<i>NLMR</i> and <i>landscapetools</i>: An integrated environment for simulating and modifying neutral landscape models in R

Sciaini, Marco; Fritsch, Matthias; Scherer, Cédric; Simpkins, Craig

doi:10.1101/307306

Cited by 44 publications

(49 citation statements)

References 23 publications

(17 reference statements)

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“…We used neutral landscape models (NLM) to assess the impact of different future mortality trajectories on forest demography. We built NLM of random and clumped forests for each country using the NLRM package (Sciaini et al 2018). The grain of the simulation was set to 1 ha (i.e., 100 times 100 meter), being close to the median patch size of natural mortality in temperate Europe (Senf and Seidl 2018).…”

Section: Methodsmentioning

confidence: 99%

Increases in canopy mortality and their impact on the demographic structure of Europe’s forests

Senf

Sebald

Seidl

2020

Preprint

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Pulses of tree mortality have been reported for many ecosystems across the globe. Yet, 13 large-scale trends in tree mortality remain poorly quantified. Manually analyzing more than 14 680,000 satellite image chips at 19,896 plot locations, we here show that forest canopy mortality 15 in Europe has continuously increased since 1985 (+1.5 ± 0.28 % yr -1 ), with the highest canopy 16 mortality rate of the past 34 years observed in 2018 (1.14 ± 0.16 %). Using simulation modeling 17 we further demonstrate that a continued increase in canopy mortality will largely alter forest 18 demography, with the median forest age falling below 30 years in nearly half of Europe's 19 countries by 2050. These demographic changes will have substantial cascading effects on the 20 regeneration, biodiversity, and carbon storage. The current trend of increasing canopy mortality 21 reported herein thus challenges the future of Europe's forests, and should be a key priority of 22 forest policy and management. 23 24 25 26 7 29 countries, resulting in the analysis of 10,440 NLMs in total. From those NLMs, we derived the 126 median age of forests within each country for the year 2050 and for the three scenarios as 127 indicator of changes in forest demography. 128 Results and discussion 129 Trends in canopy mortality rates 130 Canopy mortality increased by 1.50 ± 0.28 % per year across Europe (Figure 1). The average 131 canopy mortality rate in the late 20 th century (1985 to 1999) was 0.79 ± 0.04 % yr -1 (i.e., a forest 132 area of 1.7 Mill. ha affected by canopy mortality each year) and increased to 0.99 ± 0.04 % yr -1 133 (i.e., 2.1 Mill. ha) in the early 21 st century (2000 to 2018). Mortality increased at an accelerating 134 pace throughout the observation period, with the highest canopy mortality rate of the past 34 135 years observed for 2018 (1.14 ± 0.16 % yr -1 ). 136 137 138

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Section: Methodsmentioning

confidence: 99%

Increases in canopy mortality and their impact on the demographic structure of Europe’s forests

Senf

Sebald

Seidl

2020

Preprint

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“…To generate non-uniform density patterns, we simulated landscapes with spatial dependence by employing a parametric Gaussian random field model that allows for specification of the degree and range of spatial autocorrelation. Gaussian random fields were generated using the R package, NLMR (Sciaini et al, 2018). The values of the simulated landscape were scaled from 0 to 1 and individual activity centers distributed according to the following cell probabilities: where X i is the scaled landscape value at pixel i and β 1 is defined as 1.2 to represent a weak but apparent density pattern.…”

Section: Methodsmentioning

confidence: 99%

Optimal sampling design for spatial capture-recapture

Dupont

Royle

Nawaz

et al. 2020

Preprint

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10Spatial capture-recapture (SCR) has emerged as the industry standard for 11 analyzing observational data to estimate population size by leveraging information from 12 spatial locations of repeat encounters of individuals. The resulting precision of density 13 estimates depends fundamentally on the number and spatial configuration of traps.14 Despite this knowledge, existing sampling design recommendations are heuristic and 15 their performance remains untested for most practical applications -i.e., 16spatially-structured and logistically challenging landscapes. To address this issue, we 17 propose a genetic algorithm that minimizes any sensible, criteria-based objective 18 function to produce near-optimal sampling designs. To motivate the idea of optimality, 19 we compare the performance of designs optimized using two model-based criteria 20 related to the probability of capture. We use simulation to show that these designs 21 out-perform those based on existing recommendations in terms of bias, precision, and 22 accuracy in the estimation of population size. Our approach allows conservation 23 practitioners and researchers to generate customized sampling designs that can improve 24 monitoring of wildlife populations. 25Keywords-SCR, spatial capture-recapture, spatially-explicit capture-recapture, 26 camera traps, density, optimal design, sampling design, spatial sampling, trap spacing 27 29 (Williams et al., 2002) which has driven the development of data collection and estimation 30 methods, especially those that can account for imperfect detection. Capture-recapture (CR), 31 and more recently, spatial capture-recapture (SCR: Royle et al., 2014) methods were 32 developed specifically for this purpose and are now routinely applied in ecological research. 33 Concurrently, SCR methods estimate detection, space use, and density by analyzing 34 2 individual encounter histories while explicitly incorporating auxiliary information from the 35 spatial organization of encounters (Efford, 2004; Royle et al., 2014). Despite widespread 36 adoption and rapid method development, recommendations about spatial sampling design 37 have received relatively little attention and are arguably heuristic. 38 The effects of sampling design have been investigated for both CR (Dillon and Kelly 39 2007; Bondrup-Nielsen 1983; Gardner et al. 2010) and SCR methods (discussed in the next 40 paragraph). While CR methods aim to balance the number of captures and the number of 41 recaptures, SCR requires a third consideration, the number of spatial recaptures, i.e., the 42 number of times individuals are observed at multiple locations. The ability to reliably 43 estimate these quantities is directly related to the quality of the data collected: the number of 44 captured individuals n is the sample size; the number of recaptures is directly related to the 45 baseline detection probability, g 0 ; and the number and spatial distribution of recaptures are 46 directly related to the spatial scale parameter, σ. Therefore, improving sampling design...

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“…A1) which can be summarised in four major steps: 1) a virtual ecological simulation model of an ecosystem (or landscape, in this case), 2) a virtual sampling process, sampling data from the virtual ecosystem or landscape, 3) analyses of the sampled data and 4) an evaluation of the results against the true value for the full virtual ecosystem or landscape (Zurell et al 2010). Following this approach, we first simulated neutral landscapes (500 × 500 cells) containing five classes (relative proportion of 20% each) with either low, medium or high spatial autocorrelation, respectively (NLMR package, Sciaini et al 2018). For each landscape, we calculated all available landscape-level metrics that were invariant to the absolute plot area (Supplementary material Appendix 1 Table A1).…”

Section: Use Casementioning

confidence: 99%

“…First published in 1995, FRAGSTATS was the first software to provide an extensive collection of landscape metrics, and subsequently, revolutionized landscape pattern analysis (Kupfer 2012, Gustafson 2019. However, ecologists are increasingly turning to R (Sciaini et al 2018), a language originally developed for statistical computing (< www.rproject.org >). Nowadays, R is more and more used for the analysis, modelling and visualization of spatial data (Fletcher and Fortin 2018).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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

Cited by 44 publications

References 23 publications

Increases in canopy mortality and their impact on the demographic structure of Europe’s forests

Increases in canopy mortality and their impact on the demographic structure of Europe’s forests

Optimal sampling design for spatial capture-recapture

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

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