scholarly journals The Influence of the Interaction between Climate and Competition on the Distributional Limits of European Shrews

Animals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 57
Author(s):  
Tomé Neves ◽  
Luís Borda-de-Água ◽  
Maria da Luz Mathias ◽  
Joaquim T. Tapisso
Keyword(s):  
Species Distribution ◽  
Ecological Factors ◽  
Biotic Interactions ◽  
Species Distributions ◽  
Climate Data ◽  
Distribution Models ◽  
Presence Data ◽  
Geographical Scale ◽  
The Impact ◽  
Distributional Limits

It is known that species’ distributions are influenced by several ecological factors. Nonetheless, the geographical scale upon which the influence of these factors is perceived is largely undefined. We assessed the importance of competition in regulating the distributional limits of species at large geographical scales. We focus on species with similar diets, the European Soricidae shrews, and how interspecific competition changes along climatic gradients. We used presence data for the seven most widespread terrestrial species of Soricidae in Europe, gathered from GBIF, European museums, and climate data from WorldClim. We made use of two Joint Species Distribution Models to analyse the correlations between species’ presences, aiming to understand the distinct roles of climate and competition in shaping species’ distributions. Our results support three key conclusions: (i) climate alone does not explain all species’ distributions at large scales; (ii) negative interactions, such as competition, seem to play a strong role in defining species’ range limits, even at large scales; and (iii) the impact of competition on a species’ distribution varies along a climatic gradient, becoming stronger at the climatic extremes. Our conclusions support previous research, highlighting the importance of considering biotic interactions when studying species’ distributions, regardless of geographical scale.

scholarly journals How much is enough? Influence of number of presence observations on the performance of species distribution models

Sommerfeltia ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Bente Støa ◽  
Rune Halvorsen ◽  
Jogeir N. Stokland ◽  
Vladimir I. Gusarov
Keyword(s):  
Species Distribution ◽  
Reference Model ◽  
Null Model ◽  
Species Distributions ◽  
Reference Distribution ◽  
Distribution Models ◽  
Rare And Endangered Species ◽  
Full Dataset ◽  
Presence Data ◽  
Rare And Endangered

Abstract Species distribution modeling (SDM) can be useful for many applied purposes, e.g., mapping and monitoring of rare and endangered species. Sparse presence data are a recurrent, major obstacle to precise modeling of species distributions. Thus, knowing the minimum number of presences required to obtain reliable distribution models is of fundamental importance for applied use of SDM. This study uses a novel approach to assess the critical sample size (CSS) sufficient for an accurate prediction of species distributions with Maximum Entropy Modeling (MaxEnt). Large presence datasets for thirty insect species, ranging from generalists to specialists regarding their responses to main bioclimatic gradients, were used to produce reference distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity (IVS). Two thresholds for IVS were determined based on comparison of nine reference models to random null models. The threshold values correspond to 0.95 and 0.99 probability that a model outperforms a random null model in terms of similarity to the reference dataset. For 90% of the species, clearly nonrandom models were obtained with less than 10 presence observations, and for 97% of the species with less than 15 presence observations. We conclude that the number of presence observations required to produce nonrandom models is generally low and, accordingly, that even sparse datasets may be useful for distribution modelling.

scholarly journals Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type

Climate ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 37 ◽  
Author(s):  
Catherine Jarnevich ◽  
Nicholas Young
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Model Parameters ◽  
Climate Data ◽  
Distribution Models ◽  
Model Type ◽  
Time Periods ◽  
The Impact

Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

scholarly journals Modeling the Impact of Climate Change on the Distribution of Hagenia Abyssinica in Ethiopia

2020 ◽  
Author(s):  
Tsegaye Gobezie ◽  
Silesh Namomissa ◽  
Tamrat Bekele
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Climate Change Impacts ◽  
Climatic Conditions ◽  
Climate Data ◽  
Distribution Models ◽  
Hagenia Abyssinica ◽  
Bioclimatic Variables ◽  
Conservation Actions ◽  
The Impact

Research Highlights: Hagenia abyssinica is geographically localized, poor regenerated and endangered species in Ethiopia. Ethiopia has been experiencing variability of rainfall and rise in temperature due to the climate change. This study has hypothesized that the suitable areas for the species will be narrowed by the year 2070. Background and Objective The prediction of species distribution models help to implement appropriate conservation actions. The aim of this research was to identify the current and likely future distribution range and suitable areas for the species, and to determine the presence of H. absyssinica in risk in a short-term future. Material and method: To this end, occurrence data, bioclim variables, soil, elevation, and land cover map of Ethiopia were used. MaxEnt was used to predict distribution. Climate change impacts on the distribution of the species was performed using bioclimatic variables of the future climate data, 2070 (average for 2061-2080) was obtained from IPPC5 (CMIP5) at 30 seconds (1km) spatial resolution. The climate data was projected from GCMs, downscaled and calibrated using rcp4.5. Results: Both current and likely future distribution models were excellent and significantly better than random performance. This study has computed 59987 km2 to be the low impact area for the species under current conditions and will remain habitat under future climates and 39025 km2 area has been identified as the possible high impact areas or declining habitat. The model has also determined that 1238724 km2 of the areas are unsuitable at present and for future climates. The current study found that 15751 km2 of the area will be modified as a new suitable area for H. abyssinica due to climate change. Conclusion: Species distribution modeling is essential for the implementation of conservation actions that are compatible with the inevitable changing climatic conditions of the country.

scholarly journals Accounting for biotic interactions through alpha‐diversity constraints in stacked species distribution models

2017 ◽  
Vol 8 (9) ◽  
pp. 1092-1102 ◽  
Author(s):  
Yoni Gavish ◽  
Charles J. Marsh ◽  
Mathias Kuemmerlen ◽  
Stefan Stoll ◽  
Peter Haase ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Alpha Diversity ◽  
Distribution Models

scholarly journals Environmental factors influencing fine-scale distribution of Antarctica’s only endemic insect

Oecologia ◽  
2020 ◽  
Vol 194 (4) ◽  
pp. 529-539
Author(s):  
Leslie J. Potts ◽  
J. D. Gantz ◽  
Yuta Kawarasaki ◽  
Benjamin N. Philip ◽  
David J. Gonthier ◽  
...  
Keyword(s):  
Antarctic Peninsula ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Ecological Factors ◽  
Biotic Interactions ◽  
Species Distributions ◽  
Biotic Factors ◽  
Belgica Antarctica ◽  
Abiotic And Biotic Factors ◽  
The Antarctic

AbstractSpecies distributions are dependent on interactions with abiotic and biotic factors in the environment. Abiotic factors like temperature, moisture, and soil nutrients, along with biotic interactions within and between species, can all have strong influences on spatial distributions of plants and animals. Terrestrial Antarctic habitats are relatively simple and thus good systems to study ecological factors that drive species distributions and abundance. However, these environments are also sensitive to perturbation, and thus understanding the ecological drivers of species distribution is critical for predicting responses to environmental change. The Antarctic midge, Belgica antarctica, is the only endemic insect on the continent and has a patchy distribution along the Antarctic Peninsula. While its life history and physiology are well studied, factors that underlie variation in population density within its range are unknown. Previous work on Antarctic microfauna indicates that distribution over broad scales is primarily regulated by soil moisture, nitrogen content, and the presence of suitable plant life, but whether these patterns are true over smaller spatial scales has not been investigated. Here we sampled midges across five islands on the Antarctic Peninsula and tested a series of hypotheses to determine the relative influences of abiotic and biotic factors on midge abundance. While historical literature suggests that Antarctic organisms are limited by the abiotic environment, our best-supported hypothesis indicated that abundance is predicted by a combination of abiotic and biotic conditions. Our results are consistent with a growing body of literature that biotic interactions are more important in Antarctic ecosystems than historically appreciated.

scholarly journals Improving species distribution models using biotic interactions: a case study of parasites, pollinators and plants

Ecography ◽  
2012 ◽  
Vol 36 (6) ◽  
pp. 649-656 ◽  
Author(s):  
Tereza Cristina Giannini ◽  
Daniel S. Chapman ◽  
Antonio Mauro Saraiva ◽  
Isabel Alves-dos-Santos ◽  
Jacobus C. Biesmeijer
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Biotic Interactions ◽  
Distribution Models

scholarly journals From Remote Sensing to Species Distribution Modelling: An Integrated Workflow to Monitor Spreading Species in Key Grassland Habitats

2021 ◽  
Vol 13 (10) ◽  
pp. 1904
Author(s):  
Walter De Simone ◽  
Marina Allegrezza ◽  
Anna Rita Frattaroli ◽  
Silvia Montecchiari ◽  
Giulio Tesei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Abiotic Factors ◽  
Natura 2000 ◽  
Distribution Model ◽  
Overlay Analysis ◽  
Distribution Models ◽  
Habitat Directive ◽  
The Impact

Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and several human-induced processes threaten them. The broad expansions of few dominant species are usually reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species in the Central Apennine: Brachypodium genuense. We performed a binary Random Forest (RF) classification of B. genuense using RS images and field-sampled presence/absence data. Then, we integrate the occurrences obtained from RS classification into species distribution models to identify the topographic drivers of B. genuense distribution in the study area. Lastly, the impact of B. genuense distribution in the Natura 2000 (N2k) habitats (Annex I of the European Habitat Directive) was assessed by overlay analysis. The RF classification process detected cover of B. genuense with an overall accuracy of 94.79%. The topographic species distribution model shows that the most relevant topographic variables that influence the distribution of B. genuense are slope, elevation, solar radiation, and topographic wet index (TWI) in order of importance. The overlay analysis shows that 74.04% of the B. genuense identified in the study area falls on the semi-natural dry grasslands. The study highlights the RS classification and the topographic species distribution model’s importance as an integrated workflow for mapping a broad-expansion species such as B. genuense. The coupled techniques presented in this work should apply to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.

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