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Habitat suitability predictions for a boreal forest indicator species, the northern goshawk (Accipiter gentilis), in Central Finland
<p>This repository contains files that show optimal sites in Central Finland for the northern goshawk (<em>Accipiter gentilis</em>, hereafter goshawk), an indicator species of boreal forests with conservation values. The optimal sites were derived from the habitat suitability model outputs included in the following publication:</p> <p> </p> <p><strong>Björklund Heidi<sup>a</sup>, Parkkinen Anssi<sup>b</sup>, Hakkari Tomi<sup>c</sup>, Heikkinen Risto K.<sup>d</sup>, Virkkala Raimo<sup>d</sup>, Lensu Anssi<sup>b</sup> (2020): Predicting valuable forest habitats using an indicator species for biodiversity. Biological Conservation, </strong><a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> . </p> <p> </p> <p><sup>a</sup> Finnish Museum of Natural History Luomus, P.O. Box 17, FI-00014 University of Helsinki, Finland</p> <p><sup>b</sup> University of Jyvaskyla, Department of Biological and Environmental Science, P.O. Box 35, FI-40014 University of Jyvaskyla, Finland</p> <p><sup>c</sup> Centre for Economic Development, Transport and the Environment Central Finland, P.O. Box 250, FI-40101 Jyväskylä, Finland</p> <p><sup>d</sup> Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p> <p> </p> <p>The files are ArcGIS compatible shape files which indicate the spatial location of the 160 m × 160 m grid cells which include forest stands projected to be either highly suitable or suitable as a nesting site for the goshawk in Central Finland. The habitat suitability models and values were developed across the study area using Maxent software. The files show those 160-m grid cells from the study area which were included in one of the following two categories: (i) cells deemed as the most optimal (with high probability of suitable conditions) for goshawk nesting with suitability index values in Maxent outputs varying between 0.92–1.00 (‘best’ goshawk squares), and (ii) cells deemed as ‘good’ goshawk squares (with Maxent suitability index values of ≥ 0.69 and < 0.92). The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). </p> <p>Summarization of the key settings and elements of the study are provided below. A detailed treatment can now be found in the article published in Biological Conservation (Björklund et al.) for which the link is the following: <a href="https://doi.org/10.1016/j.biocon.2020.108682">https://doi.org/10.1016/j.biocon.2020.108682</a> .</p> <p> </p> <p><strong>Summary of the study</strong></p> <p>Intensive commercial use of boreal forests is an accelerating threat to forest biodiversity, highlighting the development of cost-effective tools to detect the locations valuable for conservation. We applied species distribution models (SDMs) in our study area, Central Finland, to locate the optimal nesting sites for the goshawk, an indicator bird species for biodiversity hotspots in mature boreal forests. The optimal sites (here, 160 x 160 m grid squares) for the goshawk were determined using the Maxent software. Optimal squares for the goshawk had forests with considerably high volumes of Norway spruce (<em>Picea abies</em>, hereafter spruce) covering only 3.4% of the boreal landscape, and they were located mostly outside protected areas. Many of the squares with optimal nesting forests appeared to be under threat due to recently intensified logging operations. Half of the squares were logged to some extent and 10% were already lost or notably deteriorated due to logging after 2015 for which our models were calibrated. Threats to biodiversity of mature boreal spruce forests are likely to accelerate with increasing logging pressures. Thus, there is an urgent need to secure the continuous supply of mature spruce forests in the landscape by developing a denser network of protected areas and applying measures that aid in sparing large entities of mature forest on privately-owned land. Our modelled optimal squares can be used for selection of potential areas with biodiversity values in conservation prioritization.</p> <p><strong>The study species</strong></p> <p>The goshawk is a raptor species which prefers mature forests for nesting in Europe. Old forests dominated by spruce are considered as important for the breeding success of the species particularly in northern latitudes. Thus, intensive forest management can impair the breeding possibilities of the goshawk, and changes in forest landscapes are likely to contribute to the decline of the species. For example, in Finland, the goshawk is classified as nearly threatened species. In our study, we used the goshawk as an indicator species to model the spatial locations of boreal forest with much potential for including biodiversity values. The indicator species status of the goshawk is based on earlier studies showing the close association of the goshawk with various taxa of mature spruce forest, as well as the reported declines of both the goshawk and associated species due to loggings.</p> <p><strong>Developing Maxent models for the goshawk</strong></p> <p>The location data on occupied nests of the goshawk gathered in spring and summer 2015 and 2016 in Central Finland – as a part of the Finnish Common Birds of Prey Monitoring – were related to a set of environmental predictor variables using a maximum entropy method, Maxent software, which is considered particularly useful for modelling presence-only data (such as our goshawk nest site data). In our case, the data on forest stand and tree characteristics were related using Maxent to the known nesting sites to predict suitable conditions for the species across the Central Finland. The forest data used in the modelling were extracted from the multi-source national forest inventory (MS-NFI) data sources governed by the Natural Resources Institute Finland. The MS-NFI data used in our modelling are based on field data of the 11th and 12th NFIs from 2009 to 2016 and satellite images from 2015 and 2016.</p> <p>Prior modelling, Pearson correlations were calculated between the continuous environmental variables at the nest sites. Of the highly (|r| ≥ 0.7) correlated variables, we chose those variables which are known to be important for the goshawk, which are useful for generalization in other areas, or whose impact was of specific interest. Our final selected set of predictor variables included one class variable, site fertility class, and nine continuous variables: growing stock volume of the spruce, pine, birches and other hardwood, canopy cover, canopy cover of broad-leaved trees, saw timber of other broad-leaved trees than birches, pulpwood volume of the birches, and the biomass of the stem residual of the spruce. The original MS-NFI data recorded at the resolution of 16 × 16 m were resampled to the resolution of 160 × 160 m for the Maxent models, to represent one potential nesting forest stand.</p> <p>The accuracy of Maxent models were assessed with cross-validation and associated averaged AUC-values. The relative importance of the variables was measured by variable contribution and model deterioration measures provided by Maxent. The cloglog-transformed output index values ranging from 0 to 1 described the relative suitability of the 160-m squares to goshawk nesting. Based on the index values, the squares were classified as ‘optimal’ (with index values of 0.69–1.00), ‘typical’ (0.46– <0.69) and ‘poor’ (<0.46). In addition, we divided optimal squares into ‘best’ goshawk squares (index values of 0.92–1.00 corresponding to a high probability of suitable conditions), and ‘good’ goshawk squares (index values ≥ 0.69 and < 0.92).</p> <p><strong>Maxent model outputs</strong></p> <p>Spruce volume was the most important variable in defining habitat suitability for goshawk nesting, but hardwood cover, other hardwood logs and site fertility class contributed also to some extent to habitat suitability. In Maxent outputs, the set of 160-m squares deemed as optimal for goshawk nesting included 6 895 (cover 0.9% of the study area) best goshawk squares and 19 421 (cover 2.5%) good goshawk squares. The projected best and good goshawk squares were mostly located in unprotected areas: 95.0% of the best and 96.0% of the good goshawk squares occurred completely outside protected areas. For further details concerning the data and the model outputs, see the referred article Björklund et al. (2020).</p> <p><strong>State of the optimal goshawk squares</strong></p> <p>In total, 11% of best and over 9% of good goshawk squares were severely altered due to recent harvesting, typically clear-cutting, of the forests during the time period between 2015 and 2019. Altogether, some level of logging occurred in 3 062 (44%) of best goshawk and 9 846 (51%) of good goshawk squares during the recent years. However, many of the squares still included enough unlogged area for the goshawk in 2019.</p> <p>In our article, we conclude that while most of the optimal squares for the goshawk were still preserved in 2019, they are under risk as they are mainly situated outside protected area network. This stresses the importance of conserving biodiversity with complementary measures in privately-owned managed forests. In conclusion, a denser network with more PAs for forest-dwelling species should be secured in areas with intensive forestry, e.g. in southern Finland where PAs currently cover a smaller proportion of land compared to northern Finland.</p>
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized. in A new species of freshwater Chaetonotidae (Gastrotricha, Chaetonotida) from Obodska Cave (Montenegro) based on morphological and molecular characters
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized.
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site.
Fig.5 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.5. Jamesoniella autumnalis distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.3 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.3. Neckera pennata distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzemes, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.1 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.1. Anomodon longifolius distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical reģions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumnkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J-upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N-upland Latgale, O-Austrumlatgale.
Fig.4 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.4. Lejeunea cavifolia distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.2 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.2. Homalia trichomanoides distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig. 3 in Hrabeiella Periglandulata (Annelida: "Polychaeta") Do Apparent Differences In Chaetal Ultrastructure Indicate The Existence Of Several Species In Europe?
Fig. 3. Hrabeiellaperiglandulata, chaetae, SEMmicrograph (thewormswerefixedinetha- nol, thespecimenfromSpainfixedinBouinsolution): A = anteriorpartofbodywiththree rowsofchaetaeofaspecimenfromBakonyMts, Hungary (ventro-lateralview), B = lateral chaetaeinthesecondrow (specimenfromBrno, CzechRepublic), C = chaetaeinthesecond row (ventralview, specimenfromVěncováhora (locustypicus), CzechRepublic), D = cha-
Fig. 1 in Hrabeiella Periglandulata (Annelida: "Polychaeta") Do Apparent Differences In Chaetal Ultrastructure Indicate The Existence Of Several Species In Europe?
Fig. 1. Distributionof Hrabeiellaperiglandulata inEuropebasedonrecordspreviouslypublished (circles) orreportedinthisstudy (squares).
Fig. 4 in Hrabeiella Periglandulata (Annelida: "Polychaeta") Do Apparent Differences In Chaetal Ultrastructure Indicate The Existence Of Several Species In Europe?
Fig. 4. SEMmicrographsof Hrabeiellaperiglandulata chaetaeshowingadifferentshape (the wormswerefixedonlyinethanol): A = LateralchaetaeofaspecimenfromHungary. B = Lateralchaetaeinthethirdrowofaspecimenfromnorth-westernCzechia. C = Ventral chaetaeofthelastrowsofaspecimenfromHungary (BakonyMts). D = Chaetaeinthefifth
Great tits (Parus major) flexibly learn that herbivore-induced plant volatiles indicate prey location – an experimental evidence with two tree species
<p>1. When searching for food, great tits (Parus major) can use herbivore-induced plant volatiles (HIPVs) as an indicator of arthropod presence. Their ability to detect HIPVs was shown to be learned, and not innate, yet the flexibility and generalization of learning remains unclear. 2. We studied if, and if so how, naïve and trained great tits (Parus major) discriminate between herbivore-induced and non-induced saplings of Scotch elm (Ulmus glabra) and cattley guava (Psidium cattleyanum). We chemically analysed the used plants and showed that their HIPVs differed significantly and overlapped only in a few compounds. 3. Birds trained to discriminate between herbivore-induced and non-induced saplings preferred the herbivore-induced saplings of the plant species they were trained to. Naïve birds did not show any preferences. Our results indicate that the attraction of great tits to herbivore-induced plants is not innate, rather it is a skill that can be acquired through learning, one tree species at a time. 4. We demonstrate that the ability to learn to associate HIPVs with food reward is flexible, expressed to both tested plant species, even if the plant species has not coevolved with the bird species (i.e. guava). Our results imply that the birds are not capable of generalising HIPVs among tree species but suggest that they either learn to detect individual compounds or associate whole bouquets with food rewards.</p>
Congruence among multiple indices of habitat preference for species facing human-induced rapid environmental change: A case study using the Brewer's sparrow
<p>Accurate evaluations of habitat preference are key to understanding optimal conditions for wildlife survival and reproduction. Habitat selection, however, usually is evaluated using a single index of preference, and congruence among multiple, relevant indices of preference is examined rarely.</p> <p>We assessed the concordance between patterns of habitat preference using three different indices of breeding site preference in a migratory songbird. Specifically, we compared the chronology of territorial establishment, pair formation, and reproductive initiation of the Brewer's sparrow (<em>Spizella breweri</em>) along a gradient of surface disturbance associated with natural gas development in Wyoming, USA during 2019.</p> <p>We expected all three indices to demonstrate a preference for breeding sites with less surface disturbance, where reproductive success typically is higher. By contrast, all indices suggested suboptimal preference with respect to surface disturbance, with some discrepancy among them. The chronology of settlement and pairing did not vary across the disturbance gradient, whereas nest initiation tended to occur earlier at sites with more disturbance.</p> <p>If the pattern of suboptimal selection of breeding sites that we identified is generalizable across other populations of migratory birds affected by energy development, the resultant lower fitness in those areas may exacerbate population declines.</p> <p>Our results suggest that traditional, single-index approaches to the study of habitat selection, if chosen carefully, may provide adequate inference on habitat preferences. Different metrics, however, can lead to at least subtle differences in patterns of habitat selection. The simultaneous examination of multiple indices of preference across a diversity of systems would help clarify the contexts under which preference metrics can become decoupled.</p>
Text-fig. 3. Tendency of changes in IPM and PE width indicators in different types of enamel of the Equidae species of the "tarpan" group. I–III – types of enamel. a: IPM; b: PE. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 3. Tendency of changes in IPM and PE width indicators in different types of enamel of the Equidae species of the "tarpan" group. I–III – types of enamel. a: IPM; b: PE.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Figure 2. A in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 2. A) Phylogenetic tree generated using Bayesian (MrBayes; Huelsenbeck and Ronquist 2001), maximum likelihood (RAxML; Version 8.1.17, Stamatakis 2006), and parsimony methods (PAUP* v. 4.0a165, Swofford 2002) and DNA sequence data from the mitochondrial cytochrome-b gene. The topology depicted is from the Bayesian analysis. Clade probability values (≥ 0.95) for the Bayesian analysis are indicated by an asterisk (*) and are to the left of the first slash, bootstrap values for the maximum likelihood analysis are shown between the two slashes, and bootstrap values obtained from the parsimony analysis are to the right of the last slash. Line at bottom of figure depicts the nucleotide substitution rate per site per million years. B) Same phylogenetic tree as depicted in Figure 2A except unsupported nodes (C, G, and H) were collapsed.
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.
Figure 1 in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 1. Distribution of selected populations and species of the Peromyscus maniculatus species group from Canada, Mexico, and the United States. Shaded areas represent distributions of taxa (defined in figure insert) as originally defined by Hall (1981) and modified based on the results of this study. Closed circles represent collecting localities listed in the Appendix; note that multiple individuals may be represented by a single closed circle. White boxes with black stars indicate type localities for each taxon and triangles indicate localities where haplotypes representing P. sonoriensis were found to be in sympatry with samples of P. gambelii and P. labecula, respectively.
Figure 3 in Mitochondrial Dna Sequence Data Indicate Evidence For Multiple Species Within Peromyscus Maniculatus
Figure 3. Time-calibrated ultrametric tree obtained from the BEAST analysis (Version 2.4, Bouckaert et al. 2014) of the mitochondrial cytochrome-b gene dataset. Scale bars at nodes represent the 95% highest posterior densities and numbers associated to each node are the estimated divergence times in million years ago.
Variable species establishment in response to microhabitat indicates different likelihoods of climate-driven range shifts
<p>Climate change is causing geographic range shifts globally, and understanding the factors that influence species' range expansions is crucial for predicting future biodiversity changes. A common, yet untested, assumption in forecasting approaches is that species will shift beyond current range edges into new habitats as they become macroclimatically suitable, even though microhabitat variability could have overriding effects on local population dynamics. We aim to better understand the role of microhabitat in range shifts in plants through its impacts on establishment by Q1) examining microhabitat variability along large macroclimatic (i.e., elevational) gradients, Q2) testing which of these microhabitat variables explain plant recruitment and seedling survival, and Q3) predicting microhabitat suitability beyond species range limits. We transplanted seeds of 25 common tree, shrub, forb, and graminoid species across and beyond their current elevational ranges in the Washington Cascade Range, USA, along a large elevational gradient spanning a broad range of macroclimates. Over five years, we recorded recruitment, survival, and microhabitat (i.e., high resolution soil, air, and light) characteristics rarely measured in biogeographic studies. We asked whether microhabitat variables correlate with elevation, which variables drive species establishment, and whether microhabitat variables important for establishment are already suitable beyond leading range limits. We found that only 30% of microhabitat parameters covaried with elevation. We further observed extremely low recruitment and moderate seedling survival, and these were generally only weakly explained by microhabitat. Moreover, species and life stages responded in contrasting ways to soil biota, soil moisture, temperature, and snow duration. Microhabitat suitability predictions suggest that distribution shifts are likely to be species-specific, as different species have different suitability and availability of microhabitat beyond their present ranges, thus calling into question low-resolution macroclimatic projections that will miss such complexities. We encourage further research on species responses to microhabitat and including microhabitat in range shift forecasts.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.