Using temporal occupancy to predict avian species distributions

Taylor, Sara J. Snell; Cecco, Grace J. Di; Hurlbert, Allen H.

doi:10.1111/ddi.13296

Cited by 5 publications

(4 citation statements)

References 59 publications

(101 reference statements)

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“…When w i (weight) of the best model was below 0.80, we used model averaging in the model.avg function in the MuMIn package to calculate the model-averaged estimates, following the protocol described by Burnham et al (2011). We used root mean square error (RMSE) to validate each model, considering RMSE closest to zero as models with a good fit (Norberg et al, 2019;Tobler et al, 2019;Snell Taylor et al, 2021). We assessed the importance of the explanatory variables by evaluating their estimates, unconditional standard errors and 95% confidence intervals (CIs) in the averaged model.…”

Section: Discussionmentioning

confidence: 99%

“…( 2011 ). We used root mean square error (RMSE) to validate each model, considering RMSE closest to zero as models with a good fit (Norberg et al ., 2019 ; Tobler et al ., 2019 ; Snell Taylor et al ., 2021 ). We assessed the importance of the explanatory variables by evaluating their estimates, unconditional standard errors and 95% confidence intervals (CIs) in the averaged model.…”

Section: Methodsmentioning

confidence: 99%

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Host life-history traits predict haemosporidian parasite prevalence in tanagers (Aves: Thraupidae)

et al. 2022

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Vector-borne parasites are important ecological drivers influencing life-history evolution in birds by increasing host mortality or susceptibility to new diseases. Therefore, understanding why vulnerability to infection varies within a host clade is a crucial task for conservation biology and for understanding macroecological life-history patterns. Here, we studied the relationship of avian life-history traits and climate on the prevalence of Plasmodium and Parahaemoproteus parasites. We sampled 3569 individual birds belonging to 53 species of the family Thraupidae. Individuals were captured from 2007 to 2018 at 92 locations. We created 2 phylogenetic generalized least-squares models with Plasmodium and Parahaemoproteus prevalence as our response variables, and with the following predictor variables: climate PC1, climate PC2, body size, mixed-species flock participation, incubation period, migration, nest height, foraging height, forest cover, and diet. We found that Parahaemoproteus and Plasmodium prevalence was higher in species inhabiting open habitats. Tanager species with longer incubation periods had higher Parahaemoproteus prevalence as well, and we hypothesize that these longer incubation periods overlap with maximum vector abundances, resulting in a higher probability of infection among adult hosts during their incubation period and among chicks. Lastly, we found that Plasmodium prevalence was higher in species without migratory behaviour, with mixed-species flock participation, and with an omnivorous or animal-derived diet. We discuss the consequences of higher infection prevalence in relation to life-history traits in tanagers.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Host life-history traits predict haemosporidian parasite prevalence in tanagers (Aves: Thraupidae)

et al. 2022

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“…Species distribution models (SDMs) have been widely used for prediction of the potential impact of invasive species to allow the adoption of preventive control measures. 6,29,30 Compared to other SDMs, such as GARP, DOMAIN and BIOCLIM, the maximum entropy (MaxEnt) model uses presence-only data, and is less sensitive to the sample size, which especially performs well in the case of small sample size. 14,[31][32][33][34] Moreover, climate change can have a direct effect on the distribution and abundance of these invasive insects, and also can indirectly influence the population growth rates, propagule pressure, and spread.…”

Section: Introductionmentioning

confidence: 99%

“…Species distribution models (SDMs) have been widely used for prediction of the potential impact of invasive species to allow the adoption of preventive control measures 6,29,30 . Compared to other SDMs, such as GARP, DOMAIN and BIOCLIM, the maximum entropy (MaxEnt) model uses presence‐only data, and is less sensitive to the sample size, which especially performs well in the case of small sample size 14,31–34 .…”

Section: Introductionmentioning

confidence: 99%

Current and future potential distribution of the invasive scale Ceroplastes rusci (L., 1758) (Hemiptera: Coccidae) under climate niche

SHAN

Gao

et al. 2022

Pest Management Science

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BACKGROUND:The fig wax scale, Ceroplastes rusci is an invasive pest that feeds on more than 94 genera from 52 families that is spread across 60 countries, causing negative impacts to agriculture and forestry. Understanding the potential distribution of invasive species under climate change is crucial for the management and monitoring purposes. Thus, we predicted the potential distribution areas of C. rusci using Maximum Entropy (MaxEnt) based on the occurrence data and environmental variables under current and future climatic scenarios. RESULTS: Our results showed that the temperature annual range (Bio 7) and mean temperature of the warmest quarter (Bio 10) attributed to a higher contribution to the current model of the distribution of C. rusci. The potential distribution maps illustrated the main concentrated areas of C. rusci which included South America, Africa, Asia, and Oceania. In addition, potential range expansions or reductions were predicted under different future climate change scenarios, which showed that the total suitable areas of the fig wax scale presented an increasing trend until 2100.CONCLUSION: Our study provides significant data to understand the potential distribution of C. rusci around the world. It also serves as an early warning for the highly suitable habitat areas that even offers a platform to the currently non-infested regions or countries who are yet to develop monitoring strategies in response to the possible C. rusci outbreak.

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Which bird traits most affect the goodness-of-fit of species distribution models?

Morelli,

Benedetti,

Stanford

et al. 2024

Ecological Indicators

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Using temporal occupancy to predict avian species distributions

Abstract: One of the primary methods for classifying a species range is the species distribution model (SDM), which predicts where species are most likely to occur based on a set of environmental factors (Freeman &

Cited by 5 publications

References 59 publications

Host life-history traits predict haemosporidian parasite prevalence in tanagers (Aves: Thraupidae)

Host life-history traits predict haemosporidian parasite prevalence in tanagers (Aves: Thraupidae)

Current and future potential distribution of the invasive scale Ceroplastes rusci (L., 1758) (Hemiptera: Coccidae) under climate niche

Which bird traits most affect the goodness-of-fit of species distribution models?

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