Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Spectral Sensitivity Measured with Electroretinogram Using a Constant Response Method

<div><p>A new method is presented to determine the retinal spectral sensitivity function <i>S</i>(<i>λ</i>) using the electroretinogram (ERG). <i>S</i>(<i>λ</i>)s were assessed in three different species of myomorph rodents, Gerbils (<i>Meriones unguiculatus</i>), Wistar rats (<i>Ratus norvegicus)</i>, and mice (<i>Mus musculus</i>). The method, called AC Constant Method, is based on a computerized automatic feedback system that adjusts light intensity to maintain a constant-response amplitude to a flickering stimulus throughout the spectrum, as it is scanned from 300 to 700 nm, and back. The results are presented as the reciprocal of the intensity at each wavelength required to maintain a constant peak to peak response amplitude. The resulting <i>S</i>(<i>λ</i>) had two peaks in all three rodent species, corresponding to ultraviolet and M cones, respectively: 359 nm and 511 nm for mice, 362 nm and 493 nm for gerbils, and 362 nm and 502 nm for rats. Results for mouse and gerbil were similar to literature reports of <i>S</i>(<i>λ</i>) functions obtained with other methods, confirming that the ERG associated to the AC Constant-Response Method was effective to obtain reliable <i>S</i>(<i>λ</i>) functions. In addition, due to its fast data collection time, the AC Constant Response Method has the advantage of keeping the eye in a constant light adapted state.</p></div

Ganglion cells and displaced amacrine cells density in the retina of the collared peccary (Pecari tajacu).

In the present study, we investigated the topographical distribution of ganglion cells and displaced amacrine cells in the retina of the collared peccary (Pecari tajacu), a diurnal neotropical mammal of the suborder Suina (Order Artiodactyla) widely distributed across central and mainly South America. Retinas were prepared and processed following the Nissl staining method. The number and distribution of retinal ganglion cells and displaced amacrine cells were determined in six flat-mounted retinas from three animals. The average density of ganglion cells was 351.822 ± 31.434 GC/mm2. The peccary shows a well-developed visual streak. The average peak density was 6,767 GC/mm2 and located within the visual range and displaced temporally as an area temporalis. Displaced amacrine cells have an average density of 300 DAC/mm2, but the density was not homogeneous along the retina, closer to the center of the retina the number of cells decreases and when approaching the periphery the density increases, in addition, amacrine cells do not form retinal specialization like ganglion cells. Outside the area temporalis, amacrine cells reach up to 80% in the ganglion cell layer. However, in the region of the area temporalis, the proportion of amacrine cells drops to 32%. Thus, three retinal specializations were found in peccary's retina by ganglion cells: visual streak, area temporalis and dorsotemporal extension. The topography of the ganglion cells layer in the retina of the peccary resembles other species of Order Artiodactyla already described and is directly related to its evolutionary history and ecology of the species

Mean spectral sensitivity curves for different species measured with the AC Constant-Response Method.

<p><b>(A)</b> Mean <i>S</i>(<i>λ</i>) curves for mice with <i>λ</i>_max at 359 and 511 nm (n = 3). <b>(B)</b> Mean spectral sensitivity curves for rats with <i>λ</i>_max at 362 and 502 nm (n = 3). <b>(C)</b> Mean spectral sensitivity curves for gerbil with <i>λ</i>_max at 362 and 493 nm (n = 3). <b>(D)</b> Best fittings obtained for each species. For each species, spectral sensitivity curves directly obtained from FFT fits and underlying Gaussian curves representing individual UV and M cone spectral sensitivities are shown.</p

Sensitivity curves determined by the residual method.

<p><b>(A)</b> Data obtained by using the AC Constant-Response Method for the gerbil. <b>(B)</b> Spectral sensitivity curve obtained using a Fast Fourier Transform (FFT) filter fitted to data points showed in (A). <b>(C)</b> Two peaks were found by fitting Gaussian normal curves to the FFT results. For the gerbil, the two peaks were located at 362 nm and 493 nm. <b>(D)</b> The same as in (B) showed in log scale.</p

Residual analysis for the spectral sensitivity curves of different species.

<p><b>(A)</b> and <b>(B)</b> Results for mice and rats, respectively, where large differences relative to the adjustment curve were observed. <b>(C)</b> Results for gerbils where the differences were small.</p

Spectral series and spectral sensitivity obtained by using the AC Constant-Response Method.

<p><b>(A)</b> ERG responses obtained from a light-adapted mouse. ERG responses were driven by flashes of monochromatic equal quanta lights of different wavelength. Note responses to wavelengths in the UV and green ranges. <b>(B)</b> Mean spectral sensitivity for mice. Filled circles and bars represent means and standard deviations for n = 3 animals. <b>(C)</b> and <b>(D)</b> are the mean spectral sensitivities obtained for rats (n = 3) and gerbils (n = 3), respectively. Spectral sensitivity curves for mice and rats were obtained at 4 nm intervals while curves for gerbils were obtained at 12 nm intervals.</p