Creating software tools that address the needs of a wide range of decision-makers requires the inclusion of differing perspectives throughout the development process. Software tools for biodiversity conservation often fall short in this regard, partly because broad decision-maker needs may exceed the toolkits of single research groups or even institutions. We show that participatory, collaborative codesign enhances the utility of software tools for better decision-making in biodiversity conservation planning, as demonstrated by our experiences developing a set of integrated tools in Colombia. Specifically, we undertook an interdisciplinary, multi-institutional collaboration of ecological modelers, software engineers, and a diverse profile of potential end users, including decision-makers, conservation practitioners, and biodiversity experts. We leveraged and modified common paradigms of software production, including codesign and agile development, to facilitate collaboration through all stages (including conceptualization, development, testing, and feedback) to ensure the accessibility and applicability of the new tools to inform decision-making for biodiversity conservation planning.

The Indian Ocean was designated as a whale sanctuary in 1979. While cetacean research has been conducted throughout the sanctuary, few studies have been conducted to assess the diversity, distribution and abundance of cetaceans inhabiting the waters surrounding the islands in the northern Mozambique Channel. In order to contribute to management and conservation efforts in this area, a series of small boatbased surveys were undertaken around the island of Mayotte from July 2004 to August 2005 to assess the diversity of cetaceans in the lagoon and surrounding waters, i.e. external barrier reef slope, insular slope (200-1,000m) and oceanic (>1,000m) waters. During this period, more than 284 hours were spent at sea on-effort and 17 cetacean species were recorded around Mayotte (n=286 sightings). One mysticete (1 Balaenopterid) and sixteen odontocetes (1 Kogid, 1 Physeterid, 13 Delphinids and 2 Ziphiids) were observed: spinner dolphin, n=118; pantropical spotted dolphin, n=61; Indo-Pacific bottlenose dolphin, n=44; humpback whale, n=37; melon-headed whale, n=5; Blainville’s beaked whale, n=4; Indo-Pacific humpback dolphin, n=4; common bottlenose dolphin, n=2; Risso’s dolphin, n=2; false killer whale, n=2; dwarf sperm whale, n=2; sperm whale, n=1; pygmy killer whale, n=1; short-finned pilot whale, n=1; Fraser’s dolphin, n=1; and Longman’s beaked whale, n=1. In addition to these 17 species recorded during dedicated surveys, two other cetacean species were observed opportunistically and subsequently identified as the Ginkgo-toothed beaked whale and the blue whale. The relatively large diversity of cetaceans around Mayotte is attributed to the wide range of marine habitats, such as coastal, reef-associated and oceanic, within close proximity to one another.

There have been few recent estimates of abundance for humpback whales (Megaptera novaeangliae) in the eastern South Atlantic Ocean. The first distance sampling survey of the coastal waters of Gabon was conducted in 2002. The difficult logistics of covering a large survey region with limited time, effort and refuelling opportunities required a line transect survey design that carefully balanced the theoretical demands of distance sampling with these constraints. Inshore/offshore zigzag transects were conducted to a distance of up to approximately 50 n.miles from the coast of Gabon corresponding to the 1,000m depth contour, from the border with Equatorial Guinea to a point south of Mayumba, near the Congo border representing 1,488 n.miles of survey effort. Seventy-nine different groups of humpback whales were observed throughout the survey area comprising a northern (Equatorial Guinea to Cap Lopez) and southern (Cap Lopez to Gamba) survey stratum. Relatively large numbers of whales were encountered throughout the southern stratum; encounter rates and densities were considerably lower in the northern stratum. The initial abundance estimate from a distance sampling analysis suggests that more than 1,200 humpback whales were present in Gabon’s coastal waters during the survey period. This estimate does not account for either availability or perception bias. In addition, this instantaneous snapshot of the number of whales occupying Gabon’s coastal waters is likely to correspond to only a portion of the population that uses these waters over time. However, the abundance estimate derived from the aerial survey are consistent with those based on photographic and genetic capture-recapture techniques. A continuing research programme in this area will help refine estimates of humpback whale abundance and using genetic and photographic data also establish the relationships between this and other populations. This is important given the potential overlap of humpback whales in large numbers throughout this region and the current extent and continued expansion of hydrocarbon exploration and extraction activities throughout the Gulf of Guinea.

The use of unmanned aerial vehicles (UAVs) to map and monitor the environment has increased sharply in the last few years. Many individuals and organizations have purchased consumer-grade UAVs, and commonly acquire aerial photographs to map land cover. The resulting ultra-high-resolution (sub-decimeter-resolution) imagery has high information content, but automating the extraction of this information to create accurate, wall-to-wall land-cover maps is quite difficult. We introduce image-processing workflows that are based on open-source software and can be used to create land-cover maps from ultra-high-resolution aerial imagery. We compared four machine-learning workflows for classifying images. Two workflows were based on random forest algorithms. Of these, one used a pixel-by-pixel approach available in ilastik, and the other used image segments and was implemented with R and the Orfeo ToolBox. The other two workflows used fully connected neural networks and convolutional neural networks implemented with Nenetic. We applied the four workflows to aerial photographs acquired in the Great Basin (western USA) at flying heights of 10 m, 45 m and 90 m above ground level. Our focal cover type was cheatgrass (Bromus tectorum), a non-native invasive grass that changes regional fire dynamics. The most accurate workflow for classifying ultra-high-resolution imagery depends on diverse factors that are influenced by image resolution and land-cover characteristics, such as contrast, landscape patterns and the spectral texture of the land-cover types being classified. For our application, the ilastik workflow yielded the highest overall accuracy (0.82-0.89) as assessed by pixel-based accuracy.

The Comoros Archipelago is an assemblage of oceanic islands, banks and offshore reef systems that longitudinally span the northern MozambiqueChannel. The greater Comoros Archipelago has been designated by the IWC as Wintering sub-Region C2 for humpback whales and is currentlyconsidered data deficient. Since 1997, annual marine mammal surveys of varying length and objective have been carried out in the waters surroundingMayotte, the eastern most island in the Comoros Archipelago. The humpback whales component of these surveys focused effort in and around thelagoon surrounding Mayotte. While it is expected that humpback whales can found throughout Comoros Archipelago it still remains unknown asto what degree humpback whales utilise specific banks and offshore reef systems within this area. Surveys conducted in 2002 and 2003 includedpassing mode and closing mode components intended to examine the density, group composition and encounter rates of humpback whales in anoffshore reef complex and a bank adjacent to the lagoon surround Mayotte. The densities of humpback whales, out to one nautical mile from thesurveyed transects, ranged from 0.027 to 0.618 whales/n.mile2 across three study sites. Females with calves were the most frequently encounteredgroup type. Encounter rates ranged from 0.98 to 2.36 groups per hour of search effort. These results, while exploratory in nature, indicate that theeastern region of the Comoros may be an important area for humpback whales during the late austral winter months and that additional, moreintensive systematic research is warranted.

Elucidating patterns of population structure for species with complex life histories, and disentangling the processes driving such patterns, remains a significant analytical challenge. Humpback whale ( Megaptera novaeangliae ) populations display complex genetic structures that have not been fully resolved at all spatial scales. We generated a data set of nuclear markers for 3575 samples spanning the seven breeding stocks and substocks found in the South Atlantic and western and northern Indian Oceans. For the total sample, and males and females separately, we assessed genetic diversity, tested for genetic differentiation between putative populations and isolation by distance, estimated the number of genetic clusters without a priori population information and estimated rates of gene flow using maximum‐likelihood and Bayesian approaches. At the ocean basin scale, structure is governed by geographical distance ( IBD P < 0.05) and female fidelity to breeding areas, in line with current understanding of the drivers of broadscale population structure. Consistent with previous studies, the Arabian Sea breeding stock was highly genetically differentiated ( F ST 0.034–0.161; P < 0.01 for all comparisons). However, the breeding stock boundary between west South Africa and east Africa was more porous than expected based on genetic differentiation, cluster and geneflow analyses. Instances of male fidelity to breeding areas and relatively high rates of dispersal for females were also observed between the three substocks in the western Indian Ocean. The relationships between demographic units and current management boundaries may have ramifications for assessments of the status and continued protections of populations still in recovery from commercial whaling.

Climate change could be a game-changer for biodiversity conservation, potentially invalidating many established methods including those employed in vulnerability assessments. Now, a simulation study finds that extinction risk due to climate change can be predicted using measurable spatial and demographic variables. Interestingly, most of those variables identified as important are already used in species conservation assessment. There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate1. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change2,3,4,5 based on the expectation that established assessments such as the IUCN Red List6 need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened7,8,9, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

Theoretical and practical approaches associated with conservation biogeography, including ecological niche modeling, have been applied to the difficult task of determining how to incorporate climate change into conservation prioritization methodologies. Most studies have focused on identifying species that are most at risk from climate change, but here we asked, which areas within a species’ range does climate change threaten most? We explored methods for incorporating climate change within a range-wide conservation planning framework, using a case study of jaguars (Panthera onca). We used ecological niche models to estimate exposure to climate change across the range of the jaguar and incorporated these estimates into habitat quality scores for re-prioritization of high-priority areas for jaguar conservation. Methods such as these are needed to guide prioritization of geographically-specific actions for conservation across a species’ range.

Summary1. Methods used to predict shifts in species’ ranges because of climate change commonly involve species distribution (niche) modelling using climatic variables, future values of which are predicted for the next several decades by general circulation models. However, species’ distributions also depend on factors other than climate, such as land cover, land use and soil type. Changes in some of these factors, such as soil type, occur over geologic time and are thus imperceptible over the timescale of these types of projections. Other factors, such as land use and land cover, are expected to change over shorter timescales, but reliable projections are not available. Some important predictor variables, therefore, must be treated as unchanging, or static, whether because of the properties of the variable or out of necessity. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial and has been dealt with differently in studies to date. Alternative methods include using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether.2. Using a set of simulated species, we tested various methods for combining static variables with future climate scenarios. Our results showed that including static variables in the model with the dynamic variables performed better or no worse than either masking or excluding the static variables.3. The difference in predictive ability was most pronounced when there is an interaction between the static and dynamic variables.4. For variables such as land use, our results indicate that if such variables affect species distributions, including them in the model is better than excluding them, even though this may mean making the unrealistic assumption that the variable will not change in the future.5. These results demonstrate the importance of including static and dynamic non‐climate variables in addition to climate variables in species distribution models designed to predict future change in a species’ habitat or distribution as a result of climate change.

Although humpback whales are among the best-studied of the large whales, population boundaries in the Southern Hemisphere (SH) have remained largely untested. We assess population structure of SH humpback whales using 1,527 samples collected from whales at fourteen sampling sites within the Southwestern and Southeastern Atlantic, the Southwestern Indian Ocean, and Northern Indian Ocean (Breeding Stocks A, B, C and X, respectively). Evaluation of mtDNA population structure and migration rates was carried out under different statistical frameworks. Using all genetic evidence, the results suggest significant degrees of population structure between all ocean basins, with the Southwestern and Northern Indian Ocean most differentiated from each other. Effective migration rates were highest between the Southeastern Atlantic and the Southwestern Indian Ocean, followed by rates within the Southeastern Atlantic, and the lowest between the Southwestern and Northern Indian Ocean. At finer scales, very low gene flow was detected between the two neighbouring sub-regions in the Southeastern Atlantic, compared to high gene flow for whales within the Southwestern Indian Ocean. Our genetic results support the current management designations proposed by the International Whaling Commission of Breeding Stocks A, B, C, and X as four strongly structured populations. The population structure patterns found in this study are likely to have been influenced by a combination of long-term maternally directed fidelity of migratory destinations, along with other ecological and oceanographic features in the region.

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