used to systematically study 11 the effect of landscape structure on ecological processes. NLMs are 12 commonly used in landscape ecology to enhance the findings of field 13 studies as well as in simulation studies to provide an underlying land-14 scape. However, their creation so far has been limited to software 15 that is platform dependent, does not allow a reproducible workflow or 16 is not embedded in R, the prevailing programming language used by 17 ecologists. 18 2. Here, we present two complementary R packages NLMR and land-19 scapetools, that allow users to generate, manipulate and analyse NLMs 20 in a single environment. They grant the simulation of the widest col-21 lection of NLMs found in any single piece of software thus far while 22 allowing for easy manipulation in a self-contained and reproducible 23 workflow. The combination of both packages should stimulate a wider 24 usage of NLMs in landscape ecology. NLMR is a comprehensive col-25 lection of algorithms with which to simulate NLMs. landscapetools 26 provides a utility toolbox which facilitates an easy workflow with sim-27 ulated neutral landscapes and other raster data.283. We show two example applications that illustrate potential use cases 29 for NLMR and landscapetools: First, an agent-based simulation study 30 in which the effect of spatial structure on disease persistence was stud-31 ied. Here, spatial heterogeneity resulted in more variable disease out-32 comes compared to the common well-mixed host assumption. The 33 second example shows how increases in spatial scaling can introduce 34 35 4. Simplifying the workflow around handling NLMs should encourage an 36 uptake in the usage of NLMs. NLMR and landscapetools are both 37 generic frameworks that can be used in a variety of applications and 38 are a further step to having a unified simulation environment in R for 39 answering spatial research questions.40 Keywords: artificial pattern, landscape generator, neutral landscape 41 model, R, spatial visualisation, virtual landscape 42 48 tions and metrics of ecological patterns and processes at landscape scales (With 49

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. Here, we present two complementary R packages NLMR and landscapetools, that allow users to generate and manipulate 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 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. 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. The second example shows how increases in spatial scaling can introduce biases in calculated landscape metrics. Simplifying the workflow around generating and 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.

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The rate of human-induced environmental change continues to accelerate, stimulating the need for rapid and science-based decision making. The recent availability of cyberinfrastructure, open-source data and novel techniques has increased opportunities to use ecological forecasts to predict environmental change. But to effectively inform environmental decision making, forecasts should not only be reliable, but should also be designed to address the needs of decision makers with their assumptions, uncertainties, and results clearly communicated. To help researchers better integrate forecasting into decision making, we outline ten practical guidelines to help navigate the interdisciplinary and collaborative nature of forecasting in social-ecological systems. Some guidelines focus on improving forecasting skills, including how to build better models, account for uncertainties and use technologies to improve their utility, while others are developed to facilitate the integration of forecasts with decision making, including how to form effective partnerships and how to design forecasts relevant to the specific decision being addressed. We hope these guidelines help researchers make forecasts more accurate, precise, transparent, and most pressingly, useful for informing environmental decisions.

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Biodiversity assets often require conservation management, which, in turn, necessitates decisions about which ecosystem, community or species should be prioritised to receive resources. Population viability analysis (PVA) uses a suite of quantitative methods to estimate the likelihood of population decline and extinction for a given species, and can be used to assess a population's status, providing useful information to decision-makers. In New Zealand, a range of taxa have been analysed using the PVA approach, but the scope of its implementation has not previously been reviewed. We compiled a database of 78 published PVAs for New Zealand indigenous fauna and flora, along with details of the species considered, the data used to parametrise the model, and the technical details of their implementation. We assessed the taxa and threat status of the species for which PVA were conducted relative to the distribution of taxa across threat classes in the New Zealand Threat Classification System database. There were clear biases in the species selected for analysis, notably an over-representation of birds and threatened species in general, and an under-representation of invertebrates and plants. Model parameterisation and implementation were often not reported in a transparent or standardised way, which hinders model communication and reconstruction. To maximise the benefit of PVAs, we suggest that more attention should be given to the ecosystem-level importance of species, and to species whose threat status is changing rapidly or are not yet threatened. More clearly describing the parameterisation, underlying assumptions and implementation of PVAs will help to better contextualise their results and support reproducible ecological science and decision-making.

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