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Steven P Brady - One of the best experts on this subject based on the ideXlab platform.
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understanding Maladaptation by uniting ecological and evolutionary perspectives
The American Naturalist, 2019Co-Authors: Steven P Brady, Daniel I Bolnick, Rowan D H Barrett, Erika Crispo, Alison M Derry, Christopher G Eckert, Dylan J Fraser, Lauren J Chapman, Gregor F Fussmann, Andrew GonzalezAbstract:Evolutionary biologists have long trained their sights on adaptation, focusing on the power of natural selection to produce relative fitness advantages while often ignoring changes in absolute fitness. Ecologists generally have taken a different tack, focusing on changes in abundance and ranges that reflect absolute fitness while often ignoring relative fitness. Uniting these perspectives, we articulate various causes of relative and absolute Maladaptation and review numerous examples of their occurrence. This review indicates that Maladaptation is reasonably common from both perspectives, yet often in contrasting ways. That is, Maladaptation can appear strong from a relative fitness perspective, yet populations can be growing in abundance. Conversely, resident individuals can appear locally adapted (relative to nonresident individuals) yet be declining in abundance. Understanding and interpreting these disconnects between relative and absolute Maladaptation, as well as the cases of agreement, is increasingly critical in the face of accelerating human-mediated environmental change. We therefore present a framework for studying Maladaptation, focusing in particular on the relationship between absolute and relative fitness, thereby drawing together evolutionary and ecological perspectives. The unification of these ecological and evolutionary perspectives has the potential to bring together previously disjunct research areas while addressing key conceptual issues and specific practical problems.
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causes of Maladaptation
Evolutionary Applications, 2019Co-Authors: Steven P Brady, Daniel I Bolnick, Amy L Angert, Andrew Gonzalez, Rowan D H Barrett, Erika Crispo, Alison M Derry, Christopher G Eckert, Dylan J FraserAbstract:Evolutionary biologists tend to approach the study of the natural world within a framework of adaptation, inspired perhaps by the power of natural selection to produce fitness advantages that drive population persistence and biological diversity. In contrast, evolution has rarely been studied through the lens of adaptation's complement, Maladaptation. This contrast is surprising because Maladaptation is a prevalent feature of evolution: population trait values are rarely distributed optimally; local populations often have lower fitness than imported ones; populations decline; and local and global extinctions are common. Yet we lack a general framework for understanding Maladaptation; for instance in terms of distribution, severity, and dynamics. Similar uncertainties apply to the causes of Maladaptation. We suggest that incorporating Maladaptation-based perspectives into evolutionary biology would facilitate better understanding of the natural world. Approaches within a Maladaptation framework might be especially profitable in applied evolution contexts - where reductions in fitness are common. Toward advancing a more balanced study of evolution, here we present a conceptual framework describing causes of Maladaptation. As the introductory article for a Special Feature on Maladaptation, we also summarize the studies in this Issue, highlighting the causes of Maladaptation in each study. We hope that our framework and the papers in this Special Issue will help catalyze the study of Maladaptation in applied evolution, supporting greater understanding of evolutionary dynamics in our rapidly changing world.
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positive sire effects and adaptive genotype by environment interaction occur despite pattern of local Maladaptation in roadside populations of an amphibian
Copeia, 2017Co-Authors: Steven P Brady, Debora GoedertAbstract:The global road network causes many negative ecological effects. Contrasting our knowledge of these effects, insights into evolutionary consequences of roads remain undeveloped. Here, we study a suite of populations of the Wood Frog that appear to be evolving maladaptively in response to road-adjacency. Specifically, when raised together in roadside pools, roadside populations survive at lower rates compared to populations away from roads. To begin to understand the cause of this survival disadvantage, we investigated potential parental and genetic sources of Maladaptation. First, to assess whether parental effects might induce Maladaptation, we measured adult body weight to length ratio (‘relative weight') and its influence on offspring survival in a reciprocal transplant experiment across 12 populations. Next, to assess whether genetic effects might limit adaptive responses in offspring, we estimated genetic correlations between environments for survival and fitness-related traits. We found that relativ...
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environmental exposure does not explain putative Maladaptation in road adjacent populations
Oecologia, 2017Co-Authors: Steven P BradyAbstract:While the ecological consequences of roads are well described, little is known of their role as agents of natural selection, which can shape adaptive and maladaptive responses in populations influenced by roads. This knowledge gap persists despite a growing appreciation for the influence of evolution in human-altered environments. There, insights indicate that natural selection typically results in local adaptation. Thus, populations influenced by road-induced selection should evolve fitness advantages in their local environment. Contrary to this expectation, wood frog tadpoles from roadside populations show evidence of a fitness disadvantage, consistent with local Maladaptation. Specifically, in reciprocal transplants, roadside populations survive at lower rates compared to populations away from roads. A key question remaining is whether roadside environmental conditions experienced by early stage embryos induce this outcome. This represents an important missing piece in evaluating the evolutionary nature of this Maladaptation pattern. Here, I address this gap using a reciprocal transplant experiment designed to test the hypothesis that embryonic exposure to roadside pond water induces a survival disadvantage. Contrary to this hypothesis, my results show that reduced survival persists when embryonic exposure is controlled. This outcome indicates that the survival disadvantage is parentally mediated, either genetically and/or through inherited environmental effects. This result suggests that roadside populations are either truly maladapted or potentially locally adapted at later life stages. I discuss these interpretations, noting that regardless of mechanism, patterns consistent with Maladaptation have important implications for conservation. In light of the pervasiveness of roads, further resolution explaining maladaptive responses remains a critical challenge in conservation.
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environmental exposure does not explain putative Maladaptation in road adjacent populations
bioRxiv, 2016Co-Authors: Steven P BradyAbstract:While the ecological consequences of roads are well described, little is known of their role as agents of natural selection, which can shape adaptive and maladaptive responses in populations influenced by roads. This is despite a growing appreciation for the influence of evolution in human-altered environments. There, insights indicate that natural selection typically results in local adaptation. Thus populations influenced by road-induced selection should evolve fitness advantages in their local environment. Contrary to this expectation, wood frog tadpoles from roadside populations show evidence of a fitness disadvantage, consistent with local Maladaptation. Specifically, in reciprocal transplants, roadside populations survive at lower rates compared to populations away from roads. A key question remaining is whether roadside environmental conditions experienced by early-stage embryos induce this outcome. This represents an important missing piece in evaluating the evolutionary nature of this Maladaptation pattern. Here, I address this gap using a reciprocal transplant experiment designed to test the hypothesis that embryonic exposure to roadside pond water induces a survival disadvantage. Contrary to this hypothesis, my results show that reduced survival persists when embryonic exposure is controlled. This indicates that the survival disadvantage is parentally mediated, either genetically and/or through inherited environmental effects. This result suggests that roadside populations are either truly maladapted or potentially locally adapted at later life stages. I discuss these interpretations, noting that regardless of mechanism, patterns consistent with Maladaptation have important implications for conservation. In light of the pervasiveness of roads, further resolution explaining maladaptive responses remains a critical challenge in conservation.
B Thilaganathan - One of the best experts on this subject based on the ideXlab platform.
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cardiac Maladaptation in obese pregnant women at term
Ultrasound in Obstetrics & Gynecology, 2019Co-Authors: B S Buddeberg, Rajan Sharma, Jamie M Odriscoll, Kaelin A Agten, Asma Khalil, B ThilaganathanAbstract:OBJECTIVE Obesity is an increasing problem worldwide, with well recognized detrimental effects on cardiovascular health; however, very little is known about the effect of obesity on cardiovascular adaptation to pregnancy. The aim of the present study was to compare biventricular cardiac function at term between obese pregnant women and pregnant women with normal body weight, utilizing conventional echocardiography and speckle-tracking assessment. METHODS This was a prospective case-control study of 40 obese, but otherwise healthy, pregnant women with a body mass index (BMI) of ≥ 35 kg/m2 and 40 healthy pregnant women with a BMI of ≤ 30 kg/m2 . All women underwent a comprehensive echocardiographic examination and speckle-tracking assessment at term. RESULTS Obese pregnant women, compared with controls, had significantly higher systolic blood pressure (117 vs 109 mmHg; P = 0.002), cardiac output (6.73 vs 4.90 L/min; P < 0.001), left ventricular (LV) mass index (74 vs 64 g/m2 ; P < 0.001) and relative wall thickness (0.43 vs 0.37; P < 0.001). Diastolic dysfunction was present in five (12.5%) controls and 16 (40%) obese women (P = 0.004). In obese women, compared with controls, LV global longitudinal strain (-15.59 vs -17.61%; P < 0.001), LV endocardial (-17.30 vs -19.84%; P < 0.001) and epicardial (-13.10 vs -15.73%; P < 0.001) global longitudinal strain as well as LV early diastolic strain rate (1.05 vs 1.24 /s; P = 0.006) were all significantly reduced. No differences were observed in the degree of LV twist and torsion between the two groups. CONCLUSIONS Morbidly obese, but otherwise healthy, pregnant women at term had significant LV hypertrophy with evidence of diastolic dysfunction and impaired deformation indices compared with pregnant women of normal weight. These findings are likely to represent a maladaptive response of the heart to volume overload in obese pregnancy. The impact of theses changes on pregnancy outcome and long-term maternal outcome is unclear. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
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cardiac Maladaptation in term pregnancies with preeclampsia
Pregnancy Hypertension, 2018Co-Authors: B S Buddeberg, Rajan Sharma, Jamie M Odriscoll, Kaelin A Agten, Asma Khalil, B ThilaganathanAbstract:Abstract Objectives To study biventricular cardiac changes with conventional echocardiography and new echocardiographic speckle tracking technologies such strain, twist and torsion in pregnant women with preeclampsia at term and normotensive control term pregnant women. Study design For this prospective single centre case-control study, we consecutively recruited 30 women with preeclampsia at term as cases and 40 healthy control term pregnant women. All women underwent transthoracic echocardiographic examination at the time point of inclusion into the study. Main outcome measures Signs of systolic and/or diastolic cardiac Maladaptation to the increased volume load associated with pregnancy. Results Conventional echocardiography revealed mild left sided diastolic impairment in the form of significantly increased E/E' in preeclampsia (7.58 ± 1.72 vs. 6.18 ± 1.57, p = 0.001) compared to normotensive controls, but no evidence of systolic impairment. With speckle tracking analysis, significant decreases in left ventricular global (−13.32 ± 2.37% vs. −17.61 ± 1.89%, p Conclusions The findings of this study demonstrate that pregnant women with term preeclampsia with minimal functional changes on conventional echocardiography, demonstrated significant subclinical myocardial changes on speckle tracking analysis.
Matthew C. Fitzpatrick - One of the best experts on this subject based on the ideXlab platform.
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experimental support for genomic prediction of climate Maladaptation using the machine learning approach gradient forests
Molecular Ecology Resources, 2021Co-Authors: Matthew C. Fitzpatrick, Vikram E Chhatre, Raju Y Soolanayakanahally, Stephen R. KellerAbstract:Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we (i) provide the first experimental evaluation of the ability of "genomic offsets" - a metric of climate Maladaptation derived from Gradient Forests - to predict organismal responses to environmental change, and (ii) explore the use of GF for identifying candidate SNPs. We used high-throughput sequencing, genome scans, and several methods, including GF, to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar's range also were planted in two common garden experiments. We used GF to relate candidate loci to environmental gradients and predict the expected magnitude of the response (i.e., the genetic offset metric of Maladaptation) of populations when transplanted from their "home" environment to the common gardens. We then compared the predicted genetic offsets from different sets of candidate and randomly selected SNPs to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance than did "naive" climate transfer distances. However, sets of randomly selected SNPs predicted performance slightly better than did candidate SNPs. Our study provides evidence that genetic offsets represent a first order estimate of the degree of expected Maladaptation of populations exposed to rapid environmental change and suggests GF may have some promise as a method for identifying candidate SNPs.
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Maladaptation, migration and extirpation fuel climate change risk in a forest tree species
Nature Climate Change, 2021Co-Authors: Andrew V. Gougherty, Stephen R. Keller, Matthew C. FitzpatrickAbstract:Accounting for population-level adaptation and migration remains a central challenge to predicting climate change effects on biodiversity. Assessing how climate change could disrupt local climate adaptation, resulting in Maladaptation and possibly extirpation, can inform where climate change poses the greatest risks across species ranges. For the forest tree species balsam poplar ( Populus balsamifera ), we used climate-associated genetic loci to predict population Maladaptation with and without migration, the distance to sites that minimize Maladaptation, and the emergence of novel genotype–climate associations. We show that the greatest disruptions to contemporary genotype–climate associations occur along the longitudinal edges of the range, where populations are predicted to be maladapted to all future North American climates, rescue via migration is most limited and novel genotype–climate associations emerge. Our work advances beyond species-level range modelling towards the long-held goal of simultaneously estimating the contributions of Maladaptation and migration to understanding the risks that populations may face from shifting climates. The authors use a subset of climate-associated genetic loci to predict future climate Maladaptation for balsam poplar ( Populus balsamifera ) populations while also considering migration potential. They predict the greatest disruptions along the longitudinal edge of the species range.
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experimental support for genomic prediction of climate Maladaptation using the machine learning approach gradient forests
Authorea Preprints, 2020Co-Authors: Matthew C. Fitzpatrick, Vikram E Chhatre, Raju Y Soolanayakanahally, Stephen R. KellerAbstract:Gradient Forests is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we provide the first experimental evaluation of the ability of ‘genomic offsets’ - a metric of climate Maladaptation derived from Gradient Forests - to predict organismal responses to environmental change. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar’s range also were planted in two common garden experiments. We used Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their “home” environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did ‘naive’ climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected Maladaptation of populations exposed to rapid environmental change.
Stephen R. Keller - One of the best experts on this subject based on the ideXlab platform.
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experimental support for genomic prediction of climate Maladaptation using the machine learning approach gradient forests
Molecular Ecology Resources, 2021Co-Authors: Matthew C. Fitzpatrick, Vikram E Chhatre, Raju Y Soolanayakanahally, Stephen R. KellerAbstract:Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we (i) provide the first experimental evaluation of the ability of "genomic offsets" - a metric of climate Maladaptation derived from Gradient Forests - to predict organismal responses to environmental change, and (ii) explore the use of GF for identifying candidate SNPs. We used high-throughput sequencing, genome scans, and several methods, including GF, to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar's range also were planted in two common garden experiments. We used GF to relate candidate loci to environmental gradients and predict the expected magnitude of the response (i.e., the genetic offset metric of Maladaptation) of populations when transplanted from their "home" environment to the common gardens. We then compared the predicted genetic offsets from different sets of candidate and randomly selected SNPs to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance than did "naive" climate transfer distances. However, sets of randomly selected SNPs predicted performance slightly better than did candidate SNPs. Our study provides evidence that genetic offsets represent a first order estimate of the degree of expected Maladaptation of populations exposed to rapid environmental change and suggests GF may have some promise as a method for identifying candidate SNPs.
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Maladaptation, migration and extirpation fuel climate change risk in a forest tree species
Nature Climate Change, 2021Co-Authors: Andrew V. Gougherty, Stephen R. Keller, Matthew C. FitzpatrickAbstract:Accounting for population-level adaptation and migration remains a central challenge to predicting climate change effects on biodiversity. Assessing how climate change could disrupt local climate adaptation, resulting in Maladaptation and possibly extirpation, can inform where climate change poses the greatest risks across species ranges. For the forest tree species balsam poplar ( Populus balsamifera ), we used climate-associated genetic loci to predict population Maladaptation with and without migration, the distance to sites that minimize Maladaptation, and the emergence of novel genotype–climate associations. We show that the greatest disruptions to contemporary genotype–climate associations occur along the longitudinal edges of the range, where populations are predicted to be maladapted to all future North American climates, rescue via migration is most limited and novel genotype–climate associations emerge. Our work advances beyond species-level range modelling towards the long-held goal of simultaneously estimating the contributions of Maladaptation and migration to understanding the risks that populations may face from shifting climates. The authors use a subset of climate-associated genetic loci to predict future climate Maladaptation for balsam poplar ( Populus balsamifera ) populations while also considering migration potential. They predict the greatest disruptions along the longitudinal edge of the species range.
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experimental support for genomic prediction of climate Maladaptation using the machine learning approach gradient forests
Authorea Preprints, 2020Co-Authors: Matthew C. Fitzpatrick, Vikram E Chhatre, Raju Y Soolanayakanahally, Stephen R. KellerAbstract:Gradient Forests is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we provide the first experimental evaluation of the ability of ‘genomic offsets’ - a metric of climate Maladaptation derived from Gradient Forests - to predict organismal responses to environmental change. We used high-throughput sequencing, genome scans, and several methods (including Gradient Forests) to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar’s range also were planted in two common garden experiments. We used Gradient Forests to relate candidate loci to environmental gradients and to predict the expected magnitude of response (i.e., the genetic offset) of populations when transplanted from their “home” environment to the new environments in the common gardens. We then compared the predicted genetic offsets to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance in the common gardens: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance in the common gardens than did ‘naive’ climate distances. Our results provide preliminary evidence that genomic offsets may provide a first order estimate of the degree of expected Maladaptation of populations exposed to rapid environmental change.
Alison L Marsden - One of the best experts on this subject based on the ideXlab platform.
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gradual loading ameliorates Maladaptation in computational simulations of vein graft growth and remodelling
Journal of the Royal Society Interface, 2017Co-Authors: Abhay B Ramachandra, Jay D Humphrey, Alison L MarsdenAbstract:Vein graft failure is a prevalent problem in vascular surgeries, including bypass grafting and arteriovenous fistula procedures in which veins are subjected to severe changes in pressure and flow. Animal and clinical studies provide significant insight, but understanding the complex underlying coupled mechanisms can be advanced using computational models. Towards this end, we propose a new model of venous growth and remodelling (G&R) based on a constrained mixture theory. First, we identify constitutive relations and parameters that enable venous adaptations to moderate perturbations in haemodynamics. We then fix these relations and parameters, and subject the vein to a range of combined loads (pressure and flow), from moderate to severe, and identify plausible mechanisms of adaptation versus Maladaptation. We also explore the beneficial effects of gradual increases in load on adaptation. A gradual change in flow over 3 days plus an initial step change in pressure results in fewer Maladaptations compared with step changes in both flow and pressure, or even a gradual change in pressure and flow over 3 days. A gradual change in flow and pressure over 8 days also enabled a successful venous adaptation for loads as severe as the arterial loads. Optimization is used to accelerate parameter estimation and the proposed framework is general enough to provide a good starting point for parameter estimations in G&R simulations.
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computational simulation of the adaptive capacity of vein grafts in response to increased pressure
Journal of Biomechanical Engineering-transactions of The Asme, 2015Co-Authors: Abhay B Ramachandra, Jay D Humphrey, Sethuraman Sankaran, Alison L MarsdenAbstract:Vein Maladaptation, leading to poor long-term patency, is a serious clinical problem in patients receiving coronary artery bypass grafts (CABGs) or undergoing related clinical procedures that subject veins to elevated blood flow and pressure. We propose a computational model of venous adaptation to altered pressure based on a constrained mixture theory of growth and remodeling (G&R). We identify constitutive parameters that optimally match biaxial data from a mouse vena cava, then numerically subject the vein to altered pressure conditions and quantify the extent of adaptation for a biologically reasonable set of bounds for G&R parameters. We identify conditions under which a vein graft can adapt optimally and explore physiological constraints that lead to Maladaptation. Finally, we test the hypothesis that a gradual, rather than a step, change in pressure will reduce Maladaptation. Optimization is used to accelerate parameter identification and numerically evaluate hypotheses of vein remodeling.
