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991 results for “Species distribution data”
Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
<p>Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.</p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
<p>The representation of a land cover type (i.e., habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g., wetlands) while for common habitats (e.g., forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e., the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g., using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology, and ecological conservation.</p>
Fig. 4 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 4. Distribution of wetlands number depending on number of species in them (axis X — number of species, axis Y — number of wetlands).
Fig. 5 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 5. Distribution of wetlands depending on species number (axis X) and average amount of birds in them (axis Y).
Fig. 1–4 in Redescription And New Data On Distribution Of Glyphomerus Flavabdomen With Key To The Palearctic Species Of The Genus Glyphomerus (Hymenoptera, Torymidae)
Fig. 1–4: Glyphomerus flavabdomen Zerova: 1 — female, lateral view; 2 — abdomen and ovipositor; 3 — fore wing venation; 4 — antenna.
Code and data for "Species traits associated with rapid shifts in elevational distributions of Swiss birds"
<p><strong>Abstract</strong><br> Global change in climate and land use have profound effects on species’ geographic and elevational distributions. In European birds, while species are predicted to track their climatic niches upslope, lowland agricultural intensification and high elevation land abandonment can drive elevational shifts. Species traits that can predict response to change in climate and land use can inform conservation, but thorough examination of their relationships with elevational shifts in European birds are lacking. We estimate change in the elevational distributions of 71 species from 1996 to 2016 in a region of the western Palearctic with wide elevational gradients (approximately 3,000 m) and large changes in temperature. We model the relationships between elevational shifts and species traits associated with resource preference and adaptive capacity at five reference points including the cool edge, warm edge, and the core of species’ elevational distributions. When intermediate reference points were removed changes to the results were negligible, indicating that three reference points are likely sufficient. We found significant upslope and downslope shifts in 56% and 23% of our study species, respectively. Asymmetric rates of shifts in the cool and warm edges caused significant contractions in elevational extent in 30% of our study species. The effect of elevational preference (i.e. midpoint elevation) was habitat dependent. Movement in alpine birds was unidirectionally upslope, with nearly half displaying significant or apparent elevational range contractions. In woodland birds, asymmetries of shifts in reference points led to expansions in extent in low elevation species and contractions in high elevation species. Generally, migrants, species with smaller mass, smaller relative brain size, smaller hand-wing index, and generalists in diet, habitat, and elevation had greater upslope shifts. While elevational shifts in European birds were heterogenous and species-specific, many were rapid, and species traits associated with resource preference and adaptive capacity were associated with common patterns of elevation.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"
<p>All the data needed to reproduce the results and Figures of our article:</p> <p>Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. <em>Biology</em>, <em>11</em>(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a></p> <p>Please, find the R scripts and guidelines to reproduce our results on the article's Github repository :</p> <p><a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a></p>
Figure 4. Approximate distributions and associated divergence times for A in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 4. Approximate distributions and associated divergence times for A) Peromyscus maniculatus-like ancestor; B) P. melanotis-like ancestor; C) P. gambelii/keeni/sejugis/sp.-like ancestor; D) P. polionotus-like ancestor; E) P. sonoriensis-like ancestor; F) P. labecula and P. maniculatus - like ancestor; G) P. keeni/sp.-like ancestor; and H) P. keeni-like, P. gambelii-like, P. sejugis-like, and P. sp.-like ancestors. Divergence times were estimated from the BEAST analysis (Version 2.4, Bouckaert et al. 2014) of the mitochondrial cytochrome-b gene dataset (see Fig. 3). Shading schemes that correspond to species distributions are shown in the inset.
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
APPENDIX 3 in Molecular data reveal the presence of three Plocamium Lamouroux species with complex patterns of distribution in Southern Chile
APPENDIX 3. — Automatic Barcode Gap Discovery (ABGD) results and distribution of pairwise distances for the marker 5P-COI. A, ABGD results showing the number of groups (primary partitions) obtained for a range of prior maximum divergence of intraspecific diversity; B, bar chart showing the proportion of pairwise comparisons of 5P-COI gene at each range of sequence divergence (K2P distance). Intraspecific divergences are represented in grey bars and divergences belonging to different species are represented in yellow bars.
APPENDIX 4. — Ultrametric Bayesian tree reconstructed with the 5P in Molecular data reveal the presence of three Plocamium Lamouroux species with complex patterns of distribution in Southern Chile
APPENDIX 4. — Ultrametric Bayesian tree reconstructed with the 5P-COI marker. The dotted vertical red line indicates the maximum likelihood transition point of the switch in branching rates, as estimated by a General Mixed Yule-Coalescent (GMYC) model. The GMYC analysis was performed using a single threshold. Haplotype code as in Appendix 5.
APPENDIX 1 in Molecular data reveal the presence of three Plocamium Lamouroux species with complex patterns of distribution in Southern Chile
APPENDIX 1. — Maximum likelihood (ML) phylogram of the genus Plocamium Lamouroux based on rbcL sequences. ML bootstrap (BS)/Bayesian posterior probability (PP) values are shown above or close to each branch and only values superior to 75 and 0.75, respectively, are given. Colors correspond to oceans where individuals sequenced where sampled. Outgroup corresponds to Sarcodia ciliata Zanardini (GenBank accession: KM360040).
FIG. 3. — A in Molecular data reveal the presence of three Plocamium Lamouroux species with complex patterns of distribution in Southern Chile
FIG. 3. — A, ML tree (left) and Neighbour joining (NJ) network (right) inferred from 5P-COI sequences dataset of Plocamium specimens from the present study. In the tree, numbers above the branches are support values as inferred from ML analysis, only values superior to 75 are given. In the NJ networks, haplotypes are represented by open circles with size proportional to frequency within each genetic species (see upper left corner for correspondence between number of sequences and circle size). For haplotypes separated by more than one mutational step, black bars indicate the additional number of steps; B, Plocamium Lamouroux species distribution; the number (N) of individuals sequenced is indicated for each sampling locality. Code for each locality as in Table 1; haplotype code as in Appendix 5. Dashed line represents the biogeographic transition zone located at 42°S.
FIG. 1 in Molecular data reveal the presence of three Plocamium Lamouroux species with complex patterns of distribution in Southern Chile
FIG. 1. — Maximum likelihood (ML) phylogram of the genus Plocamium Lamouroux based on 5P-COI sequences. ML bootstrap (BS)/Bayesian posterior probability (PP) values are shown above each branch and only values superior to 75 and 0.75, respectively, are given. Colors correspond to oceans where individuals sequenced where sampled. Species names, as reported in GenBank, are given on the right. Outgroup corresponds to Asparagopsis armata Harvey (GenBank accession: KJ960344).
Fig. 6. Olonia spp., distribution maps. A in Revision of the Eurybrachidae XVIII. The Australian genus Olonia Stål, 1862: Four new species, new records and biological data (Hemiptera: Fulgoromorpha)
Fig. 6. Olonia spp., distribution maps. A. Olonia albomarginata sp. nov., O. guillaumei Constant, 2018, O. lindae sp. nov. and O. rubicunda (Walker, 1851). B. Olonia aschei sp. nov., O. jackiei sp. nov. and O. picea Kirkaldy, 1906.
Fig. 3 in Quantitative Biogeographic Characterization Of Hungary Based On The Distribution Data Of Land Snails (Mollusca,Gastropoda): A Case Of Nestedness Of Species Ranges With Extensive Overlap Of Biotic Elements
Fig. 3. Distribution maps of four biotic elements found by PRABCLUS: (a) highland species, (b) general species, (c) localizes species distributed in the northern and (d) south–eastern parts of Hungary. The different shadings indicate the areas where>70%,>30%, and>0% of the species of an ele-
Fig. 1 in Quantitative Biogeographic Characterization Of Hungary Based On The Distribution Data Of Land Snails (Mollusca,Gastropoda): A Case Of Nestedness Of Species Ranges With Extensive Overlap Of Biotic Elements
Fig. 1. Biogeographic classification of Hungary based on distribution data of land snails according to the (a) hierarchical clustering of the (b) spatial units (ca. 50 km × 50 km). For clustering, the Sørensen–index and Ward–Orlóci fusion method was used. Shades of grey indicate main partitions of the cluster hierarchy, circled numbers 1–6 indicate lower level partitions mentioned in the text, numbers 1–49 identify spatial units (a) in the cluster foot and (b) in the map. Capital letters correspond to IndVal species groups listed in the text and in Appendix, lines associated to letters refer to
Fig. 2 in Quantitative Biogeographic Characterization Of Hungary Based On The Distribution Data Of Land Snails (Mollusca,Gastropoda): A Case Of Nestedness Of Species Ranges With Extensive Overlap Of Biotic Elements
Fig. 2. First two dimensions of the metric multidimensional scaling of the range data of the Hungarian land snail species. 1–4: biotic elements found by PRABCLUS; N: noise component.
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