Adaptive niche‐based sampling to improve ability to find rare and elusive species: Simulations and field tests

Chiffard, Jules; Marciau, Coline; Yoccoz, Nigel G.; Mouillot, Florent; Duchateau, Stéphane; Nadeau, Iris; Fontanilles, Philippe; Besnard, Aurélien

doi:10.1111/2041-210x.13399

Cited by 17 publications

(10 citation statements)

References 51 publications

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“…One example are SDM-guided sampling designs: SDMs are fitted on species and environmental data collected through preliminary sampling. The obtained predictions are then used to identify areas to collect new data on the target species (Chiffard et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Effect of sampling strategies on the response curves estimated by plant species distribution models

Bazzichetto¹,

Lenoir²,

Re³

et al. 2022

Preprint

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Species distribution models (SDMs) rely on species presence/absence or abundance data and environmental variables to estimate species response curves. Therefore, the quality (and quantity, i.e., sample size) of the data to describe the species distribution determines the quality of the estimate of the species-environment relationship. However, SDMs are seldom fitted on high-quality data collected strictly for that purpose. Usually, SDMs rely on a collection of opportunistic datasets sampled from previous projects or public repositories with different objectives. Here, we aim at assessing how the sampling strategy capturing the geographic distribution of a species affects the accuracy and precision of its response curves along environmental gradients, as estimated by parametric SDMs. We simulated the occurrence of two virtual plant species across the Abruzzo region (Italy). We assumed that the two virtual plants were similarly affected by precipitation, but one had a wider realised niche for temperature (i.e., higher thermal tolerance), and, as a result, a wider distribution extent. Then, we sampled occurrence data for the two species following five different sampling strategies: random, stratified, systematic, topographic, and uniform (the latter performed within the environmental space). In addition, we simulated a spatially biased sampling design by collecting presence/absence data close to roads. To account for sample size, we also repeated our simulations along a gradient of increasing sampling effort, i.e., number of sampled locations. In total, we ran 500 replicates for each combination of sampling design and effort. For each replicate, we fitted SDMs using binomial generalised linear models and extracted the model coefficients for precipitation and temperature to be compared with the true coefficients from the virtual species’ model. We evaluated the quality of the estimated response curves by computing the following measures: bias (accuracy), variance (precision), and mean squared error (accuracy and precision). Our results suggest that a proper estimate of the species response curve can be obtained when the choice of the sampling strategy is guided by the species’ ecology. In particular, species with wide tolerances to environmental drivers may be better modelled using data uniformly collected within the environmental space, while none of the tested sampling designs seemed to substantially outperform the others for modelling species with a narrow realised niche.

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Section: Discussionmentioning

confidence: 99%

Effect of sampling strategies on the response curves estimated by plant species distribution models

Bazzichetto¹,

Lenoir²,

Re³

et al. 2022

Preprint

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“…Another fruitful approach is to combine adaptive with SDM‐guided sampling (Aizpurua et al, 2015; Chiffard et al, 2020; e.g. Lin et al, 2014) where one sampling session provides information to model and the following sessions allow adjusting the distribution of samples (Thompson, 2013a,b; Yoccoz et al, 2001).…”

Section: Where To Samplementioning

confidence: 99%

Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Jeliazkov

Gavish

Marsh

et al. 2022

Global Change Biology

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Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade‐offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species‐targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much‐needed guide through the ever‐increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

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“…NASA's Global Ecosystem Dynamics Investigation (GEDI) can measure 3D plant canopy structure for Mediterranean ecosystems (Schneider et al 2020). Vegetation simulations and field tests (Chiffard et al 2020) can evaluate model evaluation. Finally, the evidence on the spatial patterns of land change, the processes of the conversion of vegetated land may be valuable for policy-makers, domestic organisations and for interactions of climate and biodiversity in Europe.…”

Section: Potential Vegetation Conservation Areasmentioning

confidence: 99%

Remote Sensing Analysis of the Changes in Vegetated Coastland: The Case of Southeastern Europe

Laze

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The coastlands are changing in the Southeastern European countries. Yet, temporal and spatial changes in natural-vegetated land, urban-vegetated land and agriculture-vegetated land use on coastal surfaces, respectively, are missing. The aim is to understand the changes in vegetated land on the coastal surface by retrieving spatial data from remote sensing using a new approach by reducing errors in data processed and by collecting data using surveys to understand the driving (abiotic and biotic) factors of vegetated land changes and land dynamics at regional level. These spatial and survey data are then to be employed at spatially explicit analysis to define driving factors, plausibly explaining spatial and temporal changes of vegetated land on coastal surfaces in the Southeastern European study region including Albania, Bosnia and Herzegovina, Croatia, and Montenegro and Slovenia for the last fifty years. The expected main result is a new methodological approach of remote sensing spatial data analysis by reducing data errors and by identifying driving factors of the changes in vegetated land. The findings may serve to understand the effects of land management and land use on the changes in vegetated land-use on coastal surfaces, and to potentially uncover the least disturbed vegetated land areas by human intervention for vegetation conservation.

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Adaptive niche‐based sampling to improve ability to find rare and elusive species: Simulations and field tests

Cited by 17 publications

References 51 publications

Effect of sampling strategies on the response curves estimated by plant species distribution models

Effect of sampling strategies on the response curves estimated by plant species distribution models

Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Remote Sensing Analysis of the Changes in Vegetated Coastland: The Case of Southeastern Europe

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