From design to analysis: A roadmap for predicting distributions of rare species

Yoccoz, Nigel G.

doi:10.1111/gcb.16162

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…For example, the majority of invertebrates that have received Red List assessments are classified under criterion B. Yet, in our survey a high proportion of species contain too few observations to reliably estimate species distributions (van Proosdij et al, 2016;Jeliazkov et al, 2022;Yoccoz, 2022;Erickson and Smith, 2023), and 24% of the species we detect produce fewer than three observations, preventing application of Criteria B1 (using extent of occurrence as the metric of range size). Similarly, estimating population sizes for insects is often extremely difficult, which is perhaps why only 0.0016% of total insect assessments are completed under Criteria C, and 96% of D criteria assessments are completed under D2 (restricted range and limited number of locations).…”

Section: The Rarity Of Invertebrates and Applying Red List Criteriamentioning

confidence: 85%

“…Due to the inherent statistical relationship between sample size and uncertainty, low abundances or occurrences are intrinsically linked to low statistical power. Therefore, trends may be difficult to estimate without considerable degrees of uncertainty (Jeliazkov et al, 2022;Yoccoz, 2022;Erickson and Smith, 2023). To illustrate how classification using trend-based criteria may produce uncertain estimates and classification we use the empirical incidence and abundance distributions revealed by the data described in Box 1 to establish whether trends can actually be detected.…”

Section: An Empirical Example Using Trend-based Criteriamentioning

confidence: 99%

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The rarity of Invertebrates prevents reliable application of IUCN Red-List criteria.

Goodsell,

Tack,

Ronquist

et al. 2024

Preprint

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Among the most widely used information underpinning international conservation efforts is the IUCN Red List of endangered species. The Red List designates species extinction risk based on geographic range, population size, or declines in either. However, the Red-List has poor representation of invertebrates which comprise the majority of animal diversity, and it has frequently been questioned whether Red List criteria are appropriate for these organisms. Due to their small size, difficulty in identification, and general rarity, many invertebrates are hard to study, making Red List criteria difficult to apply. Here we discuss these criticisms in the context of empirical evidence from one of the largest terrestrial arthropod surveys to date, documenting the abundance and distribution of over 13,000 species in Sweden. Using simple empirical examples from these data, we argue that even the most ambitious monitoring efforts are unlikely to produce enough observations to reliably estimate population sizes and ranges for more than a fraction of species. Thus, there is likely to be substantial uncertainty in classifying most species according to current criteria. In response, we discuss the introduction of potential new IUCN criteria to more accurately capture the conservation needs of invertebrates, and to increase the representation of invertebrates on the IUCN Red List.

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Section: The Rarity Of Invertebrates and Applying Red List Criteriamentioning

confidence: 85%

Section: An Empirical Example Using Trend-based Criteriamentioning

confidence: 99%

The rarity of Invertebrates prevents reliable application of IUCN Red-List criteria.

Goodsell,

Tack,

Ronquist

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Sample size is often only somewhat under a modeler's control, either due to the inherent scarcity of a species, limited resources, or difficulty in sampling. Given the abundance of species with very little data and the urgency of understanding their niche preferences and distributions, how should modelers proceed with the rarest of the rare (Yoccoz 2022)?…”

Section: Introductionmentioning

confidence: 99%

Modeling the rarest of the rare: a comparison between multi‐species distribution models, ensembles of small models, and single‐species models at extremely low sample sizes

Erickson

Smith

2023

Ecography

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Species distribution models are useful for estimating the distribution and environmental preferences of rare species, but these same species are challenging to model on account of sparse data. We contrast a traditional single-species approach (generalized linear models, GLMs) with two promising frameworks for modeling rare species: ensembles of small models (ESMs), which average across simple models; and multispecies distribution models (MSDMs), which allow rarer species to benefit from statistical 'borrowing of strength' from more common species. Using a virtual species within a community of real species, we evaluated how model accuracy was influenced by the number of occurrences of the rare species (N = 2-64), niche breadth, and similarity to more numerous species' niches. For discriminating between presence and absence, ESMs with just linear terms (ESM-L) performed best for N ≤ 4, whereas for GLMs and ESMs with polynomial terms (ESM-P) were best for N ≥ 8. For calibrating the species' response to influential variables, the MSDM hierarchical modeling of species communities (HMSC) and ESM-P were best for species with niches similar to those of other species. For species with dissimilar niches, ESM-P did best for N ≥ 8, but no model was well calibrated for smaller sample sizes. For identifying uninfluential variables, ESM-L and species archetype models (SAMs), a type of MSDM, did well for ≤ 4, and ESM-L for N ≥ 8. Models of species with narrow niches dissimilar to others had the highest discrimination capacity compared to models for generalist species and/or species with niches similar to other species' niches. 'Borrowing of strength' in MSDMs can assist with some inference tasks, but does not necessarily improve predictions for rare species; simpler, single-species models may be better at a given task. The best algorithm depends on modeling goal (discrimination versus calibration), sample size, and niche breadth and similarity.

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“…Sample size is often only somewhat under a modeler’s control, either due to the inherent scarcity of the species or due to limited resources or difficulty in sampling. Given the abundance of species with very little data and the urgency of understanding their niche preferences and distributions, how should modelers working with the rarest of the rare proceed (Yoccoz 2022)?…”

mentioning

confidence: 99%

Modeling the rarest of the rare: A comparison between joint species distribution models, ensembles of small models, and single-species models at extremely low sample sizes

Erickson

Smith

2022

Preprint

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Determining the distribution and environmental preferences of rare species threatened by global change has long been a focus of conservation. Typical minimum suggested number of occurrences ranges from ~5 to 30, but many species are represented by even fewer occurrences. However, several newer methods may be able to accommodate such low samples sizes. These include Bayesian joint species distribution models (JSDMs) which allow rare species to statistically "borrow strength" from more common species with similar niches, and ensembles of small models (ESMs), which reduce the number of parameters by averaging smaller models. Here we explore how niche breadth and niche position relative to other species influence model performance at low sample sizes (N=2, 4, 8, 16, 32, 64) using virtual species within a community of real species. ESMs were better at discrimination tasks for most species, and yielded better-than-random accuracy even for N=2. In contrast, "traditional" single species or JSDMs were better able to estimate the underlying response curves of variables that influenced the niche, but at low sample sizes also were more likely to incorrectly identify unimportant factors as influential. Species with niches that were narrow and peripheral to the available environmental space yielded models with better discrimination capacity than species with broad niches or niches that were similar to those of other species, regardless of whether the modeling algorithm allowed for borrowing of strength. Our study suggests that some rare species may be able to be modeled reliably at very low sample sizes, although the best algorithm depends on number of occurrences and whether the niche or distribution is the focus.

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From design to analysis: A roadmap for predicting distributions of rare species

Cited by 4 publications

References 17 publications

The rarity of Invertebrates prevents reliable application of IUCN Red-List criteria.

The rarity of Invertebrates prevents reliable application of IUCN Red-List criteria.

Modeling the rarest of the rare: a comparison between multi‐species distribution models, ensembles of small models, and single‐species models at extremely low sample sizes

Modeling the rarest of the rare: A comparison between joint species distribution models, ensembles of small models, and single-species models at extremely low sample sizes

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